diff --git a/.ipynb_checkpoints/train-efficientNet-checkpoint.ipynb b/.ipynb_checkpoints/train-efficientNet-checkpoint.ipynb
index 612e41f..d30e946 100644
--- a/.ipynb_checkpoints/train-efficientNet-checkpoint.ipynb
+++ b/.ipynb_checkpoints/train-efficientNet-checkpoint.ipynb
@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "['Sat Sep  5 07:43:37 2020       ',\n",
+       "['Sat Sep  5 09:16:35 2020       ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
        " '| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |',\n",
        " '|-------------------------------+----------------------+----------------------+',\n",
@@ -52,42 +52,35 @@
        " '| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |',\n",
        " '|===============================+======================+======================|',\n",
        " '|   0  Quadro RTX 4000     Off  | 00000000:1D:00.0 Off |                  N/A |',\n",
-       " '| 30%   27C    P8     8W / 125W |   4752MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   43C    P0    35W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
-       " '| 30%   27C    P8     5W / 125W |   4776MiB /  7982MiB |      0%      Default |',\n",
+       " '| 31%   42C    P0    36W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
-       " '| 30%   26C    P8     4W / 125W |   4776MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   40C    P0    34W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
-       " '| 30%   28C    P8     9W / 125W |   4752MiB /  7982MiB |      0%      Default |',\n",
+       " '| 29%   43C    P0    39W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
-       " '| 25%   27C    P8     8W / 250W |   4557MiB / 11178MiB |      0%      Default |',\n",
+       " '| 24%   37C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
-       " '| 20%   19C    P8     8W / 250W |   4581MiB / 11178MiB |      0%      Default |',\n",
+       " '| 19%   30C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
-       " '| 20%   26C    P8     8W / 250W |   4581MiB / 11178MiB |      0%      Default |',\n",
+       " '| 18%   35C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
-       " '| 20%   24C    P8     8W / 250W |   4557MiB / 11178MiB |      0%      Default |',\n",
+       " '| 17%   32C    P0    58W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '                                                                               ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
        " '| Processes:                                                       GPU Memory |',\n",
        " '|  GPU       PID   Type   Process name                             Usage      |',\n",
        " '|=============================================================================|',\n",
-       " '|    0    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4741MiB |',\n",
-       " '|    1    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4765MiB |',\n",
-       " '|    2    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4765MiB |',\n",
-       " '|    3    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4741MiB |',\n",
-       " '|    4    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4547MiB |',\n",
-       " '|    5    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4571MiB |',\n",
-       " '|    6    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4571MiB |',\n",
-       " '|    7    107289      C   ...er/anaconda3/envs/viplab-gpu/bin/python  4547MiB |',\n",
+       " '|  No running processes found                                                 |',\n",
        " '+-----------------------------------------------------------------------------+']"
       ]
      },
@@ -97,6 +90,7 @@
     }
    ],
    "source": [
+    "import os\n",
     "#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
     "#os.environ[\"AUTOGRAPH_VERBOSITY\"] = \"10\"\n",
     "#os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\n",
@@ -172,7 +166,14 @@
     "\n",
     "data_dir = \"../LandslideDataset/\"\n",
     "resized_dir = \"./img_data/resized/\"\n",
-    "test_dir = \"./img_data/test/\""
+    "test_dir = \"./img_data/test/\"\n",
+    "\n",
+    "import functools\n",
+    "top1_acc = functools.partial(tf.keras.metrics.top_k_categorical_accuracy, k=1)\n",
+    "top1_acc.__name__ = 'top1_acc'\n",
+    "\n",
+    "top5_acc = functools.partial(tf.keras.metrics.top_k_categorical_accuracy, k=5)\n",
+    "top5_acc.__name__ = 'top5_acc'"
    ]
   },
   {
@@ -322,13 +323,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "./img_data/resized/Negative/3842558_landsat_8_rgb.tif\n"
+      "./img_data/resized/Positive/3851114_sentinel_2_rgb.tif\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f4c43f1a3d0>"
+       "<matplotlib.image.AxesImage at 0x7face618c390>"
       ]
      },
      "execution_count": 4,
@@ -337,7 +338,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -467,14 +468,536 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "NameError",
-     "evalue": "name 'MobileNetV2' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-5c98bf8f9372>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mstrategy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mbase_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMobileNetV2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'imagenet'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargetx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargety\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;31m# base_model = EfficientNetB0(include_top=False, weights='imagenet', input_shape=(targetx, targety, 3))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'MobileNetV2' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
+      "Model: \"functional_1\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "rescaling (Rescaling)           (None, 224, 224, 3)  0           input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "normalization (Normalization)   (None, 224, 224, 3)  7           rescaling[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "stem_conv (Conv2D)              (None, 112, 112, 32) 864         stem_conv_pad[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "stem_bn (BatchNormalization)    (None, 112, 112, 32) 128         stem_conv[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "stem_activation (Activation)    (None, 112, 112, 32) 0           stem_bn[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_dwconv (DepthwiseConv2D (None, 112, 112, 32) 288         stem_activation[0][0]            \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_bn (BatchNormalization) (None, 112, 112, 32) 128         block1a_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_activation (Activation) (None, 112, 112, 32) 0           block1a_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_se_excite (Multiply)    (None, 112, 112, 32) 0           block1a_activation[0][0]         \n",
+      "                                                                 block1a_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_project_conv (Conv2D)   (None, 112, 112, 16) 512         block1a_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block1a_project_bn (BatchNormal (None, 112, 112, 16) 64          block1a_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_expand_conv (Conv2D)    (None, 112, 112, 96) 1536        block1a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_expand_bn (BatchNormali (None, 112, 112, 96) 384         block2a_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_expand_activation (Acti (None, 112, 112, 96) 0           block2a_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_dwconv_pad (ZeroPadding (None, 113, 113, 96) 0           block2a_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_dwconv (DepthwiseConv2D (None, 56, 56, 96)   864         block2a_dwconv_pad[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_bn (BatchNormalization) (None, 56, 56, 96)   384         block2a_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_activation (Activation) (None, 56, 56, 96)   0           block2a_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_se_excite (Multiply)    (None, 56, 56, 96)   0           block2a_activation[0][0]         \n",
+      "                                                                 block2a_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_project_conv (Conv2D)   (None, 56, 56, 24)   2304        block2a_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2a_project_bn (BatchNormal (None, 56, 56, 24)   96          block2a_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_expand_bn (BatchNormali (None, 56, 56, 144)  576         block2b_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_expand_activation (Acti (None, 56, 56, 144)  0           block2b_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_dwconv (DepthwiseConv2D (None, 56, 56, 144)  1296        block2b_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_bn (BatchNormalization) (None, 56, 56, 144)  576         block2b_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_activation (Activation) (None, 56, 56, 144)  0           block2b_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_se_excite (Multiply)    (None, 56, 56, 144)  0           block2b_activation[0][0]         \n",
+      "                                                                 block2b_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_project_conv (Conv2D)   (None, 56, 56, 24)   3456        block2b_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_project_bn (BatchNormal (None, 56, 56, 24)   96          block2b_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_drop (Dropout)          (None, 56, 56, 24)   0           block2b_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block2b_add (Add)               (None, 56, 56, 24)   0           block2b_drop[0][0]               \n",
+      "                                                                 block2a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_expand_bn (BatchNormali (None, 56, 56, 144)  576         block3a_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_expand_activation (Acti (None, 56, 56, 144)  0           block3a_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_dwconv_pad (ZeroPadding (None, 59, 59, 144)  0           block3a_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_dwconv (DepthwiseConv2D (None, 28, 28, 144)  3600        block3a_dwconv_pad[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_bn (BatchNormalization) (None, 28, 28, 144)  576         block3a_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_activation (Activation) (None, 28, 28, 144)  0           block3a_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_se_excite (Multiply)    (None, 28, 28, 144)  0           block3a_activation[0][0]         \n",
+      "                                                                 block3a_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_project_conv (Conv2D)   (None, 28, 28, 40)   5760        block3a_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3a_project_bn (BatchNormal (None, 28, 28, 40)   160         block3a_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_expand_bn (BatchNormali (None, 28, 28, 240)  960         block3b_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_expand_activation (Acti (None, 28, 28, 240)  0           block3b_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_dwconv (DepthwiseConv2D (None, 28, 28, 240)  6000        block3b_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_bn (BatchNormalization) (None, 28, 28, 240)  960         block3b_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_activation (Activation) (None, 28, 28, 240)  0           block3b_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_se_excite (Multiply)    (None, 28, 28, 240)  0           block3b_activation[0][0]         \n",
+      "                                                                 block3b_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_project_conv (Conv2D)   (None, 28, 28, 40)   9600        block3b_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_project_bn (BatchNormal (None, 28, 28, 40)   160         block3b_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_drop (Dropout)          (None, 28, 28, 40)   0           block3b_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block3b_add (Add)               (None, 28, 28, 40)   0           block3b_drop[0][0]               \n",
+      "                                                                 block3a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_expand_bn (BatchNormali (None, 28, 28, 240)  960         block4a_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_expand_activation (Acti (None, 28, 28, 240)  0           block4a_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_dwconv_pad (ZeroPadding (None, 29, 29, 240)  0           block4a_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_dwconv (DepthwiseConv2D (None, 14, 14, 240)  2160        block4a_dwconv_pad[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_bn (BatchNormalization) (None, 14, 14, 240)  960         block4a_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_activation (Activation) (None, 14, 14, 240)  0           block4a_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_se_excite (Multiply)    (None, 14, 14, 240)  0           block4a_activation[0][0]         \n",
+      "                                                                 block4a_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_project_conv (Conv2D)   (None, 14, 14, 80)   19200       block4a_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4a_project_bn (BatchNormal (None, 14, 14, 80)   320         block4a_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4b_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_expand_activation (Acti (None, 14, 14, 480)  0           block4b_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4b_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4b_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_activation (Activation) (None, 14, 14, 480)  0           block4b_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_se_excite (Multiply)    (None, 14, 14, 480)  0           block4b_activation[0][0]         \n",
+      "                                                                 block4b_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4b_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_project_bn (BatchNormal (None, 14, 14, 80)   320         block4b_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_drop (Dropout)          (None, 14, 14, 80)   0           block4b_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4b_add (Add)               (None, 14, 14, 80)   0           block4b_drop[0][0]               \n",
+      "                                                                 block4a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4c_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_expand_activation (Acti (None, 14, 14, 480)  0           block4c_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4c_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4c_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_activation (Activation) (None, 14, 14, 480)  0           block4c_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_se_excite (Multiply)    (None, 14, 14, 480)  0           block4c_activation[0][0]         \n",
+      "                                                                 block4c_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4c_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_project_bn (BatchNormal (None, 14, 14, 80)   320         block4c_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_drop (Dropout)          (None, 14, 14, 80)   0           block4c_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block4c_add (Add)               (None, 14, 14, 80)   0           block4c_drop[0][0]               \n",
+      "                                                                 block4b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4c_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block5a_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_expand_activation (Acti (None, 14, 14, 480)  0           block5a_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_dwconv (DepthwiseConv2D (None, 14, 14, 480)  12000       block5a_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_bn (BatchNormalization) (None, 14, 14, 480)  1920        block5a_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_activation (Activation) (None, 14, 14, 480)  0           block5a_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_se_excite (Multiply)    (None, 14, 14, 480)  0           block5a_activation[0][0]         \n",
+      "                                                                 block5a_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_project_conv (Conv2D)   (None, 14, 14, 112)  53760       block5a_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5a_project_bn (BatchNormal (None, 14, 14, 112)  448         block5a_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5b_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_expand_activation (Acti (None, 14, 14, 672)  0           block5b_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5b_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5b_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_activation (Activation) (None, 14, 14, 672)  0           block5b_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_se_excite (Multiply)    (None, 14, 14, 672)  0           block5b_activation[0][0]         \n",
+      "                                                                 block5b_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5b_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_project_bn (BatchNormal (None, 14, 14, 112)  448         block5b_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_drop (Dropout)          (None, 14, 14, 112)  0           block5b_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5b_add (Add)               (None, 14, 14, 112)  0           block5b_drop[0][0]               \n",
+      "                                                                 block5a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5c_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_expand_activation (Acti (None, 14, 14, 672)  0           block5c_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5c_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5c_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_activation (Activation) (None, 14, 14, 672)  0           block5c_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_se_excite (Multiply)    (None, 14, 14, 672)  0           block5c_activation[0][0]         \n",
+      "                                                                 block5c_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5c_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_project_bn (BatchNormal (None, 14, 14, 112)  448         block5c_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_drop (Dropout)          (None, 14, 14, 112)  0           block5c_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block5c_add (Add)               (None, 14, 14, 112)  0           block5c_drop[0][0]               \n",
+      "                                                                 block5b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5c_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block6a_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_expand_activation (Acti (None, 14, 14, 672)  0           block6a_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_dwconv_pad (ZeroPadding (None, 17, 17, 672)  0           block6a_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_dwconv (DepthwiseConv2D (None, 7, 7, 672)    16800       block6a_dwconv_pad[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_bn (BatchNormalization) (None, 7, 7, 672)    2688        block6a_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_activation (Activation) (None, 7, 7, 672)    0           block6a_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_se_excite (Multiply)    (None, 7, 7, 672)    0           block6a_activation[0][0]         \n",
+      "                                                                 block6a_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_project_conv (Conv2D)   (None, 7, 7, 192)    129024      block6a_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6a_project_bn (BatchNormal (None, 7, 7, 192)    768         block6a_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6b_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_expand_activation (Acti (None, 7, 7, 1152)   0           block6b_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6b_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6b_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_activation (Activation) (None, 7, 7, 1152)   0           block6b_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6b_activation[0][0]         \n",
+      "                                                                 block6b_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6b_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_project_bn (BatchNormal (None, 7, 7, 192)    768         block6b_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_drop (Dropout)          (None, 7, 7, 192)    0           block6b_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6b_add (Add)               (None, 7, 7, 192)    0           block6b_drop[0][0]               \n",
+      "                                                                 block6a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6c_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_expand_activation (Acti (None, 7, 7, 1152)   0           block6c_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6c_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6c_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_activation (Activation) (None, 7, 7, 1152)   0           block6c_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6c_activation[0][0]         \n",
+      "                                                                 block6c_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6c_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_project_bn (BatchNormal (None, 7, 7, 192)    768         block6c_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_drop (Dropout)          (None, 7, 7, 192)    0           block6c_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6c_add (Add)               (None, 7, 7, 192)    0           block6c_drop[0][0]               \n",
+      "                                                                 block6b_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6c_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6d_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_expand_activation (Acti (None, 7, 7, 1152)   0           block6d_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6d_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6d_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_activation (Activation) (None, 7, 7, 1152)   0           block6d_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6d_activation[0][0]         \n",
+      "                                                                 block6d_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6d_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_project_bn (BatchNormal (None, 7, 7, 192)    768         block6d_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_drop (Dropout)          (None, 7, 7, 192)    0           block6d_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block6d_add (Add)               (None, 7, 7, 192)    0           block6d_drop[0][0]               \n",
+      "                                                                 block6c_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6d_add[0][0]                \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block7a_expand_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_expand_activation (Acti (None, 7, 7, 1152)   0           block7a_expand_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   10368       block7a_expand_activation[0][0]  \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block7a_dwconv[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_activation (Activation) (None, 7, 7, 1152)   0           block7a_bn[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_se_excite (Multiply)    (None, 7, 7, 1152)   0           block7a_activation[0][0]         \n",
+      "                                                                 block7a_se_expand[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_project_conv (Conv2D)   (None, 7, 7, 320)    368640      block7a_se_excite[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "block7a_project_bn (BatchNormal (None, 7, 7, 320)    1280        block7a_project_conv[0][0]       \n",
+      "__________________________________________________________________________________________________\n",
+      "top_conv (Conv2D)               (None, 7, 7, 1280)   409600      block7a_project_bn[0][0]         \n",
+      "__________________________________________________________________________________________________\n",
+      "top_bn (BatchNormalization)     (None, 7, 7, 1280)   5120        top_conv[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "top_activation (Activation)     (None, 7, 7, 1280)   0           top_bn[0][0]                     \n",
+      "__________________________________________________________________________________________________\n",
+      "global_average_pooling2d (Globa (None, 1280)         0           top_activation[0][0]             \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization (BatchNorma (None, 1280)         5120        global_average_pooling2d[0][0]   \n",
+      "__________________________________________________________________________________________________\n",
+      "dense (Dense)                   (None, 1280)         1639680     batch_normalization[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "batch_normalization_1 (BatchNor (None, 1280)         5120        dense[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_1 (Dense)                 (None, 2)            2562        batch_normalization_1[0][0]      \n",
+      "==================================================================================================\n",
+      "Total params: 5,702,053\n",
+      "Trainable params: 5,654,910\n",
+      "Non-trainable params: 47,143\n",
+      "__________________________________________________________________________________________________\n"
      ]
     }
    ],
@@ -507,7 +1030,7 @@
     "\n",
     "    model.compile(optimizer=optimizer,\n",
     "                  loss=loss,\n",
-    "                  metrics=[\"accuracy\"])\n",
+    "                  metrics=[\"accuracy\", top1_acc, top5_acc])\n",
     "\n",
     "    model.summary()\n",
     "    # for i, layer in enumerate(model.layers):\n",
@@ -523,9 +1046,888 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <timed exec>:6: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use Model.fit, which supports generators.\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 1/250\n",
+      "WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.data.Iterator.get_next_as_optional()` instead.\n",
+      "INFO:tensorflow:batch_all_reduce: 219 all-reduces with algorithm = nccl, num_packs = 1\n",
+      "INFO:tensorflow:batch_all_reduce: 219 all-reduces with algorithm = nccl, num_packs = 1\n",
+      "1/7 [===>..........................] - ETA: 0s - loss: 0.8930 - accuracy: 0.4800 - top1_acc: 0.4800 - top5_acc: 1.0000WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
+      "Instructions for updating:\n",
+      "use `tf.profiler.experimental.stop` instead.\n",
+      "2/7 [=======>......................] - ETA: 2s - loss: 0.8422 - accuracy: 0.5167 - top1_acc: 0.5167 - top5_acc: 1.0000WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2884s vs `on_train_batch_end` time: 0.7248s). Check your callbacks.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.8544 - accuracy: 0.5210 - top1_acc: 0.5210 - top5_acc: 1.0000\n",
+      "Epoch 00001: val_accuracy improved from -inf to 0.54321, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 30s 4s/step - loss: 0.8544 - accuracy: 0.5210 - top1_acc: 0.5210 - top5_acc: 1.0000 - val_loss: 0.7015 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 2/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.7470 - accuracy: 0.6070 - top1_acc: 0.6070 - top5_acc: 1.0000\n",
+      "Epoch 00002: val_accuracy did not improve from 0.54321\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.7470 - accuracy: 0.6070 - top1_acc: 0.6070 - top5_acc: 1.0000 - val_loss: 0.6948 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 3/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.7023 - accuracy: 0.6213 - top1_acc: 0.6213 - top5_acc: 1.0000\n",
+      "Epoch 00003: val_accuracy improved from 0.54321 to 0.54733, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.7023 - accuracy: 0.6213 - top1_acc: 0.6213 - top5_acc: 1.0000 - val_loss: 0.6978 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 4/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.6226 - accuracy: 0.6725 - top1_acc: 0.6725 - top5_acc: 1.0000\n",
+      "Epoch 00004: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.6226 - accuracy: 0.6725 - top1_acc: 0.6725 - top5_acc: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 5/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5617 - accuracy: 0.7124 - top1_acc: 0.7124 - top5_acc: 1.0000\n",
+      "Epoch 00005: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.5617 - accuracy: 0.7124 - top1_acc: 0.7124 - top5_acc: 1.0000 - val_loss: 0.7006 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 6/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5630 - accuracy: 0.7073 - top1_acc: 0.7073 - top5_acc: 1.0000\n",
+      "Epoch 00006: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5630 - accuracy: 0.7073 - top1_acc: 0.7073 - top5_acc: 1.0000 - val_loss: 0.6960 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 7/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5514 - accuracy: 0.7165 - top1_acc: 0.7165 - top5_acc: 1.0000\n",
+      "Epoch 00007: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5514 - accuracy: 0.7165 - top1_acc: 0.7165 - top5_acc: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
+      "Epoch 8/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5193 - accuracy: 0.7482 - top1_acc: 0.7482 - top5_acc: 1.0000\n",
+      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
+      "\n",
+      "Epoch 00008: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5193 - accuracy: 0.7482 - top1_acc: 0.7482 - top5_acc: 1.0000 - val_loss: 0.6879 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 9/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5036 - accuracy: 0.7441 - top1_acc: 0.7441 - top5_acc: 1.0000\n",
+      "Epoch 00009: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5036 - accuracy: 0.7441 - top1_acc: 0.7441 - top5_acc: 1.0000 - val_loss: 0.6943 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 10/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4857 - accuracy: 0.7605 - top1_acc: 0.7605 - top5_acc: 1.0000\n",
+      "Epoch 00010: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4857 - accuracy: 0.7605 - top1_acc: 0.7605 - top5_acc: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.5185 - val_top1_acc: 0.5185 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 11/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4699 - accuracy: 0.7748 - top1_acc: 0.7748 - top5_acc: 1.0000\n",
+      "Epoch 00011: val_accuracy improved from 0.54733 to 0.57613, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4699 - accuracy: 0.7748 - top1_acc: 0.7748 - top5_acc: 1.0000 - val_loss: 0.6857 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 12/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4876 - accuracy: 0.7646 - top1_acc: 0.7646 - top5_acc: 1.0000\n",
+      "Epoch 00012: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4876 - accuracy: 0.7646 - top1_acc: 0.7646 - top5_acc: 1.0000 - val_loss: 0.6970 - val_accuracy: 0.5226 - val_top1_acc: 0.5226 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 13/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4397 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000\n",
+      "Epoch 00013: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4397 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000 - val_loss: 0.7062 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 14/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4570 - accuracy: 0.7779 - top1_acc: 0.7779 - top5_acc: 1.0000\n",
+      "Epoch 00014: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4570 - accuracy: 0.7779 - top1_acc: 0.7779 - top5_acc: 1.0000 - val_loss: 0.7150 - val_accuracy: 0.5021 - val_top1_acc: 0.5021 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 15/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4587 - accuracy: 0.7820 - top1_acc: 0.7820 - top5_acc: 1.0000\n",
+      "Epoch 00015: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4587 - accuracy: 0.7820 - top1_acc: 0.7820 - top5_acc: 1.0000 - val_loss: 0.7155 - val_accuracy: 0.5226 - val_top1_acc: 0.5226 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-05\n",
+      "Epoch 16/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4452 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00016: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
+      "\n",
+      "Epoch 00016: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4452 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7078 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
+      "Epoch 17/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4542 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000\n",
+      "Epoch 00017: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4542 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000 - val_loss: 0.7360 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
+      "Epoch 18/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4282 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00018: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4282 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7230 - val_accuracy: 0.4938 - val_top1_acc: 0.4938 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
+      "Epoch 19/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00019: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4227 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.7254 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
+      "Epoch 20/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4014 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
+      "Epoch 00020: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4014 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.7454 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
+      "Epoch 21/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4401 - accuracy: 0.7892 - top1_acc: 0.7892 - top5_acc: 1.0000\n",
+      "Epoch 00021: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
+      "\n",
+      "Epoch 00021: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4401 - accuracy: 0.7892 - top1_acc: 0.7892 - top5_acc: 1.0000 - val_loss: 0.7532 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 22/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4413 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000\n",
+      "Epoch 00022: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4413 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000 - val_loss: 0.7500 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 23/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4212 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00023: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4212 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7354 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 24/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4280 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00024: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4280 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7625 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 25/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4185 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00025: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4185 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.7314 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 26/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4405 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000\n",
+      "Epoch 00026: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
+      "\n",
+      "Epoch 00026: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4405 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000 - val_loss: 0.7579 - val_accuracy: 0.5062 - val_top1_acc: 0.5062 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 27/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4327 - accuracy: 0.7922 - top1_acc: 0.7922 - top5_acc: 1.0000\n",
+      "Epoch 00027: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4327 - accuracy: 0.7922 - top1_acc: 0.7922 - top5_acc: 1.0000 - val_loss: 0.7568 - val_accuracy: 0.5185 - val_top1_acc: 0.5185 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 28/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4133 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00028: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4133 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.7752 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 29/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4155 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
+      "Epoch 00029: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4155 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.7434 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 30/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4042 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00030: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4042 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.7319 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 31/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4174 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
+      "\n",
+      "Epoch 00031: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4174 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7586 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 32/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3909 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000\n",
+      "Epoch 00032: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3909 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000 - val_loss: 0.7856 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 33/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4224 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00033: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4224 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.7467 - val_accuracy: 0.5391 - val_top1_acc: 0.5391 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 34/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4180 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00034: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4180 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.7897 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 35/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00035: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4165 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.7593 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 36/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4153 - accuracy: 0.7994 - top1_acc: 0.7994 - top5_acc: 1.0000\n",
+      "Epoch 00036: val_accuracy improved from 0.57613 to 0.58025, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4153 - accuracy: 0.7994 - top1_acc: 0.7994 - top5_acc: 1.0000 - val_loss: 0.7498 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 37/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4172 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00037: val_accuracy did not improve from 0.58025\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4172 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.7429 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 38/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4241 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
+      "Epoch 00038: val_accuracy improved from 0.58025 to 0.60494, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4241 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.7420 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 39/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4132 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
+      "Epoch 00039: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4132 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.7824 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 40/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4212 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
+      "Epoch 00040: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4212 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.7775 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 41/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4015 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00041: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4015 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.7802 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 42/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4230 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
+      "Epoch 00042: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4230 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.7686 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 43/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4070 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00043: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00043: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4070 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.7759 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 44/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4361 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00044: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4361 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7949 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 45/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3897 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00045: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3897 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8084 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 46/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8260 - top1_acc: 0.8260 - top5_acc: 1.0000\n",
+      "Epoch 00046: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8260 - top1_acc: 0.8260 - top5_acc: 1.0000 - val_loss: 0.7577 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 47/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4213 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00047: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4213 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7974 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 48/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4049 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00048: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00048: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4049 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.7643 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 49/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4162 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00049: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4162 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.7960 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 50/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4137 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00050: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4137 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8056 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 51/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4371 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00051: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4371 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7826 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 52/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4073 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00052: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4073 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7567 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 53/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4333 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00053: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00053: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4333 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.7879 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 54/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4300 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00054: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4300 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7981 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 55/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4194 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00055: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4194 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7958 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 56/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4072 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
+      "Epoch 00056: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4072 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.8397 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 57/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4341 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000\n",
+      "Epoch 00057: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4341 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000 - val_loss: 0.8151 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 58/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4187 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00058: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00058: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4187 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7572 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 59/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8352 - top1_acc: 0.8352 - top5_acc: 1.0000\n",
+      "Epoch 00059: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3963 - accuracy: 0.8352 - top1_acc: 0.8352 - top5_acc: 1.0000 - val_loss: 0.7702 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 60/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4447 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000\n",
+      "Epoch 00060: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4447 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000 - val_loss: 0.7811 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 61/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4151 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00061: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4151 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8052 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 62/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00062: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4145 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.7830 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 63/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4266 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000\n",
+      "Epoch 00063: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00063: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4266 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000 - val_loss: 0.7984 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 64/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4071 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
+      "Epoch 00064: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4071 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.8233 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 65/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3957 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
+      "Epoch 00065: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3957 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.8013 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 66/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4045 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000\n",
+      "Epoch 00066: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4045 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000 - val_loss: 0.8545 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 67/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4061 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
+      "Epoch 00067: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4061 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.8196 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 68/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4111 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00068: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00068: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4111 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7886 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 69/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4065 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
+      "Epoch 00069: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4065 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.8384 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 70/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4313 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00070: val_accuracy improved from 0.60494 to 0.61728, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4313 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7754 - val_accuracy: 0.6173 - val_top1_acc: 0.6173 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 71/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4128 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00071: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4128 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.7718 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 72/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4139 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00072: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4139 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8216 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 73/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4000 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
+      "Epoch 00073: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4000 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.8411 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 74/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4125 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00074: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4125 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.8011 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 75/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3860 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
+      "Epoch 00075: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00075: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3860 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.8244 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 76/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4269 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000\n",
+      "Epoch 00076: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4269 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000 - val_loss: 0.8367 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 77/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00077: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8050 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 78/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00078: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4055 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8203 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 79/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4268 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00079: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4268 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8736 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 80/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4295 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00080: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00080: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4295 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8296 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 81/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4161 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00081: val_accuracy improved from 0.61728 to 0.64198, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4161 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.7427 - val_accuracy: 0.6420 - val_top1_acc: 0.6420 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 82/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8291 - top1_acc: 0.8291 - top5_acc: 1.0000\n",
+      "Epoch 00082: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4032 - accuracy: 0.8291 - top1_acc: 0.8291 - top5_acc: 1.0000 - val_loss: 0.7947 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 83/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4326 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000\n",
+      "Epoch 00083: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4326 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 84/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4256 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000\n",
+      "Epoch 00084: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4256 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000 - val_loss: 0.8390 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 85/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4141 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00085: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4141 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8190 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 86/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4214 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00086: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00086: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4214 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8395 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 87/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4311 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000\n",
+      "Epoch 00087: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4311 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000 - val_loss: 0.8246 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 88/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4257 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
+      "Epoch 00088: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4257 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.8820 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 89/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00089: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3992 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8756 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 90/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4209 - accuracy: 0.8025 - top1_acc: 0.8025 - top5_acc: 1.0000\n",
+      "Epoch 00090: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4209 - accuracy: 0.8025 - top1_acc: 0.8025 - top5_acc: 1.0000 - val_loss: 0.8621 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 91/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4325 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00091: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00091: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4325 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7532 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 92/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4083 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00092: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4083 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7819 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 93/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4089 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
+      "Epoch 00093: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4089 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8260 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 94/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4044 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00094: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4044 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8332 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 95/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4271 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00095: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4271 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8011 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 96/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4049 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00096: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00096: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4049 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8113 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 97/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4142 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00097: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4142 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8392 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 98/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3947 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00098: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3947 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.8612 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 99/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4289 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
+      "Epoch 00099: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4289 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.8127 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 100/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3977 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00100: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3977 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8109 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 101/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4250 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00101: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00101: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4250 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7745 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 102/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4169 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00102: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4169 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7935 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 103/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4098 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000\n",
+      "Epoch 00103: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4098 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000 - val_loss: 0.8652 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 104/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4068 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
+      "Epoch 00104: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4068 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8326 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 105/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00105: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4032 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8283 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 106/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4017 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00106: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00106: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4017 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8378 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 107/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3853 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000\n",
+      "Epoch 00107: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3853 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000 - val_loss: 0.7966 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 108/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4340 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000\n",
+      "Epoch 00108: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4340 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000 - val_loss: 0.8355 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 109/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3952 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
+      "Epoch 00109: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3952 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.7637 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 110/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4181 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00110: val_accuracy improved from 0.64198 to 0.65432, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4181 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7650 - val_accuracy: 0.6543 - val_top1_acc: 0.6543 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 111/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4153 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00111: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4153 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8310 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 112/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00112: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3963 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8353 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 113/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4266 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00113: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4266 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8709 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 114/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3953 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00114: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3953 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8202 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 115/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4040 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00115: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00115: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4040 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8301 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 116/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3972 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00116: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3972 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8246 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 117/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4072 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00117: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4072 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8309 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 118/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4291 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000\n",
+      "Epoch 00118: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4291 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000 - val_loss: 0.8027 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 119/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
+      "Epoch 00119: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4145 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.7976 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 120/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4201 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00120: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00120: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4201 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8541 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 121/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3885 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000\n",
+      "Epoch 00121: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3885 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000 - val_loss: 0.8040 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 122/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4096 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00122: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4096 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8665 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 123/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00123: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8233 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 124/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4238 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
+      "Epoch 00124: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4238 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 125/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4124 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
+      "Epoch 00125: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00125: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4124 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.8165 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 126/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00126: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8247 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 127/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4161 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00127: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4161 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.8241 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 128/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.8342 - top1_acc: 0.8342 - top5_acc: 1.0000\n",
+      "Epoch 00128: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3992 - accuracy: 0.8342 - top1_acc: 0.8342 - top5_acc: 1.0000 - val_loss: 0.8063 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 129/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3994 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00129: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3994 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7940 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 130/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4076 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00130: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00130: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4076 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8176 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 131/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3999 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00131: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.3999 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8128 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 132/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4064 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00132: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4064 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.7827 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 133/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4264 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00133: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4264 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8162 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 134/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4045 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00134: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4045 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.8722 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 135/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00135: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00135: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4227 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.7998 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 136/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4397 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
+      "Epoch 00136: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4397 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.8207 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 137/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4199 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
+      "Epoch 00137: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4199 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.8432 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 138/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4279 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00138: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4279 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7595 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 139/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4121 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
+      "Epoch 00139: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4121 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.8093 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 140/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4046 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00140: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00140: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4046 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.8322 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 141/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00141: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7741 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 142/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4293 - accuracy: 0.8035 - top1_acc: 0.8035 - top5_acc: 1.0000\n",
+      "Epoch 00142: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4293 - accuracy: 0.8035 - top1_acc: 0.8035 - top5_acc: 1.0000 - val_loss: 0.8359 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 143/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4171 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00143: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4171 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8525 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 144/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4122 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00144: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4122 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8431 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 145/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00145: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00145: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4032 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8754 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 146/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3939 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000\n",
+      "Epoch 00146: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3939 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000 - val_loss: 0.7797 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 147/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4003 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00147: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4003 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8439 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 148/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4031 - accuracy: 0.8362 - top1_acc: 0.8362 - top5_acc: 1.0000\n",
+      "Epoch 00148: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4031 - accuracy: 0.8362 - top1_acc: 0.8362 - top5_acc: 1.0000 - val_loss: 0.8480 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 149/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3908 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
+      "Epoch 00149: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3908 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8352 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 150/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4011 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
+      "Epoch 00150: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00150: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4011 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.8256 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 151/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00151: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8496 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 152/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
+      "Epoch 00152: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3963 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8126 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 153/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4223 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000\n",
+      "Epoch 00153: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4223 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000 - val_loss: 0.8070 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 154/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00154: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.8222 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 155/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3918 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
+      "Epoch 00155: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00155: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3918 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8130 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 156/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4041 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00156: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4041 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8405 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 157/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4024 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00157: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4024 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8030 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 158/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4081 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
+      "Epoch 00158: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4081 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.7719 - val_accuracy: 0.6255 - val_top1_acc: 0.6255 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 159/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4029 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00159: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4029 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7738 - val_accuracy: 0.6255 - val_top1_acc: 0.6255 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 160/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3936 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00160: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "Restoring model weights from the end of the best epoch.\n",
+      "\n",
+      "Epoch 00160: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3936 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8669 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Epoch 00160: early stopping\n",
+      "CPU times: user 1h 14min 8s, sys: 6min 51s, total: 1h 20min 59s\n",
+      "Wall time: 55min 25s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "\n",
@@ -559,9 +1961,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plt.subplot(2, 1, 1)\n",
     "plt.title('Training and test accuracy')\n",
@@ -588,9 +2015,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predict with test number:  6\n",
+      "./img_data/resized/Negative/3837558_landsat_8_rgb.tif\n",
+      "0.6771707   :   (0, 'Negative')\n",
+      "0.32282928   :   (1, 'Positive')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Randomly test an image from the test set\n",
     "\n",
@@ -620,9 +2070,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6771707   :   (0, 'Negative')\n",
+      "0.32282928   :   (1, 'Positive')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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rQeYMsM41xNR3V4KCH7DQamG1YRyGrCwtUwgKhOEDwiQGhIskWIslQxYCPOWhAh8pPJ68dp35eoPj0zMGwxH9/gjChEw418H4TusKa3Mzy5nVxmqkNkgLSZLR7vSI4pT1tUucnHZ57/YdatUyV7Y28X2P4/YJJk0dpktASaQAm7sAQgkHcImhWK/SbDU4a3eI4gjf9wg8H4ElTjVpksxCQ84uEaAFaWyIVEq73SYcjRBCoPwAqXzwQEch0mqksBzu3Gd3+wHV1iKrqwtcf+w61594goVWk29+67v8d//j/0Tn5BidJTz+1NN0ewN8v8D62iaXL18miiNuvXeb7qDDS194jmqlQhKnKCWZm6sxP25w/9Ejnrx5gzTNuPXhXSaTCf1+j8OTE7rDoVvPLCOJDUJKhHTrLTCo7JzDUca5bEGxwGgUctbp4vs+/f4E3/e4++AB7U6XYrHAL/zNr9Oo1wkKAeVqDRX4LK3CpbU1tM0Io5gsioiGQ7zc0hucdemddRGeR701z9r6JYxR9PsTxsMRSIlEIKVgaWmZZ595iieu36RcLvPue7c5PTvhzp1tvv6Vr3D92jVA0On0HPt/fMi3vv9DpPBZv7RErV6g0znh1Tdf4/j4hDhJQOF4JkAKgfIExlrIHPjPuKycJNVJggCU50MytUwvWoa4s+H5zs34JNn7/1aU/58NF/4DX4MxmnA0QWTpzA/+iAaeuQbnyCaRmETT6/SQShHHCfsHxyhPEacJSiqUdCSTjmOEsZjUuGvKAs25OTzlcXTSZu/wiDRJaLfPnIsVJzN1PVXg0hqQkqAYkJkMoxMXpssyjnf3eOutd1FSUq2UKJcLdAcD5ptNGo0mw9GISTZ0yK5cuCe39BBWOKEGkAKtLQWviJSKVrPF1a0tyqUiuwd7dLpDJiJypKg1JOMJAovOYnZ39ilVy7hpKjzfo1ApIVEkaZqvuXXhQgS+EEy6bd5/7z1ee+0dLq2uUavVefqJG/zWv/w9BoNTbjx+g5/9yleJ45BOr0sURYxHCa1Wi5956QW2ti7l4TlLGIZ0+wN29w+p1WqEYcxbt95jb/+IV998m6OjIwwZxXIRYwyeFWghnWUlnN//kRwIcc52CwvpcMSdD+9QnquytDjPcDjm5OQE3/M5bbfpj0asrCyxtLhAc76JwNI97XBy3EZ5AVuX1zlpnxGNtYs8YfLT5IRcaM2wc8ZJ4OMVPVKRYANFuVyjWqqyvLTE4sI8ge+zuDzPlz7/RX7pF3+Bvb1dfvCjH/DFFz6HH5Qolgv0BwPqtSqT8ZjDw1NuXLvK4fExf/DNPybTCQeHR0yiCM9X59EqY2f7DyDzfAo7Ww/3i8gtAiEkJgcy627jnBqwUC4WaTbn2Bn0fqL8fSpAYMZcA1iDzfIEl8yZ4MYTs5wQ6Xu5C2DcghiNzgxaZ1gsUkqS0GlI4UnSJEFHKYVyCaEUOk1RnkexXKbVbJAmmuFwyAd37nH3/kOOTo7JstRpICVy/+t8flYKyABt0GGC9QR4Cq9YgDghDcfs7e2xvr7mNJo2FHyfUrFAEDhEPjg4pN/rYjKDlRbfCyiWCsRJ5EAhsyjhYa1m/3ifLMlYml+iVZ+nPldxSSrxLr7ymYQRvlTYVCOtpVSvs7A0T7Va5rQQMBgMKBSLNJtNslTTTo/JwgiDQuX3NQ2dpjrm9t07PHHnOjeuXeHx69ep1escSY/5xUuUS2UW5+f5wvPP0e9HvP7OOzxx4zG2Lm8yV9W0mhWCwGM0DHnnvdsszC+RpAYlh+weHPH+nQ85PTsly2KqtRKNxhxaG9IwRGfGra0+J7dcuMupNJPzOEI4i0ekKa25BqtLy1SKQ9qHJ2RJwsLGJZqNeer1Jq3FBUq1MlprRkcjfv+P/pjxJOTS6jpXt/q8M7qNKBaxcYgLqeSmp3B8TPf0FOtJtDXONTWa2lyZlbUFNjcuMxqGjMYhFkutWuapJ2+ys79Nd9BlEk+49+g+H3x4m3KxyO7+Ptu7O8RpQsH3iaOYNEvwfB/fTxECdB6dwlhs5iJjH4nsWItUcsYNOSwQ6CxD5HzFNJw6BUyBIIljoug8h+Dj41MBAsAsXg7O5DHKERsyj4s6stCjUq0ShhPiOCJQHsLzXVKQhKBYoFQsEBdTVpeXSJKEvZ19bKoxcYaWGYVSiRvXHuPy2gqlYpHBYEi1UmE4GuP7EiFs7pwLpplnVgqEvmhqCYQBE6dYIxF53gLWIAWMel1ee/MtMguep9jaWOfGY1fwPMUkjCgVS9x/8IDBcECaZTQXGqytrbB3uE8UxdTmKnhBkVZzjlIxoNsZEARFbt+7S7NRo9Wco16r0DeWaBIx6g3wCgGNhXlWV1awRjMcjcm0pdWap16t4nke7UmX4SRESoHnF2gutMjihEGnC9oipc/D7V1efu1dsB6eV2VleZV7H9zi/t0HVMtztJpzbD884fD0kJ/76heYRCFv33qfRzu7PHXzBvOtOQ6P2zza28fzFCCo1xsMxxPy+AtLy4usLC0QxjGHRydkSQbaYPNwoMyTv8DJpTQuYiIsGCln+uLs6JQscZaN8hTSZHjSZe21z7oYY0liZwSXS2W6gzPeuXWbrcuXUdInHE+w1iJnkpaPXLhMmkHqhMRiic2Ine0d4jil4BdZXb5Eo9rEUz6D0ZBS0cNg+fb3f0hvOODtd9/hrbdvUS0XePBoj/bRKdpq8BTFYkBQCkiS2M1BSIq+R0LmcmCUU2LGWOciTIU+zx1AulwPsC5CwEfdASMdfgKkYYxsBZ8oep8aEMg86fJctIt/y5zHcxaBxkpFsVKiVq2SpgmeqrC6ukK9WuXg+Jhur4+vfJpzTbJMUy6UiaLEWQxKkGQJUkkW51f52Re/xNXNDRq1MoESlIolJmHEB3fv8rt//Kc82N7GSg1pNhN6K/MEDWsxSiCtPWf1pMAYR9AoBHEYcnpygioWkUj2hGJxfoH1tVWiKGFpYZ7V5UXe//BDHj18hDaWySTmysYmSZqyvrrG5sYGly+t0u32eP2t9wijkCDw6A+HFAKfK5c3OD5pM5mEKCnQYcRoOObAHBHGzof1PI+VlRXK5Qq+pzg5OkFYQ22+xdLyAqsrK3Q7fSZhTDqasPNoh7PjDu++c5vvfPcpdJrx3tu38LB0j/b4UTShUCwxHI5oLc3z1OM36fXe4b0P7rC7v8f1q1f44uefRylBd9DF9wK63S5aG8bjCdYYoigiCHzGk4jxZMKwP0CnGdLaHPzdyZ1m/00JUZMDsslDu1JbosGAeDhE5G7V/Polnn/uCdIM7j/coVysMjdXQylFEoUEfoG9g306vSHHRydMBgMXZlUSafI4Ow4ArBQzs9oa61xObWjV51ldXsNa+Btf/RpP3LjG1auX6XZ6tM9OGI8SvveDl7n1wXt02l12t3dJ0xAvKON5gjQzaG3wtIdIs1lUy+U+iBw4LUpKtDHozFlpUw/Y0WNyRohD7i7kiW3gAMsKl2sjPQ+bGDz1yaL+qQEBkSObMM63maZCu+w6i9KWQlDk0uoqrbkG/cGQ+bl5bl67ysrSEvcePiJJMiaTiDTNXKiwc0aSOk0glGJpeZnPPfMsy4uLaG0ZDiNKgUe5XCcIDEuLS1y/coVef0C7fTYjWLDGCb47IehpboLNLRWnKtziI/JMBGfaxpMhu70+WZqyu7JMEPhsrK9x4+oWxUKBbreP1oZWo87zTz/N2uoKpWIJYwUegoc7+3QHfaQQrK+u0B+OOD7t8PTNm9QqVZIkpV8pc/Bwh7DfI05iwGCjGE3CZDgkuLxBHEV0zjooJdja2uDzLzxHq9nkwYNtOp0OxpwQRTHD/jH9wQkPHt6GJMbLk7GErwhKAe4uNePBkN/8zd9mfnGBw5NjDDAJh5TKAZfX19GZpXN2Spol9PsDkiRhHEUgBZPxmP04Is0SsmnY1F5IoMrzHkRmkFoglEIEHhZQcTKzyrSn8LRGaI2wlnJQYnF+ldffepc/+Oaf8sqrb1GvlqnUahQCn4eP9ugPh2zv7ZElCcakWE+hhcAaMVM8RjkXFOsELtOWgl8g8HzWNlZZWmzQmGvRatUolQPOOqd4SuEXAr7ypZf413/6Z9x/+NCFh6VkbnmV1vw8mY6wSLqdDlbB0sI8/cGQbq+HEII00y5xjTx/RU9Dgzk85cCEcK9hnPuLsefRAyEQykMB1mpsqgHJZDL5RNn71ICATHWeNXdO+DmiyVnnyhqSMKLfG1Apl5EoDo9OqVerFEsBT964ThQnfHj3Pp6nKJdKTEpltDboJEX5AUsLS1xaWUUKye079zg6POTK1gZbG5cYjMY82N5xee1SkaXpzAwUeRiOKUmV2Tx+m1sHGrxSgPUVxC4hKEsS0jRFao3n+xSKPlJJTtpnlMsVBosTlhaXefrJJxmHIX/763+DrY0NWo0mw/GYdrvDvYcP+N4rr9Kcq7O8sMB8s0mrMcfD7T32Do7xPMmllSUa1Ron+0ekaYrveZQCn8E4xOqUJAzp9XuE45A0SyiWSrQaC1SLLRrVBW5eKziLIooR3hBrAuIwhizJebo8sdpKJoMJNssgjUmTmEkS0TnZB6mQxRJh4LOzc8DpSZfheESaOMIriiN0lnHWPiNLYkxmiEw2IwKn/6YkoLV5irQFEXgUyhUWW00E0DlpMx6OMDlq5N4vUrgQ359p6PW73Lt3D601rVqd5fV1SsUCvX6H4WhEFIXnoVjp/GiLdPuJS0O2TIvG5rBaUCpWsDajP+hTLBV58fNfwvcKtM9O2T3Y5eT0hLXVDRZaDa5dv8KXv/RlVleW+P1v/hHVcoHFhRZpErG6usqd+w+4c/8+SZbkPJZCCoG1kMSRCxlPCeKpOEgXLlZ53oARTlFKT0HiWFMlPPA8yo05rDGMBgNEniiW5G7TTxqfGhCwOfFDHoKbQUEe7xTWEPeHHGR7lCploihiEoZEwyGPP3mTF7/wAlprPOUxGo0ZjSfUylVacw3GkwnjsbMQOt0eg8GA+w8f0en2GE5c+u/+0THv3r6NNdDvD85TNPM5zNjp86cgfyyyDM8WqDabRGHIJPd/TaYRXkBjYYGFhXk85dE567j8bywLrRYba+tIpVhaWGFxYZnJZMLe/jFaZ2ysr7F1eQ1PelTKZTq9HoHn4XuKt269z9rKEo9tbhD4BfxikTRJyNKEKE2YhhySNGVv/5DMZAglyTLN3XuPODrqcmltlc2NSwjhIyVcu/EYnu/x4ft3iQaDmXxaCzZLMGkyS282FpSUrkpSG8zE0E5iet0+vuchPIUxmkKxgDEZ0Xji/PcsA84tPSR5/P9i7vs0lCsolctUq1Uur6+xtrzEzsEB9x88YjwakQzPtZtFkGYpjx49JCj6VGo1sijluH1IUC2wcHWLxdYaaRayryPCMHQgMr3JnAS20xCwtvhCUStX2Lp8g+effoFO55RX3niZycQVQxWLHkkS8u57H/KdH3yfr3zpRVqNOosL83zu+SfZXL/E6vI8p+0TOr0zMqM5Pj4m8Dy2Lm/wYHsbT3nUqnWSNCaKY4RkFglQSqKkS2tn6vPnLqmw1iWLGUBrlPSYX15BC5hrzjEc9JkMx3iFgGQ0JByPPlH2Pj0gIPKEl2kEcJYemZuJVkCWEg76jEf5DVnDqNfFl4r51jxpkvDSC5/jpN3me6+8xngyoVwsI3C1B3EU0x8M0FlKmqVoYwjDkA/u3mfv8Ijd/QOs1hidudwEzmOu5xNlFqmYZflpgw4j4sAnzVKsdVVyVjvCR0pJtzdgPBgx7vcIw5D+YMCVrU2ubm2xsNhiOJ5wcHTKaDRgZ3+PUiHg2tUtfvWX/y6dszNGY8e4SymZq9fo9PpMwojheIKxhmK5SDQYko3cYyFAFgJUsUAYhe6AZSmZSTk42EH5PiftIw4OD0h1zMrKIluXN6lVa4wHE/Z3LfFw5KI1gBEK6UnQ2WxdppSItRZpNTaDVMRYDJ7yiaOIQtEHYYmiiRP0C+jp8tzFR9ZXmjzfwYISkrlqjXK1QuAHPHnzBhuX1qiUy0yikPu379M5PM5j5ZJCpUJBSpCeC40qD1UuI4SgVimzvDjPUfuM47M2npeibR5RQoDKlVDm/G+hLTqLSBtSHQYAACAASURBVMKEr/7Mz/DLv/SL9PsDMhOjlGJzfZPxOOK0fUi/3+fo5Iw/+fNvMez32dp6jGajSZbGbK5vcPnSCt/67nd45c03ee31t9BJQmtxnjCOiOOEUrmMxTAej53rNV1TIVCeQghFlmVkmSMN0bmLppSLIgiBVy7SXGq68u00od/puXJ3maKm7sMnjE8NCADn0pX/sNIRcDOFLFzuuysacsyzsdDp9Nne2adeq7C2ukK5XOXR7h77e4fs7e7h+z5JnHAcRUThhGarCUB/MKRYLDKenHF62saYDKv1ubbPicmpqWqFOxzTrC2TzwcENknp9/qOZJIKrHRkUhTTbXfodruYTFMqlwlKRYQQnLY7XFpbp1Ao4vsBCEun26Xb7TIOPBbmWzz1+E0Wm02iKCSKQt798B5hHFOv1SgWC2iTUamUuXRplUlvQDKZuKiaVIigQBAE6DQlTlN3b8ZSKBWpz7vw6P2H96jVqzz79BMgFINByNraJeI0Y3/0AEweQ5cSWalgJiHigkUw3TiDyAldV+SjrUEYfX6wpTzXZOJ8baWxrkhLTBcbl9qtPJZXlnnmqSdozNVRnmJ9bY1ut0sSZyzOL9JbHtM+OsHzPZbWV1lbv4T0FPsHJ5y1u2RZhpSS0XjCvUfbHJyc8Gh/jziOnQZFIJSYFee4SITN99gVJS0uLHJpfR7lZXi+YfPyJeI0ptmqUCkXeWNvj/c+vEUUTdjd2SEcjigW5tjd26PbPePxazfY3LxMmlubb779HuFkzN7hIWmWEfgBtVqNarnMZOL6CWitZ0qmVq2zsrhMt9fj8PTY1Z9IlxvgogUG60tSMo6OD0iyzCk9rVFKIuIJQlp8m07rin5sfGpAQEC+MZBnsTj/zDvPFDR5DNTxSAYhPbTn8WB3F1UIePzmDRCKVmOOteUV6vUqg/6AOAqx2pDEMSfhhH6/T6VawxhNkiboTBPFEWh97gZYZpWGVubCL8U5KFzIGzBS5FENi5AKqRQ61YjUkVbJZIz1FdV6nWazxXyrRabNrIQW6eF5PuVigeFwyHgyZjDS7OztIxCkSchjVzb4+a9+mVRrdvcPiZIkb1yiufFYnVaz6fzD89VExwmjOEZYg9KuBsNY8D2fcqlCoeFzimU0HNJu9+mcjTjrdikGAdEwQpg8TS13DKyQmIKPSBPnol0E7akVl2ms1i75R1ji3KqaCtkMOy74vXlE1pGqOfnqeT4LS0tcXl9nfXUZP/C5trXBPas5Ojpm+9EOkzBG+D6Feo355VUev3kTqRThJOXk8ASsJYljRjrjzrCHsYaMzM05My4nIa+/cLdy7ooKwAUjY15750ec9Q9Zaq1y8/p13n7vXV576w2eunmT2/fv8YMfvoaOQrIMiuUK8615hqMJ880WC/Mt5hfn+VzlBUbjMdoY7j18wPbBHr1u3xHN1hAnEUop/MAjTVKy2O2tkopKpUSSJfgdD51ppHLWJUmWz9mSJjG9swThK8epZBY1Pc84d/qTxqcCBASOYLOzGU9r4x0ZYqTLigNmlVHuYLma/dQ4IVZK0hv0CcMJ48nEmUnFwFUdWldphtFMRiOSNMMrFFzYzPOwRjsyano4p2WaUyuAnBeYyoS80MHHnic3IbUzZ7MUazIXZlICzw9YXFgg8APCMKJQLLK6skKhUOS0fULgCYb9LsPRkEq5TKVSwlOCbrfNXL1KIQioVetcvXyZrY0d7j3aYTSe0D7rEqcJUkpSndPbgDAGTOJYY+E8SiEVhVoZvxiQxCmNuTnqc3X6wz4P7t51fRGSyFGBaeIyDoXT8saTSLLz2mTsORDCLLvPNUGZSpLTsCKbxvjPex7YPB17lgWah8GmjVxsknK4f8TRpTY3rm5x+fI6zUYT2EYbw87DRygkeC501+sNSBPLJByRJCnpZDKbZxZHaCVdeXogUFLliWbO/JdKIvNQ3cxbyddsb/s+7e4JpXKFp556hp956UXSLOG7P3yZBw8f8tY7t8B4+KpAGk+Qfk4nW8NLOU91/8EDao06tWqFF555io1LK7z5zi3eef992p0uJ6dtjNH4vodSPkoo0jhCGMOg1+WeTsi0wRqDFOIcSKcKKfdRjbUurJ27DFjXd0FMQ4yfMD4VIMB08a2rfQacSWhmBMGMiRdmepAkKInnKWq1Kpsbl3jqxmPEScxZp4MQBrBkWYZQCr/sYcIYm7gqnUq1graGQa8HeSWbyBd4ij+QP5iaJfkcz9t+idkmaC9vCJJrwhl5MK09SDOSMCKJU8aTkHq9jhCKQqGAwHLaPsbqmCDwsUbjScV8c444SVhbXaHZaLC7d0AYxTQbc6g8GzEMIx482sYPfDKjZ51n8lSnjzKZStJaXqRWrzMcTAijGIslKHiMewNkorHShUBlHqYFJ7z4rpuTniScp+s4wDRKzKpYRY6YVuShPmD64tT0vzhETpLOyqjz/gvGZAwGA07Pzjhud1lYXGJn/4gP7z8kzhIInD8srCEaDBgVS3x49wHHB0dMJiO3n9ZZb0bhrEkhkVa6VGFl0dLkZ4pp24I8y86dHSEgHoWEo4gOp4x6Q3Z3d/H9gKODI26/9wGD4YStrU0Wmi0ODg7oDgcAfHj3Du/feQ8rNA+392m1GmRxTBiGbKyt0en0ePXNNwnHY7I0Q3oKVSzieR4mczUU1lrSNKPfH7rq2Twt2GqDwHWMklxALt8lNojUJRBleRKR1Aap/19YAkKIDeDXgeX8KP2atfafCSFawG8CW8Aj4FettV3hkt//GfBvARPgH1pr3/jLrkMeipq2CpsK0TQ/2gq3oTLfNIcSGVmakqYpWEG5XKZWKZElMVpnrC4vMgldameWafQ019rmvUD8AOEpbJprbJdG7sZMs50DgGVKhM2m5/xZKZHK8RNkbrOk55bW5MhsspSTw2NEngSSZRm9vjvky0vzLM0vU62WATg9a9PudEjShNFkghAKKT0e7u5xeHzioiJxQhiFCKPJTOqSlQARSGxmUZlx4bNpuzIhKFZKrK2tUipV6A8ecNY9Iwg8hFKzcJ1LzZ2WN8tZZx9rDEmcQJJ9BFe0yrs75c0sptrcWlfoBefrKoyLtJqckp9q/YtNV3JIAE/Rmm9Sq1Y4PD7FCknB97i/vcs4nCB8icE6sjJOGQ563N++z+D0DLTGeHKWfHSxFF0IgZyFBPNowNSVSzXS6Lz7Un7+hEDldzs8a/Nhb+AyRLMMKxVXr9/kpS98nuuPXaVWrvFoZ4dOv8ude/f49d/4bZ5//hk63QFLCy0qpSJJEuP7PncfPuCs2yHLcxykttg4I9IaYzQ2UFidN8/JpnkAbkjj8makcS6yzHK3dKqspvSKtdhAubyH+MLZ/tj4aSyBDPgvrbVvCCFqwOtCiD8B/iHwZ9bafyqE+CfAPwH+a+DvANfzfy8B/2v+8y8eF8gi5//nBOBU6+Kahljhes4Ja0Br0jSh2+nyaHuHu/cf0mrUXY9BKXn2ycfZWFvh4c4ej7b3aQ9dVMEK6Pd6yELgrpEL9cwMwAm/+Oj0zn8T51yBnEWqOW/thCvzdBVxkiAIsFoThzEidsSjKZeQStLrd6jVSrQacwRBQBRFdPPkmkKhgFSSN2+9z/t37rGy2OL45JROt0eqUxLtwm3S4sw/kQvfFKU4/4EQSOEz6kUMezFxmBDGIeORIUojDHnfxZmPD1Y5gLPWgslbmIiZjeGSpqZ10FNWN//8dB9njS2mlkHunsxarDFt3+auqSWUKlXq9TqX1la4dmWTwWjErfffZzgasXd4xHA0nvWNtACBR5wlZKMhCIuVBiu9vBDnfA+lMWAENvfDIc9MNY4wlcY4ALhY6z27iEBagUk1qedIw1Zjnheef55La6sUiwVu3Njk+eee4PW33ua0fcpwOGLYn3Dnzj1u3QopeD5+MWASTRiOhhirKVUKZFGKzqtCfa+Qnxvt2pFluVWXu51yWleTW1XCunOa5aFFoQR4rlhOkPfvzSz6o0zuR8ZfCgLW2kPgMP99KIT4ALgE/D3g6/nb/jnw5zgQ+HvAr1sHXS8LIRpCiNX8e37yNYTTEtpzSCaNdey7krkvPt1wBwzac0fHDwKajQaeVIzGY3746uusrS7x3FOPs7m+RmOuQRRFCKE4PevTkW1MmiGkQKNdkw9rESrX6jJvHmJcuuqMC5gebC4IuxA5qeSqzsS0AMaC8jz8IMDzFI25JTbWVhiORuzs7DPuDbFJSjYOMVi0tbTPztjZP3BhIJMRRRFXLq9TLVdItabbH/DaW+9w89oVeoM+x52zvBddrqV17ooIpyHUxXCQFZi8X108GLJ950NMfmiM1mij83520/vPrQGTdy6aAjDg+QEogxYJ1rgqTPmx0NNM5qdPWyfwdsrz5N9FbvZPORgLlKsVFhbmuXZ1i6ubl5mEMRuXVml3ztjZ3+f2/ft0O11sEiMuuIh22lUqjlwhjRA4AuhciUxLrq02pBnOAsmz7tx8p4TPhZu4uIxWYK1E59mERsPC0jwrK/MoZQmjCd9++ftcvrROY67GC889TcH3qNWqjCYDvveDH0Km8SsFkizDaoPvK1cQJZ1Gr87VeezqFbr9DnuHB2Ran89FivMap6mikmLWlBdcyr2VynExUpI3PCZLNeVihVE6/ony93+LExBCbAGfA34ELF8Q7COcuwAOIHYvfGwvf+4jICCE+EfAP8of5OXCedhNyfO0YeH6yVl74UAK13egOd/k+tUrbK5fIktT3v3gDnfuj1ldWmauNke3P+Hk9JR727ucds4wRudhLItVToc7vzFXUNNOwNY635ZzjTUdM7M1BwKTg4bIchQWgDakSYxOJX5znvlmC8/zCCPX12Dc6ZGmMTIR4Hn0BwMe7uywvraCpyRKSZ57+gmKfsAPX3uTs06Xs06HnYMClXKR4cB1ugHhuohLgdVTYi33Gy8QQtNuvEYnJGGCQaC9vBmpFNhZOaEA5eL0cvpZhOvdIF1GmzXamf/mgqCfGxDnSzUl/2TOKVwwUcWs72LeNEZKgkKRmzdv8OLnnuXSygqtVot+fzArdx4Mhwz7A0gSvEznZwaXPYdrOSdFrvmnnZ9yZTK9T0FeCCbzZrPkgjRVMlPrbxYbnuYsOJAySrq+EtIiU02apDzc3kVKw8rSMu++/wEvPPscP/vSFykWfe49uIcQhv7gjKXFBp1unyRN8uQrgwa0sZi8XiZQimq5RBgWZovqwqv5mubhTOde5vuqmK2n0BaDcfPEOH7BgAgCVDGAT8gX+qlBQAhRBX4H+MfW2oG4YF5Ya634MRvqLx7W2l8Dfg1ASmVR0t0QuJuXApk5E9SZpHmP3LyaLCh6XLm8wbNP3KQx16DVaNBsNPnuy6/xg1df5/72DllmOD1rc9g+YhKHZCqvzppmXF3wkeTUz88p4lmt9scB4CI/YHNtmGsTNSVfpMHoDGvgeHuXZBLiV4okaUKSJWTKgoFa1fX46/Z6ZFqzvLBAtVphvjmgWCgyV6tRqZSZhCFYy/HxKUHRZxKFM7/bSlysO4+uZIFy1kw2LTW94GZNIwfTg32B3Ly4FlaCzn32KWiiDTZzjqUgN6Pz907dIUxuTYjckspbp7nEBdcFadrKXXiSYqnk/jYDgmarxdbGZa5uXaVerdDt9bEWBqMB7U6PNM2wcewAwIKZxR2tm4vKIctMQe+iCzc9cznAZ66oTOBcBDlNTJv+P/OknOvgoiMCWQwoFkvoJCGNJ9RrFVqNJq+89hp37txnNIl45okneLSzw4PtXf71n/wbMpOQmYxKuUShFKAyTRw7nkgpHz/wHKBrQ7874P0PPyTT2lU/WovMBZ8cNAHXgOYj5r1bY52DbKlUQilFKSigVECv3+ccSX58/FQgIITwcQDwf1prfzd/+nhq5gshVoGT/Pl9YOPCx9fz5z55WGfKGKXcAZHn5aQuwSQvosi1tTQWJgmT4YQkMdx7uEOcPCDwAuJxzCgc0+sP6PX7TJIJVhiMcQKT+QKl3WGU5FVqH5uLM1FxKa2cEzEzbSfFOXmZlz0LKfKUznMXQlhLFEUcHRwgCj5GCXSaYnGgk8Upvf6A0WhCMShwcHzKQqqJoohbt+/h+z6n3R6j0RgTJYzTmHEosNYglOue6PrP5ftk85bcU9NXnGthV4d1zhNMW1S5WHluyZi8M63IffW8TXlewI4VOZ+h3P1pj1yQXNHNRULX+hdIPnLgUW7u0lfMtRosLS3Sy6sYK6Uy+4cnvPH2ezQadfYPjmjMzSGE5f6jbY5OTrFao3D3qP3pJrk9nM03y/cDZxKTmfNmJXlDV2mcUyKNQV0085gZm+dnwYLxcosmzRC+oVAsYRJDu9sljEZc2dzk/Tt36XR7vPbmG8RxyL2HDzlut6lUAoSCSRjiSUFQCqjX5phMIuI4Im8shyx4ZGScdTrusnkvzWkH6dnfGkDMUtiB/HeL8gPW1teZRGOEhFajSZqkpGmGQFCv1uif/mTxEz/2104+/gan8v850LHW/uMLz//3wNkFYrBlrf2vhBC/BPwXuOjAS8D/Yq198S+6hhTKeqoIvnIJZVPtkpu2wuSH21q8nBUVFgqVCouX1rFK0O32EFYQj8boLAUpSHQ6KxD5yF5bi8pgSkZacoU4DUsCVk1TWJ1GmOYQOFM7/xpPuMYP2jrewjqAEL6HUgqMpdlokMYJvUEv7y+Ym3mpRhZ8SvUKfhBQDIouF1/DsNtFBT5IQaY1UTTBTBLXYCXvs2c8AUI6QDIWlZqZnyw1s5CQyfMUpDk/1LOOPTL3ddW5JWBy0x+L+8MW08995FC456b9/l1EB+diaAteLjizt08jAQ4sG/U5rm5t4imf/f0jet2+I1iFICgUKRWLjIdDioGP9SRhGjPq9RFRgsp9/jhwCkNlxgFCvkfTfcoCyblPOXXxcpPfgNIaOaM4mUV8ZveXPzTS8QAq32NRLLlXdEapXqPRqFMplzg6OSOaNnYpKMLxhEkcU6mVELn7oVAICYVCif5gQjiJXKt866oBrbCui55ynIM2Fpk3BTDWYA2ugMg6kLBWoFJnFXzuiy/y9a98jf3jQ6IoZjQa8N7t95mrtxgNB9RrFW69/crr1tovfFz+fhpL4CvAfwC8K4R4K3/uvwH+KfBbQoj/FNgGfjV/7Q9xAHAPFyL8j3+Ka7hzlffxt7lffv4CeZfg/Kn8pXg8ZvfBA0QhoFQp4xcKRNKSJQmQL6h1oTuT92XHnrcn58IlPjoRcvQVM6Z9avbaaYmpPZ/X7KAZQIH0JKVSmXKxxOb6OnEck93PGI0nGAG+79NcqKOtxS/5FIMivvLo9Lr0Oj1smjmBlmIW6hKe8/lEHpd3FofBmmnrtfxm8lJT51a52LjOk3RmgmhzczLX8FMzPn/kwHBarqsuCPOsrdW5CwS5wOffLa3FGDFVcA4AhJiVu/pS0aw3CLwC43BMsVhAAZNeFysVkyCkb8HojJFOnfVU8pHa/RWfaWv2GVlryV2fcxNZe/mOGjsjeq0QaOVCzMpcAICZeTcVemdJOcsmB1ArETp1MflkwjTCEfc6nPQ6GKHcvHyPoFJikhlqzRZy3KfgK0aJa/ivtfOhrInAupwOkRqwBiOdiyKFzOsCyElLgZISoQVGGJcQdNEvFTjLaq7KtWubNJs1tjY32d8/olSsUK0E7m9hKMmtt1/5ibL300QHvsfH5OTC+Pmf8H7L/0Xdm8VaemX3fb+19/edc+58b41kFbuKM9lkzy2pJTmSHcVyLMSw4QCJ47cAGeDEQJDXBAES5DlBXpxHGwHy4OTBQB4CZbAgyZKsbrXU6m42yZ5I1sSqulV153umb9h752Gtvb9TbNJSpIdUDkDeoc4937T3Wv+11n/9F/zDP+tzP/nKteKnCCV5Q5JMe+9pii4AndbIg/c0CdoQtHqQ+Qaii1cXzUpZCEpMLDFRdbFsrvwKtXnEWJ47YHGvw2C4wWkR3X8dxNAS/YjdK1cRcbRtRzWa4JqW2PeMxxN2dnc5m005O5lyyqnGx31PSr1BaSmLOj9zEUv+1K4kS6UoL6n6TsjoRvQC7fYV1R4cBOc1KRoTyTk1HAGepgBCMHkryMhbysZxUcozKCxBezmLWaNADgoQSzY2gbOTM9peOfJ939M0SxvSEmC5tIqNJyWnkvPLnui1iuCSmNBIKtOa8tfCB8i5HEv0Ru+gEsQnXAeuM8GS/CyjUoSjd4SxCp3mUpDrIr7r1LhhPBUzhilqc1UAjcmXPZsX9/iVv/Ir3Lh+nT/85r/g/v27NJ3OMYwp0bY9Yxsu4pzQ90EhuxdijKormOO7lIgx4SvBiyPa2DXxEO155fs7X8x5fLDPfLbEO+GrX/4CdQ2//0f/kou7F3j+ueufufeeDcagvRKU+XHZMmMLrPDOvei4sqyiYgt+eT4lLRYEZwtPFNIr5FcIUcpZtsjLcZ3CR01uDVbAmYfMMwByfJy8JbjcQHxJkvT3qCHrzuc8vHOf++ljFs1S43MS3lWMx2Oati0loma6sH2oKAgrT0rM2X31SMGptLmY3Lmp0JdLkZiookLY6FWtFzT/kO+tKaPoSDKLNUtsjZRQB2dToJ66F0PolLXsyNUIl4bRaXaTxQQvMIMjRtHd2FhnfWON+/uPWC6XpL43NIN+ngOy7n4E30QYO+LYazdciY9ZcRTDuYpJlUuC6D2pcnhRLb4Uol6nV+OQaofrErRG2ukTqTKD00eqLpRS5sCU1COJaJzpUTINXrj2/DWev3qZv/7XfpXrV6/wT/7nf6z9E75itpwrDTsGolGAq8pR12O6PhRh0WgIQG9sINhjE3M2yVBuMn5G6np++lOtRFx7/jmW3Yy3X3+Lpu145713eeOV1/nG136ez3o9U0ZAYobtRoBZWZQlSx9jiXNLv4GDIAmJwbyCPSRjVJUSysozjCjcy7A2Wc6hiIXYW+1fbVLQSgPRcJiSVU5Oy1U+JELfM52eag3cCVtb22xubXN+do73OlC0jzZMEmjO5oo8KgtZVg4g5m6SFxh7XKuIgTx8wqEw3c5Xcg0cionQcGYIdqMhi2QLuciow5DMs3xDvsh87fkVvOY4JK4eSQiV3ivvNUywkR2IwGhtwuVLF9je2iLEwP39R8xDB8kNyUpBjUeesQC4NoKhASJU5gljrkAkLC9iuRyB5DzJDG02StELjOx+JN1YSRy9F+2YDFj2ndIxurIQzKxKjro0GhQBVyFdy8npCXfu3+d0es5v/I1fZ//RI37nD36bnZ0dnhweM5stmE9PqXyki5HtrW02N3d4cnjI/f19o647fF2TYiR0LTFYNWOVvbqSy/N94uzRE753dMS7m2u8cOMmXSNcvXqR+aLlJx98yP39B3zW65kxAsmgo+/TwDlf2ZTqTfJ79WZoVjppzJthXXhqnVr8OhiAJLqQ1POkoXxmCwehKAllfTvIQNg6GZ/e/SX+zecla2MV8OhaTRqGxPxshhOP9xV96JmeTEFgY32dPmzQnU21JFq7ErsrAnB4J6QQcJWnJyG1w7eRMniEpN7sE+FiFAwZyZAMLPc7Mx0HdCR2w3IsLJVAr5siC35qzKZ/onTVIaxK3mLwIOCG6gU5b1E5uhipR2Pe/vwbbO1s8eT4iPN5gFoHrmQ+YrJSo+tTAW7SJajSkMAVIXoPKWh5dM0pWusBrwjO+Fwm1WUIUTT5FqIaOm85jdgLqdO4O2sePv2yVZASAfCjMePtbWbTU8ajCeE8sH/7Dsu2pWkb/t7f/Tt89Stf4qO7HzAeT3j+ued5/8c/YjpV4RXX9YiDS5cu8dabb/Ot73ybux/fo6rGXLx0kRB6njx5bIshDSGpfU0eUq/PODroQmCTirdee5M3X3mJs+kpW+vbjOoxjx8ffObee2aMQFmkOZa1JIFEjdWGHgJso+Z/z5v2ExvTPrO0qOfPzvbFFkfmUz/FcPOUjZgz3/nDP7mZ8IpEhuMLG9uaMT46PGa+WOBGTuHgYs6oHjGptD7etg1NsyTGiHixrP5QisQ2AghSmZBh5vJnwo6VTHPyq3gsOx+3cl3iBkOVvX9eWDr9SHkPsZYS4zuv2zLacWW1WpAsBLD5EElQJCOCSLJJ7AlfOya+Ji472tmc/YePObxxwu7WFl94/TV+euc2jw8P6UNvzRj5+nUgjQ/266RTniRl4yOaM3AajkQRDRfLeUY1ThbGaF8HEMCJLzMdBaEzWXExghFeSCOn0l3xE8agmlCtT6gmNd6Ba3u6ZoqQiF3L4/19vjU/5/ToMZPJmLPjQ5Ibs2xmNE3PaDRiPl8Q+o40ndGHyI3PXSelrxFjJCZhbW3MyekpIqJVAut0dMteFYgrwUVRbordm+gr9vZ2+dy1q1y9vMPFvU1+8WtfZ2d3j1/4+lf4R/8jn/p6JoxAEk3CSZaXkkEt1UxviWujeR0xiJ43ejHaeWPLEC5Glxlxxu5LqVAwn0IN9rCjDGEIK3FxCT9Xf0Gi8DNDQHqYnUxpFi1t1zGajLl65ZIOxth/zPz8nOVspuUmI+UohjXHHrRWL2J5iDaVJGcScJV6uOiFqo/FKIWKgfATh/PM56zUWbuYaJlO2zA5e5i7AV2nGnagnjRmFp6d56rBLkYhh2+15Sq6iKs81bji+tWr7O3scOfOPWZH5zy8/5B33v8xX3r7Tb701ltcunCBP/zOd9g/eEI2TmLxQ6w1jNAyLIUjU8Ie8+4SLVlbjL0YArA5FiJI5ZA26Drro0qgaY1FP9epEcGmHqXK0dei4+qSJqxDiLz80g2+8Qt/hY/ufsiffOtbVPWEydYWi9mMuJgjTUtqa44ePeDh41Oq0QjxjvFIePO1Nzk5OeXDs/u0TSJ5TZB+8NEtjo4fc3F3l3oywXnh9PxMh+XaPpBW81KC5YZyxxZaHk0x8PjhI/7P3/5tzmbn/Fu//hv8G3/tr7K7w96fJwAAIABJREFUu0fXffZE0mfCCIBa9TjKPeb6cH0YusCcJYSkcgQrn4XauNPpaa+fIV9eMSJDzBtT0tpqhrUr7LJkBkQ3om4fXWxPnWlZoDnrntkEThx+5IldYDmfIbVnMhpTVyNiSoRc5QjDsElxBZCbNzUGnAzHzhRbUAjuOl34Q51eryH6/BlPWSn7kMwXzDmQAdEkAUaa9kpGmZYuEntDFN42WA43MnrwHj9yKuaayS0JSBFBmEzG7O7ssFy2PO6P6WJCxqMM+Ekktre3OJ/OkCR4PEmiGfSk2f1sxgo5a1gvg0UY+AHJO6QSnHfGktSNG419KmOviEASKYVyX0SEeuTAe1o6DR1ShFqPk4I6CRnXHE6PmXenvP7qyxBge3OLPjY0beCdH7xDc3zAfNZwe/qIGMBPesYbI+o4oq5HfOPnf5Gf+1rkO9//Pu+++x7HR49ZLs65fe8hIo6q9kQiJ2dnetOj6hMEUslFqONIDKU0bSuOCHfuPmRn8xa//q8HvvKlL7O1ucXjg89gCvFUj9X/d6+80MXIHi4TgvK/OylQV3qFrMkLqdY2yRWUD2jCKlaiX1fVIjIf3pB19mJZ4FRXsZSadvaggP7O4lAsns5GQwDvara2dtjbu4DzHnHCzvYOk/Eay6ZlVFds7+5S72wSR2p7s6JuXtvOvJ2Laqh8r0YwZ+VXNRh9SFSdhQG1J9q9SDk8ETS+LgV1+719wJDQM9jfa2a8iH2KhRIxQWdiFhk5mfGsnWPkXQmFJEHtPLWvGa2Nubi3x5ULlyDB/bv3mR6d0M0XpD6wXLSk6Amh5uBkynSuo9stkwCizL/oFYnkaoY+X20DjoaK8ssFLenJPJBmSs11ojwRnfuoRjaNNMRJCSrnWKvHrI3GVM5RVw5fGzzqsyGyZyRqLNqm5/s/+AHHJ4f8zV//Nb761be4eGGPF2++wGQ8IhjylBB0knHTsDydU482+PjxE249uAc+UY8848kap0eHzM6OuXr5Mn0feHxwwOHJEW3fFTJbAlIl9LXyPnRNZqgMyXlCPaKqKr741hd49bWXePBon/PZGfXI8/Zbb3zm/ntmkIDvLcFn3izX8h2UgcKsLM4kTh+ulcISKNEow9TitmzzWPY/Jf1ZKnuPrIQSUCBu1Sed6Fq4BujnhWSsQGceQrXhU9/R+w5Jot13MTI9OaOeTKj7GpFE13X0TfYyaJlRAsniu7zaogDG8pOUCBNHtdSGk+wYM6chSlaj1YtYzW0M12Tlz2QFRbdaLclwWhOYMRNtMuRPrPwtWqaKIF7oU1IBlQh15TVkSDCejNjb3uXG9escHZ2xmC2pLUEqkkhdw607d3j4+AlXr16mCQ2LtiGJjpbPiVpxECtDKE43MAUB5LFbmVORkbFWjYiJuOhpR748YMG+NUidQiIQ2d7dYWNjnY/3HzKdzTUkKvZZjIYMVRuh7Vimc/YfaU5nPKmYTeccn5yzubHGZGOsIjUpEZ0beAV9z8nhMU3b8+FPPmJze50Xb97kq1/9IsfHx9x84XP83Ne+xns//hH//F/8NtPpFOn1hpZeCO+UjdlD7AK+10G4vYc4qkhdx87GRX7h619B8PzW7/wufdfStkuOjj8bCTwzRgB7vnkTp0z5xG5CkhKPEhO0wQYssML/X9EGYAUd2IMvhjNm+J9DgQFSJbGFHhJVgDAekmRZJNMlqMcjQh/prVFIukB7PiNuBOrxmPHmNpPRiLbrWDYdfT+jbZaEprU6eybbDAk+oHi2VFm2PESkN0puSvQjh4tOORAGmcvVrjKazNvr2KpYYHy5JbbxfTcYzCSKCLKxTU6gMrGUYPsvIw3vCH0gBUdda1zi6oqdjXUmkwnj0TohmMbfwREVOSzTG+zajkV7xJ12avLk6rWROJB/opUGBfqRls4kKXMuX0TJ68hweUoSs36PpXLncyK45ECsNTc2PfPTGVubm2xM1pmdTofnIIJzTo+XzCAZZ6SZzfh4NuOfPXpAlTy+HlF5oZk35PyIzk/UZKSkRHd2QnN+jl/fIvbCfLak8p43XnudN15/g5s3PkdMPd9///uE0LNctBrLr5YGgeSTtgznnyu9KBcCXbPg6PSIN159jf/9//gh7733A3b3dtje3PmsnfcMGQGehnyF/ZU9lijEzzXzp5N/gxd9ygOSIb/Z9GxcvGi5aWX/6EFlIOrkGLxLhNre6kytJukMQEFDFSrteUhdj297di9dJEY4O5sSmk6zwHZ+KvgoZaNVxvKLJhKaiS6Ip2S38zlGwSexuFmGZBb5Ptk1rRiC0o+9cj8ErMZtycbcIyGKxLzlHJygeZqEavE5pbDGmNgYTzjv54QUWXMjrj13lVdffonlfMHZ+ZzJeMSibXC1h1oHpgpGuAmtFmUqTxeUIu1xamhssEvM/Qz5evRm6/ATGKYU5e8/8Sh1E+bnlp+v3gsBC/nUEMYQWV/bYDIZcz49Zz5bFNmz2EccmXdgydo+IanHixA7IcWeznV0k9EgVW+KwIzW6BctleUpdncvcPHqZQ6PD9nb26NplpzPZty5d4fTsxNmi3MWTUPX9YQ+FMObSGbQ9aK8aDgsziFtj7ehpOdnZzzcf8hiPuXRnVss2o7bfsxXv/alT2638npmjED2yoUIlveCPcDywFblrJAi5OEyicgy2bLyGQVZWFyb4WV2I2Wks6GEklGyY1btIFyREWlPAueoxiMIiS4kRptrpBg5P5/x3HNXGHvH4/sPkdAXtJHsgUbbwL63ikXmleeSVqGEqqHJ4hk/49ILxrV8QEYGZhQyckq5EmBwfjUpWM7ONlvMCkApIU0sJBVBqFxFG3skJrY21klA3/VUfsy/9vPf4Pj4mN/5w2/RhUTTdog43KjWZGjKz9cIQCFpF18IpNSXhGsiqTRZ9oDaDklmEUoSrWBk7y6Uc0zlGQ4VnvSJBKogeO9JKRJjpFsuqV3FjZs3OTs758NbtyEmKuus9CmaLDo4zLNbFjSJ17kFUROabjQi9MEgamTrwi5vf+MtvHdc3N3h8qU9xvWIW3dUF3Jra4cff/AR333nd5isT+hiy+nJCYJqOgI6ttxDH6IO4Y2JZdeTohKOqhD1vLQ1lW/93u+SIqSuYySOlDp++u47n7n3ngkjkIBQK41zEPWg1PBzychFXTihElLlC2tKDAGIUXrzBk4WPg51fnC+IoagG1q8Jop6czm5vmxxoNKMdWP5QiZSw+NDQiphY7JB5XVu1fVrz+OS8P6PfsLRwTF1VRFHFaFN+BiHqxWxvMagpmQXoucYKBA+ZQ9oDSUStVvwk+3hKd+DbLxW/k2jZmutFS2bYQSqlK87A6OESazbH0egEi5dvgiSODudQoIm9Lz96htcvXCRD+/cY7lsuHX3Y567fAWS8M677+O8J3Y9sW1XZNj0mWlVxTZljAPPACMlibEa83OMER8ifaXG2OfSqVF8i7F3K7cyK6aDXmMl0CVN+IWgk3v6SIgdp8fHzC7uMRqNWV/foG1bIh2+D8SgRtFb7qm3acsuRqvIiIZVi4bYttZ0pMhwd2uTv/0bf50UEpcu7HFycsDly5f4lV/6OfafaNvw+vqEH/30PfYPHtF2LfnBS0pUzrG7tc3169cY1SParuH45IQnByd0voe2h7hUtGNGNixboqtxzut6SYl+8YzPIhSMGebUuxISqbJFUppD8rfW9AIlTkiWAXbGbEs2S04sjs0fUAZgZL5/7EvScLUZRskooqwwW5C5XFVtrBHE0c8XSBNo50tuvPYyu9u7nE9nrE8mbK6vcXBwgEdHnTvrvAt+hY8vdn4pFZWfXOYS0OShEztnrJxlfxdl2LT2ysAG8uVJob8mS5alPlj4kK2kVZmclQJXQoroBW98g4SOx37xc9d4IPs8enjAeG2DnY0t3nrjDcbjCd995z3+8I+/w87WFh98eIvldKpad6Bz9rIjXwUxCSXmpKdZnZmpKYZgolBIQmI5ojASq2gA3ujhSXTjO4um8nWCcSeUbyJBE5o+h4kkurZjOptTVzVvvPqKysHd/5i2azREC+CjK+rKiBCjL+cfKzufkOxztbIhXsejeee4du05JpOK0bhma2uL6aLhm9/5E373D36f0+kJ1ElhPor+UgDvKyaTNS7tXeDlGzfp+p4Hjx6xt3OId44n+4/Yv3NPJx1vbDGpRzS95qF8r920KQkyWof2/FP33zNhBDSjawshRlwIxCwtjqiykMXUeVGWsl6GfG6oqydjEOrCs81duNdS2Idl9mEliPOkPhTkmcOJnHjKI7H7tjVSm8UaITGft0zPH3I2O6fyFVGEzd0dRlXN/PSctu/U+4eEREUZyQMGa32JTw35roQvyijqh+tcAQ7kRGj+jW3+LE2Ny/G+lEx/QQnG6sOounmzWNCyMlRFtRe6piH0gc89f43D/WMubu3w+isvQ1QjN6pH3HvwgA+7lmaxQHKOA/Q42UuvdB+WSGa1RwE7FRELjZQphwtEZEVlN1PFNZzyIRHdQCiKY2dK0ORigjoXBDepiF2ALuAjxBA4Oztj/eQU7zxXLl7i8oWLzGYzHh8eMJlM2NjYYDqd0pzPqLpYWKWZep7bj1UGXC/BJTifnvPO++/y2suv8uT4gPPpCU9uH/Hxw31OTk747g/e5cNbHxXJewS882xubpJCYrlsefTogBQiy2WDc8LFCxf45Z/7KnfvP+DOvbsEcdTrm7zyxut86a3Pc3J2yh//8Xc5fPiQSjy7Fy/y6ltf5g9+7zc/dfs9E0bAV6oxt+i6EgNmzriWhtQdK2NNWSvaFJSK14eVhhYjWAxxri3tGBGbPltoAcmshlSKMOKwU0qzkNg4NKEkpnxUr9IvWx4+3Cf0HfVkxIWru7TjEY8OD1l0LZ3h7uQpo7r0xJwhH/VeqYLea/YbGBJ+vcbKOa7NSAfUaCYo3YuSLyyBONvt1txTtrftvNWOvSytlsq1KgJKXpBJRVoGltOlZrO3a6QLPHqwz9n5jK+8/Xn2nzwmkVh2S7rQ4ryAsRn1BlJyLjkqX30lAK8S4rk8GL2oYAsJCSokErxSiCUpRyJD/1xLl3wvuoRb6EZNdTb8elwRtItPlLiVrD9lsVxydHSCrzzr6xtsbK6xvb0D4njpxk021td470c/5sl0Tqw8fjQiLZe222UF4ViPi0CVEicPH/Bbv/XPOfnGKR/c+oDbt+8qe/TomDt37hAWC6qm1fXdCnHiWNta482XX8X7ih/+5KecHh5x/94Dmq5nd2cH5z03X3iBFz/3Aj/58EPWJxtsbm6ytbFBiJGN9QmvvPYiiKfywn/yH/7HdCH8xY2AiPwT4G8Bj1NKX7Df/a9AZh/sAicppa+YEOkPgR/bv30rpfQP/qxjAGzsrtGcQe2E0ZZnOVuSmk7j45Toa8itq86EGRUSr+J9GRKDeeEZ9z179ZSUiJ7DBMndaDkVndSSY1z8RIIKXGvHMswqDqtS6Hivze0tcHB4ckIzW9LMZsQUrOSYPVYq5CbX6by53M+OOKTySIi6GSxOJl92PnaJkVPJX+Z25mz4dH9L2XAODW+MzA9+aJvOyIMEVCq1kWLUZJeRtNzEw7znwccPeTR+QoqR0WTMxQs7bG1s8PKLN/mj732frmlxXh+AATdKJjUbpKfpl+V8lRRk5dLkSOJwURNs0Why0UrFWV4wN32t6kAkB3FsCeCYSnnOecF7rWyEYmxiXuO0XceTgydUdW1Mxxf5/KuvkYCXbr7IbD7lw9u3B+NsnUklL2WoIM8sSPn+hcjp/iN+97d+l1dffx1SpO8Di+WcZj5Huq6UuolqvGLVcXx6yubGBqPKQ6XHOZueqTp0Au8q1icjm6e5xvraBoeH53x4+w9ou5YvvPUF/vP/9B/y7g9/yNWrl3SS8me8/jxI4H8C/hE6gERvdEp/rzxEkf8eOF15/4cppa/8OT63vPoQmC0atjfGjEc1a5MR55Oa48NzUquz7FwPsdKdnxc/LgfYqUBdhZ22IRLlIWXPb2J7uovzz2Dexq6PARY7iyXDaAWPY+o5ZlTC2RndcomvK/qupe97fEAfrrPQwfaBcr8zW4/SditJNGY3TbzSJJMPabuloJtypgZJc5ksam16QDsrbnJFPUhvgR4n1/6daKnT1x4nQrNsGY9GXL54gdrX9F3L+voah/WY3d1dnr/yHBcvXKC6/TFdF0h9TwwaJyfbFPnCB07/cINzOJDI7NfB6rmgBsCnHNoIPuqkp4Epq/cxi5oWL5wYdAbNIXjnc+d1oWdjh5SUYNmQXIPb2GR7Y4PNjQ2mswVn0ylXLs91gvBiCUENXN82ONRhRGsyc4YiU3IE8QTxeOeoJBIXC376/nuWlNWmJGcIRZOkIL6iXpvQL1vu3brLeHNC2zSMKk/E0TatNh6FQCIwny84OjpjazPy2kubvHjjBn/6g3cIXeA3fu3X+bt/62/ya7/6y8yWDW37l+gdSCn9nnn4n3mZ/uC/C/zan/U5/+qDwHy2YNGqHLPzjks7m2xvr3N6NCUFU4QNUpJGKf9hbowfvmhs5ZzKlcWcccreQhd8YlAnEkB69ThZPgwG6673YWUh22fkGXsSIyksaRsLWSpHECv9RdQzmFfNC95JJglRJK2kHyTR9RyFYfOvpLkSA+KJqHevhh4GGPIeWCJNRHCVympG65jLnzWqPXjH2nhNJzr1PV3bM6pqdre2ef3ll1kbj5nO5rz8wgvsPzng/v4T3vnhT3j9lVcZTSZ0fYdUdUFpsVRazAiiMXxO4K7YAnt2et+daUTkUCcjvhwqVLaWo2jTWXKrTVDDM8qPPD+/mBI+Wa9GH4lZqk1SEW+RGAjLGfv39xGpmC5mHB4dc/fuHdbWVAbceU/se6q2t5mVlP+Upo0JOAU1qFXNZGuHxekBsQsrjW62YPNKFoeMR4w3NuhEWM6nLNoleEftPeOx03HvrTCbzej6BrOTbG9t0TQNZ+cn7G5v8YXPf4EXX7zO3oVdzudzRHpOp4efuf3+sjmBXwEepZR+uvK7l0Tku8AZ8F+llH7/0/7wk3MHUqaHkoghMG9bLm1uEhYd8+kciYJP1lASFXJpq7p11VWDlxvEFzSplSrKMJHSaSeAs1FWcWXzxLI0KR8IlL6DwjLMGWF7k6EK1fDXeDoY4eWTOgfJocrKwchDQjEAtm0H5d6cR8hJ0KdYjvq7KKIeaoUj4OMK0SfTaPP5Cwy6Q5r5r33F3s4us8WM4+NTYh+5+NwVfvWXf5EXnr/Kh7fucv/+bUbVGqO65vGjJ/hqxAd37lFVFdsbW+zLExXG7DqF7Ek9rsbeet3BVJky2UtDEZsqlY2Gy6rJ2Yanp6ohLqOsGmKdORQgIdPGB2SYnKh0F4ne1k6yZ1kGddoEZUFIsePR/Vs8erSPG6kuxMF8TiIyrmuVAPMOn6KpFlOkx7KfERdLm0ZIifF4RLe+zXw6w6WIJK3ShEpU8BZUEDcE2q6jXptQEQnLJUIiSKRpe7wT6lo1CPuuI6ENcU3bIuL58NZt1iZrPHflCrfv3uOHH/yIKxeuceOFmyy+t/y0bQj85Y3A3wf+6crPD4EbKaVDEfk68L+JyNsppbNP/uHq3AHxPukQCDWjCVi0HSduwXhrQtf19E0H5JBy6F9X7xAJ2ECN1ey4HgnjqZldUIWZlONjJwrBsRjzE7F3/ruE1a/J7DFHqmxDrxTti0JyTkDaAL6Y/z6By7I2piVXvJWXMpBTsriKubQMnQe044xLr9dDYRqaGUlYt9yAHghxRRYsGxeI8x6oS+J0Z3sHtmB9bR3vK9o2cO/+Po8PDrn/8BET7zg/OuH89Jz3P/8me9vbPDk4IvY93m5aaXCzacBFvyEJoaZ0cubnl5OSWUwk7+LcG5DyZwBVk0xNWREZmdOQnas9MRF9vmK9EzHrAjlXREOcAz9Zw0kiLKeICN5BDC1p0amuoN3DfrEk2dzGUHm8SY9JoOQBcsOWYR9cv+Rk/4FyCaLKnCd0PWRSVq52eAHnFK1U4zGhD8SmgaSTqoJDhUjthLyrSNIzHo0YjyZcufgcm1ubOHHU1Tq/+3vf5hs//3X60CDS/czmza+/sBEQkQr4t4Gv59+llBqgse+/IyIfAq8Df/JnfBqCDYoMthhi4nS6YH08Ym17wvJcaNu2bMrS/GIWWFuKY1ls2ZOW0WKJosunQzDMUITsYSlQLYlQVTrOKYQOb+gg5ocEReKs6KOHUlTHB8gyaKQcElAgehLR0WUxL/JUGpWCF1KyBRNSYRiW0uaKedMTMYXaClW8jTaGy1kZ1Xn1cCmShVrypzinYYIkCF3Pk4MjmmXDjc9d58tvvcX9h49plksOj495cnDIfDEndT2haaiA2C75zve+x7JpOTk7wiVtyJFKCs1WFYYSpXlJKHMLVjUflbJs48NTNtAUQ5FRg+QYAdSQ230vVZekqynzLSVp6siJI6aIq+0cugi9OoyqrnBe6JfDInBWr43mbbQ0q+/3xdAPL2/rViGAFMMrqFS4XrsrpWkXVNy2SNxX2hPRdw2OsXZTVh46V5LFVKKNZVIxmaxz8cIFZvM5m5sbvPTii1y+eJlls2R3Z5tXX7rJ0bE2b33r29/mD7/9x5+5+/4ySOCvAz9KKX1c1qTIZXQ+QRCRl9GhpB/9eT4sJc3+Jq9NGyLKkW/7gB9VrO2ssTzRxNkwLcjiTfOOvs9emtIGViS/DEE4U9AN5Skq+5A8ly4lKl9x9fmrjKoRD27fI7SdkXoE8ZUq+XQdCaekphWikZJIbCZgHMIHpcNKYb+VsCSlIu5RaA8G8TOPoUB/VpBu0gRVck6z+UZgSbZTsr5+KSMmkNrr5yQT2XCijMkoStsNHSkEJAnXn3+OECL39x9x6cIea5tjwn6PpKCVA4EQem7duU0fOkII6jElqwzbnrQGGsPNdp/ses24Jqdy4CkbgAQluM7oJmUeSdaGsFvSM+xGdfea8ERQfGjhWYp4p5ONl6FXMpEhjmWzxDmNF1eoJwxXgZZfJZXzy0FZPtX87pS8PnPvihqxsi49k/VNmuWc2LSarOwGurp+37Nc9rRuqbkH9O/oVe0oIaytrbG7vcf29i7jcQ2inZd7e7tcu/Y8D/f3uXXvNleuXuTGC9f44U9/xO9/85u8+/6P+azXn6dE+E/RwaOXRORj4L9OKf1j4N/j6VAA4FeB/1YUe0TgH6SUjv6sY+grWbIGYgrqwUQ9WBcCtfWt55KWIktdKLLy4CQqys4OWlWAB2KJlvxcYc55r29MvcZYkhKT0ZjPXbvOzuY2EuD+x/cJyyU+obP4opFSeq3/h7xyikezdZ9jdKekl7Kw89jwRFFXVmNlGyBnBkQZjqUc+kmqcBZHjYnsVMWSitHmBUin1sZ5VbX1taPrOzW0TlguGztnHfC6u7FD5SueHB5x5dIFutBxOj2j6VttZAlDiNRXQtvMyRu1cDYGB2/cjuF3+WuUTGOWMoE6h1o5X1aQUw7BbL6iSr6ngSFq9yKtHMM7pYQ7EdVoTBBDom0DfYpgswoSELtOk3hBkZGxUuyzUmGUFk+fkQf5WWH5FjS35czgisc5T5UqxpMJG5ubHDzuaPsOl9ErOSTSRjJ8RTUaKR+lbUtbsjrHissXr3Bhd4/pbMr+0QHT8xkbmxsslnNC7Dk8PuCbf/Rtjk8P2dvZ4b0f/YSPP95nY339M3fen6c68Pc/4/f//qf87p+h48r+379WsZWtJHXMiRrlamehyEwRzclAF6Im9/LDsa85jsZIRdGhPIFsymPEVTUxmFZ2VUHX0bc9T54c46Vmc3uHavSYYMQQCYFS4kvGUrOGn1J9zAskZ4CdGZoYtO9A0ooHMY9dWYyf9LqHbkqKgXF9IhoVd0AYqihT+BIyHDvnJCSAVELtPaPxmNFohPfOEkot1WhkmveRG6+8wLWrVxnVIzY21nn+yhUOjo9Ztg13790D74k+WXJTVrz2yuFlOO9cNcehmz6poRLQeQAkI4NRGJMl25/ZhWYA1Ak7NOwTct4mJ19DgR+6mSvbQE40J9SmQNdqt52YIlUsKCOV+63LbwUP/IxRE8p83GROyTgQqTJ+VgzUa2Oev/w842pM23Wcnp/S2SAS1UOkyNcXCXg0TEveKQM6WGOcCJubWzx/9Tm6tmf/wSOmJ8d0IdHHxP2HD9na3OLDjz7iycExx3/0p8zmc+azBdvbW2xvbX3m1nsmGIOQb7T6mIL2jK8/8iYMYaO+SmXFYGWw9tZS0rMEntj/XbQykJXoxBJmkiC1PdHKhepFPe1yyYP9hzjnOD89p5svFUlAWfBljYRoIp+rcekKnddnSDKsmkJFdgMSIMbCkMvJUdBMePSo9l1IthFWoY9drrDinViJAeznPtCa16/rinpckxIsq4ZRPSI0AQI0bcfmxgZt19F2gYOjE06n5/pcvAcica0i5NqC7ZpsAFfPo7ATV8/JblG5VzFZmCBP/60tfGdMptJJaZs8DyDJ+Q8XVMykHlV0bY8TwaWqkIaitXBnA+ByDifHkjmhuhJ65RJrOf9VC1cMnRC9dRvmHEWV4R1sb26SUuLk7Ijp4txUkgyxiZWHrXLlQsL1Pd1MM53OO6p6TEiBGDpq51kuG86nM9qmQfoO5yqqqsa7ip988AHf/d736frAcztX6bvIQhqmZ2d8dPYzufnyekaMQN64kBNIKT8kL4zqim7ZQTDhCbIHUC075YjnRJB2mWVjkvdf7pnPXPMMxek7G/kElRPc2oTWEmDHxyfMT8+Ifa95gxifKlXpQlCrlNfHavUiIwAxrkIZ1Z1prDmXAKaEm4qUVq5+OIHKVdob4bIxsz/1mgh8CnloFgq3OsrdJfAOV1dMxhO2tza4cuUS57Mpy4+XdG1LbHp8NcKJcHRyorTWjx9wenqCrx2LprHrSqqqFNKg0GP/fYIMuHJ8+GMfAAAgAElEQVSjVh5EzoskVDAlJft1KuSonEhVzyvWiCRaWrMkXd6A2TPHCJd3L3D50gWOT09plw2LRUPsOkIf6EkWo6cVFmf24NmID5b1KU5IHBK6+VoUyQykr1RBn5wm7pxXleYQ+PjBPUJMLBdLQuyt63FoGHN9ItROy7k9uNzRmmA8mrBz6TJd33N8eMB8OuPxwRP6zgaoVCOqWicl933Lrdt3OTw8QSp4oXqe7Z1NptMzFsfnxPT/B6FRUuFga1onkePdZdeZ7iDg9IEUBmrCRBcgd4plFeLCEpRC5CxeKHPwpbeEU9CMsI8KW7um4eTJE036We1/yDukEmpIXGW72UETZDkzWJlVkFB2mAPxyoLMENObvFdeaEXfz2bT5bhQehuXVmLtzG3Qe+WS4MYjVcSJURNeAhsbG4zGE0LXszae8MrNl7i//4AHjx+xmC/x3nHlykXeePVlKu85n8149OSAu7fuMNlaI6RINM2DaP3ysoqA4Cm+RVFI+uSDLrt74AU4cwIpDqFAnrybKn0HIScYh3sEupnBEJU4nFRsr28Rxhs07ROWsyUh9IWure+1Sg+UvMBT4ejq9ysvF3O/wmAMYLgP0aotHuVhpJg4PdPOvRTRa3A5pFBytXQ29ajWvEwwi+S9Z2Nnh2vXnsd5JZz0oWd9bY2l9KytbxInawXBfPDRLWazOZO1CYvljOlsRlVXzJdTQrRBNeFTLopnxgiYCguUDawS04mUIvOmY72qmYxr2mVbmj4QrEwHRRIb2x9xcNkurcRsbiAOqWdOBc7S9YTpzDLHSdWHcnWBuLLYh+OALaQVeF4IRblP3uefwcVYzmsoKUFeTC4MRofezrfvixHzpgOYMpQe/lxZgbUmlkhW605Q+Yq9vQusr61x/+7HzM5nLBYNs4VSUEdVzY3r1/nqF7/A85ev0LQtN1+4TgiRjz76iNPZucbz5qHIzmpls7g4UHbBQIn/9P1UGpbMpQ6bWg1IFlXJtybGhOu1/Btri39yHiKh4qIRurbn7t371idQsVw2xJiFSCzWto2L1xxMtHPPuaN8PoIMBj3XAlKCKCXpqs9wOPMEpU05+IjUHsFpn4ldU4yxvJ9s2M0YZIghdc365hYXLl/iuatX2NvZBnpm8znPXXmOBw+fMJ1O8VVNIhJT4PDokBAD9bjG15scnx7T9R1d6EpL92e9ng0jYPDMAdGr1FRvZBAhEbqexjs2Nif0bU+MgZyTylJbq+2zGNQiDV5qiPRWjmmbyMJMQw/2edmglJKVfkCyBVDC7VWD5FyZD5CSqgU/leDLXH37zE/O8xsKBKKL1MICCdGYiGoIXEj0Tkz4Y+UA3pPE0S4bVesJWs7rxHF2dErcTYQ+sFwuuXv/Y+bNnLEf0YeO2tVcu/o8m+vrMJvSHh8zX86RkTMSkdXaQxj6MvxTt1NzF0lKS7hutOHfwYxFknKfyMjNEhs5FyCJQm3OFZI8S1GihiI5P0By7O3tUlWek4MjTf46Vzj6YgZzlVIsplCVUhicxqrFGvZkQZPln2SFs4AShHJTlyty9iqEKxaiJTDjlh1CwiWtJCQvuDxstKqYrK1zYWeXC7s7pBQZjWpevnGT2/fu0XUdXduwODshmBHZ2dvVVuf5lM3NDUQSB0cHzOZzDWHzOv2M17NhBFDnJ2BiChYa5FxBiKS+p96a4EYVoRkMhIRUhrj+zIWuQNX8M2bhM403x7IFfq4slIJKyCGEQKVJsZQ70BKlL6Cw9wVdGCu0YSpXHOlT5ihPkDEHlxOQKWZOqq04O9torcfiUsloK5FIvd94PCL2SkRqm6VKfzlH0zak83O60LNRb7K1ucGFvR36Zcf5wRn3Pt7n9775x7x083NcuXyR47NTHj5+iK8rQtcXzkIxiK4g0ZV7YG9xKmBadZFg/IVys22zC9GqPPaY/cq9zndpRegkZbQXhmPmd/vRiI2dHUIMWl5O0XxBbjnXc445jDP9gkwfzpOt3EqpN7+SE61imGFPkp5CQBrl6fBSb44n1h4/rtUGRctFyPB5Dh1P711Ns5gbfTyVkDb2ur7niyXHJ3dJMfHqSzdZNi0/+fAjnjzep1nMlA3uHIvxiMV4xGw648LeBZquoet7nXmJhlu+qmi6T6cOPxNGQLCF1PRI7bX054A4xPIF2lee1DDEw6RCu8XeJ2RO+vD5eQGuOPnB0q+eSBoaXKK3spR9LRDX6ruq6WceqmyQvEuyMRgWekIXXsJpRts49TFfTvnTT6APoSS0yNnllcVNTDjvGY1HvHzzJpcvXkREODw+4smTA+aLhqquGY/HEBNt23F2NmNtMqHpemRUU49rDo+P9fxSpKocIQYu7Owwnc2VT5CSHt/q+pGAdPn8KDkXhCKwUQXdsZlIlby3ZGBS0lG5Btv0WMiEPNUZmMeq5QqBVELtPGuTDUQ80/lMz8uB5lGyQdLcC0k3udgNHURrylLQlWaGIDoUTYg3065lyVhp2VHXjjU1dVHl2r0iQUZKyvJeiH20FWxeJis7OVG5M+v7KGux7ehi4uTkBGfSYrvbO1y5dJnNzU2mszkff/yxVsREP3l2fk47ndGh66BpG9Yma+zt7FJ5r+PMvPB4utrsO7yeCSOQUqLvgyrsNAEJYjoA1tRhhL5l1w+JqNV4mMGaq0EZPOTq+zLpZgXrmyw0PLXrLCPsUiSMKiWAhGCbXUU2C2U9r3rbqIgpGGHePYt7pAE5lN0rqfAKEoIjKlS2hhfXayiAMenyqRchEMnhsWPv4kV29/YYT9a4cuUKTdOCOC7uXeS9H/6Y2XzO+vo69Uhr1iEEprMps/mCmBJt27K1ucGli3tcf+4K9/cDfZ9ANBnlvaNte5axURZgsNFwK0gpIxpnWDlnz8Vi+BICrFZF3IDEYk762XOTkK83W0N9nx9XTEZjtta32Nvd4/DJIcdPDnAjr0NdvXp6n/tDyvPW/2lIYqgtd3c6Tb6KLaLkNbzTXEdcSVJSwscEA1+kcrr5RRiNa/quL0xSYtJJRhHjSCSSCzRNX5xHYsiV0PdMz06p64pXX7rJlSuXdHDphT1+/itfpGnmfO+99zibTtWA9IGWHpmMmC3m3Lj2OV66eYPrzz3P+tqEb/7JH/OD99/7zP33TBgB0Pgvy1m5NulUWVFvmyqNA0NI4LTUFbteyRZALhuBPI0KPuUlUZV+Vwd16ubKlF7zQJWHPuC8o18Vx8tfysLVn51Z+KdySSX8yD9nWGzoJQ6UYDGufDJ0oF4zlQ1UFjeGPKKN1XI6xWZza5vxaMLpyZT9/QPOzs95uL/P1tYWi2VD23QsF0pU8d4znc0Zj2q2NjdZnJ6zPDnh8XjMjevXEHE0XUcMkZPzU3xVMVkbUVcVS2mIfdRL6Q2luXzvUhkNn9umk8PoyflmK2vTgcbpbvDyrKK6zMfI3jlfdxPZ3NnAiaNpOg6Pj1lMZ9B02sAUwYdItAqAX8F6ubwbnW3IpwEkg+SchlcSUtGZKMpOaUCu+bn2Y23RruwaY4w473XAalJOQjKtyAxRgyW9xfJCyZS0XM4PtS2Ls3MWiyVVXRFiYlTX3HjhGr8Uvk4CfvrRLY6ODumblmTMyL5tuLi3w9e//CUu7l3g8ZNHvPbyy9y5e4/l6aeTd58NI5AG6xoqh+8VXnknBCWIEQViikzGIypxzKdzghvKRYJZ/bTieRji1tXQYCXHg7N5h+UfRo5MVY21xq6F/JMnIWXPPzh0vQxBk1Ei5T3DhtcQp2TE0b9zRYUYG5zq1PNUVpcu9fhcUkv40Yj1jQ3W19fY2dpmc2OTZdPx4OFDltMZ8+k5m1tbzGYLjk9O6ZoGEdGZfyKsr68BcHJ6zvx8Slw2uD5wfnDA3dv3aNuOx0dP6KPOGAxty2Q8KiXcPHwkOXQIKxS0m/fcALXlKXSWyrU+vQkLXLewQO9FBKe9JCHp9xtb65Bg2XSMR57Tkyld0+BXavaSdMJzqIS+ooiLgn12ZejDNB/FrilWqmhUQq8U7RnZZOQVA1VQmf1M0snBIUa6rqfynkpEH5+FbAqTHClYfMJKqdJuXPAaKrgu4Bw0bcP+o0f0fcdkVLO1sc7p2TnzxYLdnR2atuUsnpBCJDQdrov8+Mc/5ZWXXubi3kUePnrEbD5lsrb26XuPZ8UIwBAvicZdWRbKt1ETvbUnVUnZfbVjsrPGvOm0MUSkqLR47WIxhJBIfdC6tpUch6TSyoAL74iVV1pvNvMp8xEGhdoSt6WBMmvAn6ip4GHxpzQkmrxTi2GqNDn5uJqEzPcgSRymC+XfOwGnUuk4x8s3bnL50mVGo5orly5yfjbjB+//iNMnB/pWEtvbm5xOz2mPl3oNztF3rUqux0QfAtPzM5YnJ6plAMSu5cMf/4j7j/aRGvq+o64rNtZVv+7o+BiWHW7kNVCzWjeZh5INb8JGp1u7bi51Fu5ANqIDrMq2On+jUl167dFCatcnnnvhKtvbW3x46y4x9iQXSbXQe4frrKcj6TPzJk8fzdu6SEnW/gxKc77oHKrPSOSZi4XUmBGbJJvMtIIyirETYt9rnscp9TdVJomWqcm2djJpaVSPcJOKbqZyYwFHqDzLrmP/8RO2NrdYX1vj8qWL3Lh+jf0nj1ksGk7Ozui63shVQmo76rUNxuMJ65MJ25vr1FXNTz+8zWwx/cyt9+wYAS/IyNtmFp3ZZ5ZTUP53SImuD8SUqGvP+trY2mFVoqzvo8aVyboEA7ja4yc1k6oihUS3aOjajoRZ3crUYqJC/sI8FCsxoVyCpzL9WPkPq0JVriS1gIIAipcwaJuNkEvp6ZbnqHDfEYuYxmpC8NKly1y6cIGDwyPaticmwYsjBLh7/xGP9h9ztP8I13eIr1g2LWdnU6ZnSnkmRkII+PEEJ8L52SmL5Zy+aYaciZ1z37acnx6zdWmXuqpoZi2xDtR1rTFzjKR2KN54MR78yit3A0r27Ni1W3gHKxvCjq170YyDhVWu8ka0SvhOVagPDw5t4CsspkuQQF07OnTEuG/i4KGTEbIyi1cwmXfKsWMlhmqEmAKD0Ih1fWbWt2WGfTswGxFwSWzMuRD6RGcluT5GKnvu3ukgkU4ik8mIPnX03VCe6kNAQiCGQFWPGK2vEYwNW1eVTjqKkdPzMxbNHi/dvMFbb7zOt/70T1ku5oQ+4Jznjbe+yJtvvsmo8lTes1guqEfKr3GyUs/9xOvZMAJCycCnEKhqj6tc0W6LUXBofKY6fpF52xJDVIGQlEgh2oOiwLO8EiQ62r7HOcGNPb7ytG2Dr6qiykMC8VWB616E4JxBRkpcqF5aY8qcpfddRKXEnVU17DxsPoKiTc2MpxSK6o4ESoLJATFoN1lJAjo9tzBrufz6ZRway5+enDE7m1KNxhwfHdMtFkjXmDZAz9mTJ5wdHRH6XsdmOSHiCIsFaaZhVNvqZtRIKINRy1C0gbOTU1wQpOuYxsiia0hZrNJQjGB5AJLyGMyr5tKqYFiXpLr9Iasb5ZjaUJbdI5UKMwSR42YcTsAnZUOenZ4zb1vGWU5tadn3mhXhESl1fLfClpWQ32OIxKm8WKGXroYnaKo2j4UrLNNKKENqM4qw3FTEdAONxtyHzK8QHIpEuqaxNSjEAISoBgF0yvPGBuP1MeB4/uoVvvj5N9jb0TmCH99/yMnJKU2rz8HlHFMtrK9v8jf/zb/B1770ZU5Ojzg+PmbZtty6c4eP7txhpQPiZ17PhhFAvWWeotUvg6Fo3bBdH0gpMR5VINDFYKQedOcwbHiwttSESTkBKZBECCJ0LlJ7x2h9Qh+itRAzwHP05mZYWwRIRQvjmhkHnBQdwezxJU/2yQlKGxiSnHobHXRKkRvTP7MFCGWQRU4nZJg8nc34yU8+YjqbQ9MQFnOCQPKeWHlGaxWRntSpPHkMQQ3kEK/gYygzCBwgnZTR366Q8RkqKIu+IGUJga5Z6kaMmnmPtRiSck95dbEynay0yZZriqxMclrNGxjc7hKxojTTlFkRCJ0XTb5Kou9bquQ1b9KBWwRcpwNJVuP04IXooylW6+9S5i0kQ4259GjnkZKUdRNdLLmD5DQvEGp7v2lXeK/iH501pWkjmDmhGEmV02ip63U4SVWzsbbOYrZQkRxJlvfwhEYR10s3bmq/TNdz5949Dg6PuHTxIt57ZotDHj56hBPH3u4ex6fHrE3GfPWLX+WXfuHn2Nncpq6Fj27d4oc/3edPf/AOZwfHpefm017PhhEwKJo3lwAxRFLoVLY7aRPReFRzslgYVCLjNFbaCvVLmTxkuYFMX5WEeCGQqCvROW1LExhVTSmLv42wZBY6OSXixKglPKI1sjh52nkw8NEz9AeUmBJTgck5znXWzlo+xJ5Tzpb73CzUdhw+eEgiUcd8To4ogdFkzM3P3WAxnfHg7n2CeVGdLJTHm1PITkXsNIc9dl+eupDs5fRIFnM6YmWMxR5cq4YgulRIQvn8S1usYGKsaTUqKu9bTQxGIx+lkqCz5+oS65Mx9fYG52czUtuTJNEsO/JwWULCd3qckuRNOenrTOfAEJ/F9GVc2YrmQ36AMZoaVT+ESip8wlDiTFC0E7uArx1eJYzo2zDkOHLnqzmpzBUZjcZ0tr6ccyrqemGbn//q13j7869z684dvvuDd5nOFogI169NOTo54Wx6zt72Ll9++3UeHx0yHq1x9eIViJHbd27x1pufp/KO9fUJP/noI86nJ4w2R/RN/4z3Dhh5JBo9NokgRsrIzyiS6LpA7Adg40w/PybtsCusG4Pkxcp729SJIivY9oH1cc3m1gbT0ylB8at6c3FIr8qwsRIQpzmDEEsLqLFV1WgFjZWBgT1XhqZSugaznFasdddoTZ2VDbhire3bZHAzesqiDF7zIL4HukDX97QxkSZjzTy3neUg9N46sJZrGaBw9sRRN6BjxRasnEbvB9IOOUbOk35C1Kx57u33Ujy5YOcnGXbbM8tZ+pWQgowMzFD5LjdJCeuTNZ67epUvvvEmP/7oA37y4W36rrOkbqLAK8Xjuh5qRYN1Zze28rg2lHuuVzJ0M5Y1aIgNEjQq/aU6FM6o0knDAdQ+eWdj20UTf7V3tATL88RiMKkG6rU0HYv5kqoasbOzy7iuGY0nfO6553nrjdd589WXufbcVaaz81LNadqWk9NzvvyFL3H5wgXWJiMWzYLj4xM8nuefu85iueT7P3ifR48fc+/+x3x0+za37t2jaTsm4xF9CCb897Ovv+jwkf8G+I+AJ/a2/zKl9Jv2b/8F8B+gduc/Syn9X3+OY2hc7xxiG1sEgnlKQQ1E9FEta17IZlXTCoxW2CnF4kuJy4s1sQUZmDXovEAE35pQhUuk0GlXoYETEePL6weZMrJpxWWtAvPoZRy2oJnijBZSLC2yro3FgKgghWkhYPmBpxxzhqtZnmNoafUx0bcdD/b3abtOE3d9KByI/Jl2A0pSs9BxoUh8F0Av5ZArIVHKU9KBRPSoypKXUjXR61U6dmYHRlGZtZzziW4gZyU35AYKSQbInaM4YX1tnauXrnBh5wKXLlxmNl9wb3+fs7O+XJTrI6GyZ54NSxwqM4NhG7y6npt9guRwTdGON/QYLeeQcqLWjMdTRiwTfez7NoYycUpkQCI48G0szyPMO2S9Yn17nS+//TYv3biB93BhZ5fJZIJzjrquuXrlMtPZjO1ql1/5xV/m3/k7f5vrz13l4OiA0ahmMprwL7/9bS5f3qOqatYn67z7/o/4/rvvsr6xzmQ8Zm2ywcb6OrPZ48/cf3+h4SP2+h9SSv/d6i9E5C1Uduxt4BrwWyLyekrpM4DIU3+bV5luJmN75UVceT+0BuSFEjLryybq5Cdii7m0meZSvEDm98coxNATRBhtjKnbnn7RFQ+e14xvhhg2VGIzBHWR5dpzOXT+dmUTFY9g9e+cvIze47ze/th1VN0KkrCvObmlaYOkCKDkE6yrsA200ynUlVJ9k2g4lNtbZdh0OdeQPx/r/BPjW+Rr7i35WSBt5jrk3+XNawSX6C1XZow4vQcC3psUuo73Fj9UEnKcnvsCVFzDGbFGr685mfK4iXip+c7332X/cH/QZhF9FlGMV2pJMskt58kRbEJ1ipaJz4ikwA+DhTnZF1GxFMtbhOz1szURp/3+CeLImXaNGYJK5b8z5wALFdRIDuskWljSz5ekC3Dx0gVeeflFxnWlaCQlfucP/pB33n8fUmI2X/L5N17k+vWrjMY1125c57W3XzP2ceJf/skf8Zv/929x88Z1Lu7ucD6bU48rXn/tFV5/5TW+94N32NvZ4cNbd/jBd7/9sxuPv+TwkU95/R3gfzHV4Vsi8gHwC8A3/9XHyN4gkgJPiTWQwFWOtfGI89mi/I7MJQkoj914/Eo1UAjvREU/C0ftqdjXvk+J6COj7XX69nxIpq0iC3ur77M2ACtxHkNKIv8+KqrR8eZ6DLELHWLRSPp/qHuzWMuyNL/rt9ba+5xz5xtxY8q5xq6s6qrqobobbAw2tjxhEGpheOABGb9gCfMCEmDBE5afEEg8+QEhAQIkLLWEB2G37TZGHlTI7u6q6qyqrCGHiMzIGO+NO51x77U+Hr7vW3vf6MzqoroxwZYy495zz9ln77XXN/+//1c6/Yqi9exoHkHf2mYbm2S7Z0n+HQNasekgx4KlC6web5vTZiDW1mrXqmHoBHRV639WpiQDJ6VBQGoTlRgcWkaeS/B/zWdJyqisyoAK5CIxLH0YlIHSbuuXxCKEXvfD+uKCu3c/IHcdXew08ViFMgy4gGLCFgK0hvYsvgZqYCQCrWJQKjt0Mfaigo6oN4FNbSRMkrYxbxQmncfSYrgQX7RiuRM2xQbBYH0jUisUTp8OEPrMsyfHfP2f/BPOz0753Kff4DNvvMFiseD+w0fcf/iY2zdv88XPv8nO9hbvvP8+X/7im4RG28Xnp2fMplO+9pWf4d69+6QQOT455eT0lPl8ydvf+x4v377DZ994g9l0VnkNPu743eQE/nwI4d9B6cT/IxF5BrwCfH30ng/ttd92/LbhI+6Q1uyRauk2JvZ3t1iutCTomdsgaoFAf06MCCvNbc01sSN4mbRqZyCkhBTtW2BbmO1vszifX4EU1+v1DV8GS0947j1+2UWFvJKJhMG1FLtftSAm3WEwTu49FLfchlZLHZRG4/rxHHLnIIjrggfFxYA5tZ06UL0Z1yfVIJbBNfd7dpfeOQuCCZCTfgQZuu6czTnZ15dJrA1V9Z4CtbTqsbhr0GzhQTSUpyvZgNF1BejWc33PCJ5c0YsyeBQUdePjKlNaFTpPFyjiMhDWBr8OgWC9/GJzJ2rEGE2obaiqAsZUmEsaFJh6m3ZfIup5tomh6U0rHMUQo5U/wrzC6zdu8Ad+6fdxdO2Q/+Mf/iP+1t/7+9y6ccR63XHr5k2uXz+gbRp2d7aZtVt8/4fv8tprL/Plr36FUoSbN27xb/7yL7O1NeOD+3dpUsOjJyd8++3v89GDj/iVv/rXee2VV1gsFzx9+pRPOn5SJfCXgb9oy/YXgf8K+LP/T07w/PARQDW715h7bducbE/pup7FShlycWYdUGvpGe8yCJS4SvHNEYNRcdtDK8GytkOL58V8xdZsymR3Sj5fMxAGUDfH2EHwF8Z1/upmM7jMV9qbBSTFykyEv983BoNFrbRVYfh46CEkG8wZB0ah3Iwgs0i15LWXv4xcbvt9PIk4eFLMrsOvy+9XRn8PGcajxmMWcjDegDZaCFOGuH+4+tFiBn9Uw7rYW3KI0GguQcZj3F1TmuBGUbfacQfFSrAxRiTl+lzGCVZf99gLxeDB3sHlOIfYaV4DaxwaN0bpFk1ILtWoKGxScyYSLERLgWL5LB9rp89ADAcCaXeXnZ1tHj15yGQS+Oe+9vPc++BDTs/PWK50nuX52ZxbN29x5+ZL/Kk/8cf43Oc/S4pw9vSkVhhu377Nn/yjf5z/6a/8FZ6dPuOrX3qTn/+Zr7Jer/jmt9/m6ckJb7/9PZ2g/AnHT6QERORRfbQh/LfA37Bf7wOvjd76qr32Y54YsKhdmoikwLrP7E2mtClqQ4Y1GRX7e8gCvWZkQ03C6bNRa+ZxHSCmLKIKmT5MddH6LFwsV8zaCZPDHdZnl0N8/pwyYCyY9rorB1cEAUa0YvqGaIqNIMP7ykhQxGyl4+xNcF1Qq+vcgzRSP1g3agyj0GrURx81w+2hR/CRaGNBEa06JC9/+X011sDlXtVIUYhYjiSANI1pwh4HAYd6cr9O51uQ6lFJdDyRWvLGZjK4t5eyyVkNw1Spl64oRyHmnflE5SBI4z4HkKQ+e6yCFAQFn3UM05FG+R0PywCDO4PPvBQbrgIq7B5GiF8bUPJQmiYFGCWCg1UsAoEUIx9+9JAfvvs+P/W5T3P9+iGrbo2EyP7uPndu3eaf/4Wv8Zk33uD2rSOu3zhkZ2+Hk8fHfPThA5qmZbY1480v/BT/4Z//Dzg/O2OzWXJ88owQAp967Q2+9d3v8K1vfJP56hNKA/yESiCE8JKIPLBffxl4y37+a8D/EkL4r9HE4OeBj89GXD1jxYnrA3G3WOhC5mKzZtYkZN1RDAQSSrHyVhjcaXN9iZZHt43lCK8q1ULtGAtOYBJUm/ddz87uFjnP6OYbJQexTVF5/8cSz1VXuv5Tv0fdaWfjSblUl75u9h6tT7urKcHAKmah8BjTuP2wNlv/GktEPbekg7tsv/rorUH49Trc6SmJisGP5soqB0MktApdJQVCZ25yYwF+jISuv6IwS4wkE9LRxrFLGxaqDhSR4T5CsCSoBIVn95DsRnIJSKvoUacuFxtWE7uuTigWT1SMH1hMWt70kW2ioY/2Bhg4qW20FNvlGqJubW0TUmC1XpGNsASC4YFGisiVcatDbkrOhK4MpDMJpMNmMWaWqyUwY2t7i0dPjnn/7n36PrOzs8sXPnObXxl1ZHoAACAASURBVPraz/MLP/tVXnv1ZWY720ymEwD2Dva5Nl+Sc6GUwuXlnNsv3+SlV26T1x2L5ZIP798nl8zT02Ou3bjO1v4+D+/98GOl7ycaPgL8oRDCz9rqvg/8ewAi8u0Qwl8BvoO2lfz7P05lwK2YJtj0YY+RZilGps2E5XqjMFgXfhzS69I/WEf/IRYn8vB6t79PKny3iHbt+c+rTUecNIRVrxN5Rpda9fjIza+QUldGdk8VVqoLWZNtScQEfWR1iyfW3I1Xy+Mapbrz9rla8nSRsu+Lliirw0yqNyQ1OTe+3mJKs3oltkTaihuIQYibrLV3K3dJGwdmpmLCEv371PPxeH4cDfltVWZpG9KiOcZQcxOSkvXwCzlROR0lWHy+8nKb3pegSUj3pKLdav3eAs7kHJqkUGERQvDJ1JY/KUW9iBSh0yagQuDo+nVeefk2Hz5+wEcPH9uMRyEPaRibSxkqLbzaHMW7KPlEqdWsnBpE4PGTx+zu7jKdTNmaTSkCm03Hzu4uRzcOefXVO2zvzFisVmzt79G0TmgQ2N7eYzprWSyWtDESmkRIkaZJ7O/MiA8f8FvfeYvv/eAdvvTmF3h8fPyTK4FPGD7y3/2I9/8l4C/9Tud97kN4eWQI5uzIMJ025JLpi6iH1Rke3uItHX45WHah4DMGgEqAKUmTN162sXBQhcIYQiQXVl1HGxtmO1MWZ4vB6AsDsqzG9fZ/GQRoXISoSmokeH6q6sbXF+xv3qxkb4rjqgShgnNcv6RRpaBEgwHbOX00elUodmHVKbKcShxH7/aZGGqKS0tvCXVnDdMRNa0yPDKhZv+9s3CsGL16K+OwTYTsPP3Z+ROsZIk945EiHvM26ppYNsYrL8XRiXpHtWJhpV2xZygGJtPwS72gkFFvU4QQInHSEjYd3XLD7Zu3mUxmPHt6xmK1IPZCg5BTpKRIarSTNduEqtJlUps0Kej7orfrDVAWS9abxGq+JMWoTUMbJXvpNx0nJ6ecnV3wwf2H3Lp1k8Mb12rXZds2XLut/QRbu1sszi45e/qM6daM6WxGmiTe/+gev/r3/k/2drZ5+c4dTk4/nlUIXhTEIFRmX48x9UXrECyZaUiaHS7K066xrVimNw6KBBdsGbnJriDQuv2okywgVagIo4RdhDRpKZtWcwe9DvQUFChTHYrx5crgEoZBpqpXM64qlJGFVuVggzuLbZagXozHqqk2wozacssQotSvC9Yv7yFM/R6ueBaICnZTbA1HlQt3pa8AmDLkjfZNBBFKC/R9DXUcJYlYxj0N8xaDe2LunYyVvFC5EoM/x9GQ2OChoivMECo+YewRSqBei7sXEXQ93akKKJGHsQ05i1BIEbL2oHgbcigZ1oKUniZEzs6X3L33Ed18Q1p7mCM0pdBPJ8SYEOnNGdKfcy7aYZio3paAEta0SdmCpdC2E1KIatXbCTdv3OGl2y8TU0PTTAgkHn30hNwLewe7JJvDgEDfa3v4kyePiAlu3bzB9u4W3/rWd7l77z7Hx0/puzWL1eZj5Q5eICWgjTgqLRWlpX+h3xRSE5mkxKYO89TseJN1pzkwxoEaiBNUjKxvoCoaj0Xdlw4wbDSBLmf2UqRpE72VwZoUmcxaIpoh7lYb8rrD7aUb2spQ44f5wUM8ri5r9DuOAWmUnZa+kHyQpcvuSBj9HoZY2ix1VQYWdgz2d1CGNqdwnCcoXl2xMEPLgcFmIPgXunIVm+uozRhe6cD+FjoMZq3r6tZVb0VqaODKDhh1HKIWOki9FzBhHCkAV4DqYfkCjZ6xjXePvSYIixG8OlGNZIjeJu7UY62uRu089FhCMiHAxfk5b731bS4Xc8qmt7UbjEfIWd18UYq8JkT6tVCShSg5IBPUCeh1L5ciTKcTjo5ukFLSUi8NlMznP/spPvOZN2jbCYcHB1o23Kw5PXnG6bMT9g/22D84ZL1c8fTJU/b39kGEk+Mzcu6ZtA0f3v+Qi8tLnj5+QhKh/f/DGDKwrHZGqbYNoy4FcgSaRO6LbW5rH42h4uO9DTXapqq02DBsIKjAFwliDDbV5HAFFddlSinsWoly2m4xaRtSTFzMV5QQ2b+xz/JiyepiUV21qmj8e+3fcU5Rxj9EE0CrcDBCBQ4xchiovMbnGByYwe+Onhewl4pZzOTfZTG0LpslVq1WL4LkzEAXxlDqDBo31y46A2GNEyYKqdUSrucYwEI1hpxHseTfUCGw9fLOx9HhQ2cJg2EoKWjYQSTmbFTnrnwht3otoUBw7y7Ymgr4lKba0LVWNuVUo6hQ/x8irC4uWF1caoVFzDtBKtdk2vQI/ZAUna+snKrVlhIhrM3LKFAWK0qMlNSxv7fDp19Tgb//0UOenjyjmbRK8nrtkOs3Dtna3WL+ZMnx8QkPHj2inTR8+Utf4Oz8knffe5+jowMO968TU+ByPmdrOuX2zZfIfU+SQiToLM1POF4QJeCu5OAqDjTegZ3plE3f06M5AfsIQBXiCjYydzBUl9uFxv137SQs4hUJRt8ZddpL1vLT2XzJ7esHbM+mXC6WrBdrNuuevussviw0k0izPaVbbYZrwFxTu0ynuK4hge3KYlWOYCg77b+PlKQ9+mVkEbUj0BWN30sYrt8EF8CptH0dvcyorrd+v3P7VajvlbCBK+w3apq1axAgdpYotHJhjdEbUwTBugAZyoReDgwWLqjlB8d5BPHWZ9sHvkwyWlV332vMxVDydG1lIYG3akc3KHb/YxzHWIH54nmyNIoqEJEBPu0syB93+Olk9LueK47cLiBo5aVpJxweXeel27fZmk1ZrtakJrK/v0eMDfPFkgePHnC5fMZXv/IV3vjMa9y4dYOUZhyfPOPuvYdcXl7w4UeP+K3vvs21a7vcuXmbrdmMadtw42iXOzdv8vT+B0AkbF74cEDqxnYMOVGbe9qUIMF6sdGtY3h3STJYcXP9PdYz57VmvoEhY24uNyMlg2fbQ6yEEAHIm44Hx2daGt90dp7hqpeXS0IKzGZTpnHGcrkcLCgQYqCdtLSTBrKwWa7pR5yCgOYzkKGOn8too1qewL2McetyRdepoMWuVEHTv+v/aoMMQ5NSPaUL5khZDuxJw9p5AjXmQYo87JEkVyDFyuLjrd4ueYb2E23oai3c620+QvSRRiEM1yFjL2dY0+jZ+DbiIhnMJUp99ZGuSmOh4kbGoZnnoV1INTSzX7MgFqsMsPCqZV0HjUKcq4d7IXkSiE0DBM1txUQTJoSmYWtri93tPXZ29vneD7/J/QcP2dqe8vL8iJNnTym5cO+Djp3ZAdcPj5hut3zpK5+mX73B5eUlT5485aOHj3n05ISzswtuHt7k4MY1drZbfupzn+Ozn3mDb3/7LWuhLnzS8YIoAZSdJQ9LKQXaNrI1abmYL0w4gvWq64bOzVBWqhUxkSFzHxzjEYYNxejhefIwapLIGYpgyLZL1w+NIeIf1s8GFNXWpZ6trSlNbuk2veYcQmQybWmnEwRhMktIhG65ht6mIOMbWyiNueli0GBzqd16OaW6lw/d+/eBF3V/+vqF8b+DhwVa9hKC9TjoVN0rQuPniEFbq/1lMUXU6CuulMUTcmG0yGL3ECIR7SAUw9vnIkbeqc9KQEtsI3e8Dn6J5i0Vat8EITCbTun6TGoigjMCW7HeejdCctJY0xwh1Mx/EJ1UHMUTqEN+RhmpgwGQlKtgePbCyGEaKeVh/X0JQ0HpyMjESYNE2Du4xuHuIRcXl8TYcP3aNa5du87+wSH37t/nyeNj/sE/+jrz+Zw/8gf/Ze7cfIVA5MH9jwChbSbs7G6z3izI0nP92iHTyZR2ol2HX/u5L9FuTQhNS95kvvGN3+L9996hrUnh3368OEqggC+hW7NeCr1kJjGxKVnRvjESi2Z4U1+UNCIMQhKLYCBk9ZRt86hlkQqOcVRYqZ0vGhP7dbjSqI03lpBlJLzaYCPkvqfrEpPZpI4JbyctMQY2fV9DgThpmAFl05PXPZJL5eobNNngcnpyMTcD2u/K0SizkDAoryHhMPJaJNRYu5g0x6xuaTFqdzfBVdm1KnFj2KxCsGOFwsYCklGMhSsas/6xaN0yTFogKElqQKdJZaHk59YVt6pCHUzrYUYK+mwAiUJqJ0yaVseiNUW9q2hxehb6TY+kqESfpac8t/tDxUZHfHK0ktHq2vu4c8/tlHqN44SvKr2CJ7X9tVE4Y8aioElDSqBbd2zaDSk13Dg64vDwkLsf3udyviTFRIyR5WLDr//T32Bne8qf+uP/KiHA5cWS6WTK9taUvivcf/CAxXLNdDrl9VdfY9IkZrMZ2WQgdB1f/MJn+cN/6F/kV87PWJydw4s8gUhXetD2Lgd9n1kI7Ewm5K4osUcbkRKQLldG2RKw/m+q0XOj5GU7DxmHRJdbnqGU5XsloG7hmPBDsfOBsKm8YPXLJAvr1ZrGXP8AlCI6LKUUYkz09EymDXs7M8q2sFqsWc9X2rwSgvHNewwtSJOUWRlU8AyIgpSab0hNshBY6KESm+Bhk62Bj9/yo8KJg1q92GGZyzDkYkSIOdcKi/vLoQgxW8iVrePP1kRRdz6xxyoMq24IOWIg9GUA/4DNnwuQi80DDCa0Yfi/gM8BIGrINl+tdOyYx24O5Y0JJoncdwbfHXIKsWg4EmIwBibIPjY+6TpHU2LFQhDESFx83UQ9lRAiTnHnI+P9rgamZaz1OQCRawcHlBJ5dnbBpG15/PQp3/vBDzg9X5BiZHd3z/THkouLU77+j/8x1w732NufMZl+iuVmwdPzpyDw7bff5qVbd/jql7/Cp157nelswqQJPHrwgGtH19ne2mY22+Lf/tP/BvPVmr/xv/9NVqcf30n4YigBP3ynVosMJRS6kmlnLd0yK6pLRIeT1DzAc66+l8H8NVcMQsXNjxyPwROW4RJStjd5TN0kZYcZZakVxWfvKcJmtSEE7W130KluBCFvhPV6zWo6IbWJyaxllhLryyW564f5B/UzugMFHakVi1oUYiQ1SSGjRhWuIapt8GygK8IQCtR1hXHcrYm1YFkUjy90TJgz8YCuWQUTdQW6UunAHOno7pFIthBDX4qWuBtPgvbxWb72wSskWS15k4ekrs88lCZASIQm0Usmi5YSq7eHrlnvxhgsB+EXNkqcDjEWNSQMmO4sgwCbZQ+mnKuxCOYRleHc1fsCRTlKfYlIZGtrl5/78leYtDPeu3efk1Nt+337h+9w++Zt2kliMpmwvbWlZ8mF0+Mz/vpf+5vcf/SEP/aH/yjX9q9z/9FHBAoPHj5hNpkynUauHV2jnU7oFyveeutdjp8e8/kvvcn2/i6vvfoSX/3ym/zdv/trnH+C2L0YSsC84PDcyykY5XKM9Jue6D3gcXhnpRC74rba31xeLe7zTTt267wVtD7HcWydUDpq81QciypNVLc2aIZd0ohXvoj22lvpDTQWjiLQCStZIX2kTQ0H29uk/W0W8zX9prM4xVx2S2QmAk0IxADrIjqLMWCgGKk8+SRtoFLUmmmv4pv/qjasAKIQlFGnZrf08+SqwtR6myILI6BP5SBo1BvzROOQPBiekSoD/159fqaPtGRo+AMlFRlClSFxp+CaHARKP3h2dn1K/Ras/FvqcxwbBr19qeuqFt21PJY3Mq/DKiZq+csVSrZ6V12uoYyMvsQDr4ppCIEUG1669RK/+HM/z2ffeIN3733AX/vVX+PuBx8yn89Z7l4SQmJna0fDB7E5lf2G82cL3v72u7xy520+/cYbfONb3+L993/IxcWC+/fvc7k4J6XAweEhn339U0ynEx4/fUp+6y0ODw/4jW9+k7d/8H3kt0nXcLwYSkDGD8oNVmAyaQkBFusN0mfS6O9lbO1HlhlGPQhctfJDaOiNJ6G+p6IMxwISDPZtySa3osESezWMN4H38VyKVzBEg2WJJUQiOrk2B2VMPp8v2N/eZmdvi+UqsfahnykSCbQp0KYGYqBpkzLWEuhzIU2Dsi2hlj8Xg0QLTGIgZ9HBFEAwt1Vvy2CybopjGGD/MdA0DXHS0Hc9fddXbspQjPSEoQkJy9+IK8eRctY/KzlnMiyHqWt8JoEEtMvOwyD3VNIQkhQwRaxl25JifaAiBQkRnxl4pWHA249dsBkZCFFhjdEFVqj8DoHBnbdcjnsxV8LD8Z6LDlu38KroXspJ2bHLesW1/X3eeP013vzCT/Hyy3f4p9/4Ft99+/uU3JECvP7ap2knLSenT5hfPLO1KiTpOT9+zG/+5m9y78O73L/3Ifffe59us+HBhw/5zne+Q9dt2Nvf4w/8/t/Hv/CLv8hq3fHD997j9s0b/PW//Xf4zW++RZq1nyh+L4YSsEPQMlSJQtO2EKhwx/GQz+rOWschcDUx7TEsBtQwzT+0spqgR3MHZXBpfTilQlCLZvL9u9G/x/5qOS5aMklSGFGZ2Xcyhu+aW2/tzJu+Z75csb+7xd7OjCYF+pxpmkRKDZMmse57Lldr2qBTblNIGm70OupqZ3tCjJHFcs26V2zxpGnI2ZtchFACKQQmbSIlRat5TKyCr7kFzV9EJm2LFGG5WtF1PTmL0rODTRNSkz9mzfH1ec6hosRAH5TGu1pM88gqDsjd7pqvi5U1uBRB8zYDpNfDCQFqtt7KxP45n3/oEPGq9M3rjDFoWCUWXvqEqmSKLVY7MgCaZPCkvKzq+600ylUZOqu9FGwKMpB7loslD5885aXbz4DMdNog/YZ+0ZF390itcH7+mGdPHrG8uDBiUiHGQl4v+fDue9x77x3WyzX0PU2CvF5w94fvEMg8CBOOTy5ZzFdcv36dp8fHvPHqy7z73j2ePD1htVh8oty9MEqgbpACSCClRN/1mjAMYWD7sSOMP8NQ0gPqe4uV2qRutnDlwYJbR4srqwuPfq9QSTmHbOPw/QhKfkJGGm1nJaKDUHKpysRjUJ82VN3UENhsNlzOA7PZhKZtaCZNFY5V1zFfbchSyJtCFGhiJKMNKps+U4LOY1huOpbrjhigS5kUI23b0JqBS+ZNlKx4+BSuxsilCH1fkJA1B5MaYpPYalsmNpF4ve5Zrzaal4E6nsvZhRzMpOAkRfMlK0OOqyq6jqGGaTVEQUur6uKXGqtXkJTFyiFqGGJa4EooUqsUTnhaQwq7V3tvsayde1IKHBuUxBiFGYTaAUhi1MA18jb9u1KwhGsh9jpPsBBYrlbc/fBD+q5juVL0X8i6vzerOffuvsezxw9ZX1o53B2eACKZ5dnZsAXtflIoVgnTZOrx4yf8+q9/gy/+9BcplqxeLpZcnp+Tl7/HfAL/rx3i2PNAShE1bIYN8BjVtLKGAzIIZgy19u9FthKoM+vFN2sxbF7WDRBCsMSUx4tSs9egJUTnsMvJceuB5JYD80JFB014mk0sthzyEtWDrh6HJ51W6xWrriOFSDSW27zJmiUvhucP6Bg1sO43rQqcz5ekVST3uSo72RQ6oGkb2lbHWAmw3vRs1kZHHoP2xqfAutdRWHWCE+rdxBCYtC2TnRn7e9vM01pd8z7rcKSukFeb+hjwaoPlRSp5qQs7WC+/gQDKsEZAJYURy9iPB8via1eouY6hBIpfdD2069EgjS75o54IjR4yjl8cW3bnqhB/bhmtWqWoVQgLPSrK0xWAQ6kFZKJTtLBS6Pl8yb0P7rNablgsFnxw/yMkRvoClxeXrJZzNvO5njEOz0HvLwz2Jzz3t+pyFWK34cO777HpVhzduMXu9pT5ckm3Wg1I2485XiglUA2uSrOSgZsb74H7wGEvozg+Vne/mGuJdSMWkZEwRqV+cq3OYP0l+PnqV6llKq6ApMZ8+kF/o12DhIHizB5Z9ToYFEHNY9hROxhzQUo2tpuh2QhzdWGsUCx3Yem73mvwAesN0Pd0XaeoPKtYiKBj3pqGFAPTNCGkwMJmEg5YSz1PlsAyr+n7nv3tLSaTlv020cSGFKHbdKyWHeenc230cRccKhDJBVCRlDK6CUYhU4BmKBWCME74uiXX5K4JmekS13xBrCErBIXqYn0mjTZ7ecXpimzJyBMcPSwxFyIE31MonsCH0GZXzn4fpvRDoG2nTCcT7ty+xf7OASfHZ5xdXFIEjp+d8/prr3N4cMDdex/w+PEx6+UKNj2y7q50S9YHrlcyGJC6o+wFv1aBIIXNYsndd++yWG7Y2dlivlyakeMnHz7yCXMH/kvgXwM2wDvAvysip8ZK/F3ge/bxr4vIn/udvqPeXxi0r5fj6oirAD5YZOhIc3fNHkp9quOT2oYRc5pC0HOLVFCK44QVlivmgupnnI2nuLtqSZ/q4o5c0XqdY3RWTYhpgikUjElYKdJKdCS9kVlmcLUtPvij1BsYFKV9TglNfSPoGxQUZT/LYGodpZhzj6AEFH0u5L7gyYxg/e6u+BDoup7TyznbsykhBJomaxWhCNeP9si5MD+90O8nXFFWNQYIjkEYXO2APdOkAuYegCv0cd5lTANfB52GAQRV5YbRD0XxwrosYUgeBhdyA6aJ1OSmnsseaBk4KINhNCgjT9GNEZARtvd3+fxnP8/tmzeYTSZs7+xR+sI3v/02Hz18wNn5OZfzS2az67z8ysucnp7z9PiYzfwSum50F6aI6lSY8Z2Fq4q0Co07HUo7P18see/eB1yenQ+lzU844o/+M6BzB/7Ec6/9HeDLIvJV4PvAXxj97R0R+Vn778dTAMIwL44AIRKC0jNFg5U61t/3ZrSMsysB2+a1k5iqNMztditplFc+kyD4MFJCpauq5oGrVYggmmgsTbAZBGEQehgUy1gRQU1qEWSYRtTL8H1iVjiaorA1CQaRNj/EtluoNxgMOXnlX1sH/6d6UjUe0XXoSuFys2Gx3gzgnBBr23EdWRaBpHMXL5crLhYrnl3OOV0sOJsvEISX7hwx3d7CewArHsPyO75Ri6+X72fDXxBAcqEglVhUQUmeoZd6TXW/eI+DjznzEMwsouITCtJlayqToQDgTUeVXGbkmuEKx4wPmvWPvVKPx15srU2hm1cQU0PbTFkulrRNS9O2nF9ccOf2Dfb391it1pyfn/Pu+3d57+5d1psVr7/xGi+9fIcKGzUtV0oi05JDGj22MAJP2L/FFT2EZsLu0Q1mB/uEGFhv1jx8/ITVYjneER97/ERzB0Tkb49+/Trwp3+n8/zIo96XCk9jLlzpM0mEPgyQYMwNDGal64SfMPIKLNEjpjFjcKCoWQ1zj+qgDITGKJ8kDjF+sN+vrGA0YSzFZtCjrawjizFg/J2gwr7F8xNW/gpFzP0dFqI0qs2DTyzqZdQhqC4tjXkheQAkVYtbN42FSX7P49jEjpxztbDi3W6I1zWHN5vbrVlxfb23IbEn53M+/fJt7rx8gwePTjR7fWVW5HBt2oCk/Hti55WIjogrUhUzQRUBLqwBAwuJTjJKouQmSM2DlJSG2QP+3QVizSgPuQX1Nu3CCgNMGQghQpTqQU2mU3Z3dllezukWCz2LVw6KJztVSaxPz3n//JLTswvu3LrJYrVivV6zsz3j1s0jjk9POT09o20S88UcgMXiUsNAV9Boh6u4N2b3UQ9fU//VnJvJZMqnP/MpTs7OeHC/I6/XSDH6c09Of8Lxe5ET+LPA/zr6/dMhhN8EzoH/XET+wcd96Pm5AznpTccsTLYi3bqvI58GE4ZxBupngpeZRu3A7qKrG20vmZJpG6V70scf9GFrAwEle2JNKmuRf7c3lwTUgjuyD6g8Bh4C1E479zTKUBMPovF+iaJMQXYfXt6yy9dNFiJIMVi0+9B2EeYd5cEp0IYZ35S2wceEHwRv9KHG7C6pMhZ4lZAhFheMU0+1oYdLFCEGuFyv2XQdt24dUErh+NkFy8UKKUUFiUHelLJL0/pq+Ysp6sBIXw1W2O5bCUuyhROqCHw9qidWVHlpF6pbfD8f1oV4NaSQATABQJxM2N7ZUWNQCpPJlNQkYkiwWhOaBvpuyFGpTrM+CSHnTJ7OOL+Yszg/p/Qbnjx4wN7BAYv5gn7TMZ8vOTjI9F3Pg48esDo/J/S9rW0kNw1CUcAUw/oN6sblZ/y63VuCW7eOOJ+fc/r0GOl7g2LzI1TA71IJhBD+M5RN7n+2lx4Ar4vIcQjha8D/FkL4aRH5bYjFK3MHYrKtR51F2JdCY5vBIbNXgW++aTUpN44ZpVpO2+eWxPG43h9eqTkAGxDhVtOTZCbYdXZfAaIQ+wGcUBWAhxwGOa2kniMtHgqEBi0j2fuUPMQ3O0M5yvIBxS1bdPMzKrXVtuFY41vx6y+j9QhU3kCvkIwRfDJSalesjiXYxq6Qr72HCW2TiFKgFK5f34cQOW+ScucbZZfjIpqmoSDkLquSGCUSPREYcxm8Fr9Pu36KjTa3SdWIegzeWVoBUN6wJSAGax43Xwme7IUwaWmmE7pNDxKZtTNCjMQUidGaqALs7u/SXj/g/PFTNotFvbAChOmEEKA3XoZuuaB0a4II/XLJ6vwcRGi2d2naCSLKXHVxuSCuO/NWAtmmVuF4hxLruHo3fO75jclhAtAvVrz3zrtMtrdZLpfGkmR7WkZ75WOOn1gJhBD+DJow/CNiQZWNH1vbz78eQngH+Cl0StGPc1Km0ymrVUeF5NomudL0IlTrqskhal6gxrJuIWxD6SSaPNT9rWQQLCmlHHNQF00/BVip0bLLgCXlqP0HlcbM7mEwaX4+GQBLFi7kVvMevmHdU/BPB5vJV6I2F8UUKf1YQu1qLQxypeil1PHDr70UWf/k31WRvUXfd8WdDhhGAy0LemIMcG4+KcLOdMJs0uo47gjXr+1x7eiAZ8/O2RgCss+FLhc2fUe/XmutXwwabLiAKxUQ7wkJ5rH5LeG+vz7vmD1Ra7fb6DrEIjSj9l8nrPGcU/AbNGHqi2iyOGcuLy4opRBTIrYtL9++xe1bR/R9ZtNtmD87B5aAjbQPgYOj68QYOD+/4PrhAavldPZYBwAAIABJREFUhounmu1P0iO5pwjsTWd87lOvUyTz4f17OuLM9miJSqrqhDcSrRImViL3EXOWDPV9nCz0kpK5OH5Gs96Q8SSrGicJgrTxJ59K/HFHCOFPAP8x8AdFZDF6/SZwIiI5hPAZdO7Au7/zCYEiTGet/ViI/TD9xcuAdZjkqGIgg2cIGMQzhcFdiqGGFaHucdtIHv+i56ygEnf1pIBpZxEVxlDsO4Q6426oFTEYzbo5n3Ozc7D5f7ZHa2Zf3xZHM++G3LcYmCkavmH0XTFY/Ex1UwW7bw8zHEEZ3c0fxn0RNP+gTTgDiw5elShYtcKuZOxKCRzszGjbli4LfdchZEpIHF3bp8uZ+eWC5WpNv1qz6Xtr/LFEW1bhwxWon1pG5Vqv1QqItff6eDVPfFYlWmw+gQmoJ5vdm3hemYQglJLp+rUqWiJdr9OdUwhI33N6dsb29owmJU6enbFcrRhdLZISlxcLrh8d8vqrr/Lp11+h2/R849vf4/L8HFY9EhuO7rzEV376p/mlr32Vk9NnfPf736MU6xMNgb6NhDaSQkREriCgc9Lyp1dEcqMPvM51sEfVzGZ88QtvcnR0nbsf3uXeh/dZLZcw9vQ+5vhJ5w78BWAK/B2D1Hop8F8C/osQQqfbkj8nIie/03cgMJ1NaCYtl0t1o6LF0iH7hi4aG0uornxNxom5t14aMOEb9xWAWvRkjT8VyGIunDeEBWeblOHcEJTJiFAbXJShN9YQwHeY057HanVHoYn4HjTBN2CNTCLOexgKpE6z5GU0pFSN+kDbVbWIeSL49FurZdea+FhQCMNA07r0IwU5utYY0GRchtjnOk/UfQ0K7O9O2Z/N2PRF5zmagpDSUzoNAxYlM+971l1P8TbiPAi51txxw1yF2S79ihD7WrjCrHML7X6cQ7AmYE07SNDIRkaf9ft3XopgHptsMiEmJlszNpuek8dPWS2WtLMpi9UwfMTtBQJ5fskiRWIJvPf+BxwdHdGkROl7Ra+WwvbuDq++dofTiws2Xcd0NgWK0rShY51TijQhKuqzz3gfmIi1V0flaUg2hFf3tC5caQK3XnmZX/raz/L6qy9xcvoF/uqv/m2+//0fQIpD0vVjjt/TuQMi8ivAr/xO53z+CFEpus4Xq9pX7rGqu63RYjtnqBkn5BwC6pt+2NrUMd4AdRaBgCfKxlOFggzvdYYZvHYf1UXrY9A2Y1O/tU9h5EHoK7p7i7HbVLxD1O/Qa5WqNLI1reSk9OKxR9ul20DqFIbsBBh1B3ro4QI/Kk+KSCXgHCvC+nFbV0f5YfyKJWuXXjEKthjTqAxHpWybThpeunaNmKb0/YYmRiQkctGybs6Zy+WK5aZTUpStKUkgr9aKCdANUyHeXpmp/RijpB6MMhPByufFlUDQ3NHIctaBIGHAJQRLCNU+Diww8H3VREODaql1YcScUrISclwYG3E27R6kErNGEVZnZ6wvzjmOkY/uP6Bfrkh9V8uajx8/4RtvfYfPf+ZT7Gxv2TpnaBva6YQYoe9764uIOvQl6bpoHwimKMMVReh7T2LkYn7Jb7z1W/zw7vv0uefs7GwYzfe7UQL/LI4YApdLzShH796CUaltZPkNSgpcGbxZkrXBeswkVAXhKEwxa1dFodi/cbA+45h5sEJBXeKsf6jTkENVGcMmtOsVQvUi1Ezppcv4u9xM90XJLISqbBAIfYEUyS7MVvr092H5EESgCRSbchO9LfA5BeBrBlTykAEppxu6lk2D35a1+VZlKlCEG7s77O9ssTVrWa16QoiEmLStF3Vn15sN/XJNbFqdkLMzIwTolivlG4hJczFXsvhDZ597MCEM/SA5YQQvrm212jIe/x2seuK5EB9ME92BCgyIxIKOb2+qI6NH19n6KVGr52iqd1SwEELqHpWsYLR8eal7yvZiQOgvL9gsNyyWG6bTKbt7uzRbU0KEXDq63hW59nBAIFXUpNR9KCnqNO2cNVzNhaYvtF1h/vgpb3c9O3s7LJYLzk/PrIFKFJHKxx8vhBIoRZCuHxJ+9S81QqUmOCIVOjzG+EuMtk9t13gCwBo46hnNXUSgyeiU40bVbPDv9Bq+naa4e2lKQzAr4JbX427vZcevNQwZeNfEIldvS6gehVuNWo0QiJtSlQ4BYtQuQJ1xJ8qjJ4I4JbmA5KHE90n6X62Df3e0e3bU4PCeylzcRAVahciNgxmHu9tI6Vks5qTUICGSe+VjLKVwvlhyvljS9YWJFOiTckPMpiDQrTVpmDXIGcIQGVt8w9G7njI/3PeIU5j3TSRRNFFo+YKruaKhtDZ22urfBUIvlcKs3nsYXYdfX7Ic0UYrIh5yihkK3zfODl1DstKzXF7y6PEjzi81tyBSdHqxIRPdiIl5oF0Zsv8SNYmdmqQTjixUroNbAEpmdXzC4vxc6eGKciiSh9Dp444XQgmIeLlMxvKBu/UaGthCx2jWq/rd1Ck4wwmHM8SoHV0iZP9U0QYZx+D7E9ba8tXSmjYfCcpEDFu72/Srnm610pM5UKj4VjPlUXfdyH8fVRiwtJ8y8CjmobT1T0NjjVtDUx65mAttltJLbZEINqijWjTvszAPYqBRq/6LuvxJr0+ynm9IZorlKgZt0k4Srxwd0aaGUjK9wGzWkHNHaiJ9huVqw8ViRS6FTenpN5EgHVuzKXEypZlNKH1PNhrsMQ9BYYTzMImtCeGQ1Pq58IkKmv/dE66VEs1DowC5QXM1I+Vrhn4wJkJFhFZ4MxiexJqPiqggNihPoqEGQxj6HcTCjDDsCESE+++8w0cf3ufanZvMVwulRmPYHgXLU7u3aFsmxMRkOqFtWwRhvV5pLqPLo+lNdqoi0PcUytW/jZPXzx0vhBLAvDtN8AwJPXfpwfZ0Qd3HQEUKhqzJkmptR9BSoI6vJiRmswntdMpqsaCsO6pvbZZj4NDXGmxpPVGmFxPbxPbWNmEWePakQ9Y9mcaEdQQnFYbE49iuilsvVKmUjNN2xzwAkbwGH7tin1PXvQ2BHqnz7arCtEx3mrQ6E8HDVqh9E1cuBVdNQ8djSgEaKH1AemhTQyc69ZYoNClAitzY32bWtna+Bul6zi/nNFF597ouc3654OxiZVUVDH0H603HLCXFC8ymOuAl97Ur0l1efab6EAeQVTIHT0WrNnUVaKq7rOueJ9rGPCBso0KRyUMS0vZXKjbcxpXkWPhheP4xEHtlXMoUs/pD4cVzGTXBjIyuU73R0s4ofcfD+x/BpKmEKvVhZX9gVM8ohAghGc9DQy6FTqjj6ep3CEPy1gxqvYdx9eVjjhdDCeAWeHBtPJPrmHrx5JdrcC/SQ6UVHwMiBq9baNoZr7z6Ml/96TdJqeEffv3/4uTB45rkEntgJM3C1tgxeoNJYDppmU1nXJzPWS9XKnRBCUayUXth1+ptqKkmrBwPIIOLLma1jQEnB9342rLsMxOow0JUD4lJFThwJDbKqls6hTGHmJCStbbPkF+pq2WbWnH9uqjiU4OsZ2Nra8rWbML5fM5mnXVkuxQOtqe8dnRIahpCCCyWa12DmAgxslp3PD694OT8Qq/B8jcaoUXtFULLgqlJ9K2O3RpfYBR/JsPzDUB0HIeVvyuacGTF1WP0ksHonGZxhUBuo9LUgVlvqu9f6esZhKgkJR+pNG5RvdGSxZJuVuuvG1nPl+MwLMbl/ODaAZnCxeJSx9tJprdZh7loeDeUlvUzIUaatiXEwOVyQe57pNcKRW41BI551IdSPQLfW3aqK2H21ePFUAKCAUcG11MFxay0scXk+vrIygZD/YlCcYtj+S2G393d45WXX+ZLX/gpfuZLX+TBk8d0qw1FgeWESaP4/RpChEFYRJXLztY2d27e5OT0jFyWOB1VcXfNb8Jvx60CDPBhd2+jmMsXCAZZrt/rCcFMdUFLHM5REGInNnhDTIBFOQUjZBcoVxgR6zOAYYqyK9dR2GDHrJ1wdHSdw719Hj99QtdbtioqFdeNnW2m022ywGq1oE2Bvd1tLhcbjs8veXJ2yen5nNCEOv15aADTGX27bUsWWCLk2YTSZyT3ftn6nEddkVfWVhiRsRr6MRqQxqpFmpDNw/nGlYOKShxe82EjIFc9z5Fc51KovSRB1zWYOyERSseVfIIqHv2KbPcQES6fPYNpQyw6iMbXqV5stXK6sZTnIACFrivkvldDglQGZB/rltDhKwJDyzM+wu9HuwIvhhKAOmTSE1o5WWzlWnb8IEWQjM5jn02VhWhjaDQYSowi7O/scHS4z872Fq+/+hLXD/f53Oc+zfd+WLg8O0e6HolxEAhzOUOBGAql12Tcy7fvsN50nBwfVy6/2i04TrrYOYBaiQgx0LQNJRtturlzknyYCkPOozx3muBIPnvdrZWfI9rGJJBs84jBdBXoEwZFpLumKjAEI7AI5FWhnbZ89c0vsrOzzbOLc5q2JXe9YfxVSGbTlsW6Z7PeMG0bFos1j5+dc+/pM3KfDfij15UsCaPArcKi28CFDuIUAk0TYW+bbr4i5666te6PVy/GlvkKe5RPLvb90jj5y8CS7J/1tdMpz6YgRkbavzIKQ8+IhQwloAlRe96hKJZAPYJA2MgVcNO4C7RYlSb1aK1/0+n8AfT5FB2kQU4yulxXfNiz7Olz0cpPpZsfPCRs7xYpFQpe94mfMTiN+8cfL4QScPenZjDNPbN8nwmZxYnVPQ5q5V99mf29XTbrjrt37/Hs5FgBPQUowtmTE84OrnG4f8hqueboYJ9f+OpXeHx8zPz8XBfHF9dHggVrN5ZA6mF5ueb7796lyxtqx1l19UIFNFUL4YGlu519oe83o9q83adRjdU2WFxxeAVsFJqg96wJPtEe/GwKxUk1HSxjLrEnFoPteBGlGSNESnZqLXUdU4gkEifPzllveprU1CCzbRpSSpyu15wv1+xuzWiv7XF6fsnJ6ZzjSx3IGozJR8zzgOGZSqMVk8vVhgtLCLbthOlsRjubIMtMKbnW8P2zLlAwCgGDlYQ9Lo5Bew5ca7pyGIWXV8arjQXFQ4+xwnFjjCqFiCX9YtCuTn+/7c0Sg3pofm7xdVdDVmz4jfIOFnITKRPbK32B3vfFEFZSrLhYQEKhDlZFvUodrKLw5iY19LKh9NnmV4BMIsErCSg/5YgO8srxQigB8DDAADxjPLwvqMtcQoU1BDb9hvOLM6T0NT52IY1GHNIvlpxfXPCDd3/I99/5Ab//F3+Rvf0DlquVuZ0AQWOvlMjdaOZgEdJ0i63dbR4+eqBCPkpIhoy2tVoGXYJyI05n2lCyWqzU3bWwQQIK+DEHJ3hvugipQkItE82gCGqroF1u7HVBfDKRrp+ws7fLbDrj5NkpJdtMAs9gx1iTXJM2IW1Dn7WbreTMzs4ON2/f5N5HH3F+eU7Xd8rxiDCbbnPn5g2mkynLjZDkktJ1PDm94GS5Zp2zTUAySHCKioEPvV5+itWD8zJaAHLXsRFhMpky2dpiM18iOVsSDxsEQoVDB1QxJBdEXFiV7k0TdPqvz5fwcFKcp8CpxE3Yg41rd2XgFaqAhpUO9AoEpGkoI1EK5nFV6+uP6zkvwyc5Nxsh9PZg3a3xR1urRmIuv/0xRKUfRz2BmFI1CGpohJ2dLZYEurKEjX6yD0IS0e0WIp+cEXhBlIDmApyxxVzZoBay1Bgt4IwhQYS4zvTrnsebnkfxCX3X2cMdau4avglPHz7i6xeX9KXwre+8zWw24WJ+qVrXYjcfXKHut30+BqTrIGhloFg2H3QqTfIauggxF6TVh5VLIYZQeQwKVAGuDT52f+MNLmMUY/HY2NxDO4lEUzy5ECYJimLeU2rY3tpmd3eH+XzBYjHXz4oi0ELQCkBIqZJ/FJF6PZHIkydPOb+4oC+ZZGO8mqZhuVgzv1yzc2Ob1WrOcrHm5PSCZ88uh8y6R9ZZY9Z2EiAmZF2Usbkxs+6YB8sV9Lkn9IFJO2Frb4duvtQKRxiES2vvYbQXbM0ZeUnmzot7lC6M9t4aaoXh/UDtNBx6KcLVcjMMZeDeFYC5/0GfS5IhRBOT79wGQ7ia8o0qmNFg6WEMvjLlrKGInwxCjKSmoYiObgsxWrVF5ygiauWXqxVOpejsVU2IFBFycafxk5VAeJ5V5f+LI4YkTbMFUOHC5ujhWfCaHjBGnpTRWH6SkCZUFiLPgqYspBFhR0mJ6f4+i/WSvvSEVuv+4m7cKM66QjTq483GwaU9K40hLfMcgnFvaDIv+Ghv6+byc0SoOHYBmlW+Evv2bbgqAH7jBtQhGTIw6BgyDPLLppBCZLq3zXK9pN9o6KIejSqn7b1tbty4SekL8+Wc1WpF27R0XUe36Sl9T0iBa9eu8dLt25zPLznY3aPvM5eXc4TC/v4uT56c8PjxE7tAvd5osWoxSHcIg5AEV2BiqX2bKzAk7QJtE5nNpgSJLM8XlL5XS/yc5a8l4FH7cEmRphuITNy11lNLLR+PIeTFQytRJVO8xh80tPBSbraktD/+cEVchurEgC0Jg/UPXvrUDROLWeVgNHcCntAeh4leQibqNChdN6lvELH9VQSCz+1jGAdXxLzKq9yEZXH56yLyCzx3/Agc0T/jI9gCjjKboUl1I+tUGLXslvwnUhDJWo5CrsSTXvbzSCrmzOr0GdIboWMWTfiUobfcPqgPIUVSm0zDC22KxKgc/TElhem2gWIhiC62toKqG15slBimwEK9Lu38CwrmmTUjl17zC7HXJiKnr6p/H1I9ECLZviOmSGkCnfRcXJ7T970lrtAKSNHzyVKJTGOIBCL7u/t86tXXuHXjBjEIJE3afeXNN/mFn/kZ3njpVVbLDbPplJ2dHVJs+ejhE56cHOs9x6GNe7yKtZvR1rKgG1ktsNRyKVi3J0KPsN50xBSZzKaK5Q8BH0muD9FDRN8gmkbzTS8MCgDEciFKDyZBWZvwcNPzDbVl2RWxXAELJZ+IlI0IxiyuPhuTP7P+3vp+xcsbhbQiQuiz5pDCqInKbicV66wESIoNCDmTNllzERUopd6V8mSImRZHsGqS1Pek97/8KFP/woQD2R6QtvcyuGtBE0qhd7fJMrYJqrnxOK74a6iQmKsapZiFNu1sliHEQGn1IcSCbtQQdLFFac3AkjUxMJvOODq8xunZGdPJhD5nLi4WlK5XVy6gZBSoBxAM3qnZeoPAFtFuOuvsUl69MDAL2T3GjApmADrj2seywKI+hTMINanR4ZwpEovlCSLkPpNEuQgzBjFGlGhUCuenZ5RNtphdg9sYE9evXef64XU+mHzE4+OnvPveJaXvICqeIcSoo+QL1G5NRK0Xlqy0x+DjyPXmZOThDP5XMOHuS6brO3b3tyHCcr7QseCgD6miMqlcC8ErQRFztYdNpQlUrFOQatFHb6lNapVIRtSTK9Fp5e0+7LOVB7L3103wPaFnIWI0zL5bY1cWFCvlSVD+g6jEMbG680Ez/kUo0itWJIVacnRhLtHWu+i+ElEPwLEJOnzVPNM07KuPO14IJQAQOygTkBStXj7EyxLCkOHWTAf0Zv1rQM3wdFE4bjR3tG8ipWmqcKsnoNRW0bSnJEixBYHcbUauKhCD0m7v7dEY28zRtWuEEFkt7tEVFS4lqCgQgwJqDMI5WDCojMBRVLEj0CSdamRKS7B8gCMDRT0Cz2ZjHowL4LpfKCFFo0oor3tCLjRtw2Q2pS9L6AqbzYZHTx+rByKZvOlYLpcqxBFSiizP5lxeLJhOp8xXSzalp6QCKVUrnlIk52zzD/2eRJOBClgYKiN+AzA0XLmFjuohiT0nQmC12bC7vc3hzjZ5taHLm1oVCoQKgKn1fIcHu7UzAbL6SAXRDJRl1DXV+RX6/T4tKIq1r9t1uQLQXoCh/i8RsjMzl5HVRz059yRiLzXfMA4zY4Fs/BBYWJDdAylCFEUnlhjIKSifgDtAFdruU3Oh8fCl1WsqvRhrlX/hJ/sCL4wSANTVyWizihhQQrK2gY6PcaAzkn533avWFY2XdeEMHReAXqsH0ngGKUIw9h6P4YteTzT3Pi4LF+GCy4s5Zdmx2ltz7do1YkwU6UaXoxarjCziGKiDgE+6iRGkBEKLGdAhRh7daFUYIY8315CL0BKnDj5JswitcSmWwma5VGhuo4NK++USGlV+BRksOtZFuMn81rfeosuZ9+99SLfesLO1QymZdb8hSKSJLSVrprr0vSZ1fXCreVm1ndcNeRrCB10C/bkUCw+K3meJgfVmw/bWNocHexyfX2jpK9h7sxXDY6CJkZQipKjhsVg7rhiM3HoLRjGiCb+BjkZIP2+iqlOmzeVPI5fdrWnj7FA2Dj3YXgviYCY3WAweXho6Np1JKXrCMij4B1EYcx0CE0ceU4KRG1WVmyeTvZKh/Iv6ugtC0yloaaSDrhw/lhL4hNkD11GC0U8B7wP/log8Cxr8/jfAvwIsgD8jIr/xI89vDyZg2rIbza1zV9NvXgbsgFJSh+F94/cHRZA5q6/Ghbawrkl7UYVjfYYdnSZcYqyJnhSSIsZSoC+ZGAsh6bTkXDI729v0Xc+m31g+QOG3EOxBBM3gh6Cttj76qojymcRAyZCCjMAebrXC4A3FYKzGgdS2UDpC0fAlTac67rxt2d6ZEWNgtd6AoGOvLuf6ncZl59ZXKQR1UUtQFGLTJB4+ecTDf/SUXHogcevoCBHh8fExN65dYzrV6sr55YUuu8e0jYYofS5+WpN4/c8BR4iQzS93AUJcGQUCkclkQkSH0W5Sz3TWsu4zueuRTWEya2mniqVvUmLd6f1OZzPWq42i60h1D8SuVBRlpa73vecb0e7Docm+oXRLDZUb93xciF1J+FHicF4XXgmhlgpBqwRhUyrJixOmYN+RjZ491WS3d6TqAFkZIUkrt4ZPXHIa9OJVCIWwf5IS+HETg/89v332wH8K/JqIfB74Nfsd4E+itGKfR9mE//KP8wVOze11XyFoeaX2VDM8OIeIRoY58L6+UT+XU6gLV2IgFaHphdRf7RL0c9c8rtW7J7Mp08mU7e0tRbhNElvbM7a3tpltb3H92jXu3Djis2+8zsHhASHFGufHGEkpGQApEJuGZjKhmbbUXgVRVzimoPPtjNSi3qtp8xAjbdsSY0IItLMZB7euI1uttlMT2NndZRIT29MZsUS6TWbaTrh5dJ3rR4dMtmd6o44x7wU2Q/tx00TapFiJstWSZomQhHbSEm0I6mKxIm861uueaTvl9o1bmpfIpQq+FCHFUMujila0te1RpetMQuYBaK2eIfkrQl86plstBwd7HOztsLOzTd8Vuk1HbCNpS5tvLhdLlvMFl8/OWV+uWC1XrOYLjaEJOoKtaUiTlrg7Q2YtpW3wrL0utm0dK5c625OTtUowvEDdc8Eo7OLA6pOCNn4l90if91xltEe9VmBuf2+xvGChsCdbXSZMkE1BECLSJMKkhbYZfUbDRLpSQ6fcRvpJRCYNTCefKHs/lifwcbMHgH8dpR0D+B+Avw/8J/b6/2jko18PIRyGEF4SkQc/6jt0GnGo2tYTJKHkEd2UuQHVW441J+BJIvCHYK8TKua7an2zsMrbr2o/Cdojrl9ELxuNyZJwsLvL3u4Ot2/eYG97j+9+74fM50uNYdcrY+NWIWpTw9ZUOen6XMi5kFLi8GCfs4tLNt0zbQ21ttDUCClpDqCsMqWxkphtmjhtObx2jfPzC/rLOYe7e0wnE45LJjSR1AubywV9t+HkQuv2cZK4c+s2t2/cRBCWiyWr5VI3XA0j9JppdLy3ZR0pUmimU3a3t5hMpzx+8phHjx+TJdO2Lbs722y6nmmMNDHRCYYWpCq3FCMhWFLU4/8UEEc42hrXRiFTnFJAcmExX/Lk9IxZO6ETzZXkkgnrnm69MUTk0LEZNKqpqcZ+taFpJ4QY2azXKmytdi+GGMldpmw6houhQqp9mlPNG+DoRCrdvbd1uyuvb9dzBXNh63Qkk/gQhp/rqHapfgbj6VeAUp75Z00ZSFHuizBpdCy9CH3X48zDtcU6NcS+J4ZA30SDxY9cleeO301O4PZIsB8Ct+3nV4APRu/70F67ogSuzB2wO9dBlmCdmgi5Cq27Y1GMwrrW3vWocEkABmvvnoI/sFQGzT4uNUmncae/v08dzBJS4M6Nm3zmjdc1/s+F78d3eOe997l95yZH1w+ZTafEGDncO+Dlm7cQgcv5nD73xJTY393llTsv8d4HHzBfXLI1m/LqSy/x9OSE04tTrQM7LNVq1gGN+2TZMZ8u2Kw3pE5YLVZcLuaQs26iFCkUSlAyDwRkAycnz9iabnHt4AA6IXSDMgXbu405vJsyAGxyoZkmYk4kGuK60K3n5BjY2t7hcH+fi/kldz/4gE1eK+7BEIJeli1YU5OgiVdRxF3xyo27vgbKCUmVRIxCEcVBPHx8oso5BppJy3RrSizQL9eItViXOLjinoYs5h3mboP0ocYqJWc2JWvCNiZtHBOM6FQtuocrQcseNmBU3X9HoHooEXAFN4QCvh/FIb5usKzhTcZhQ3DewGgYC2tVDlItewWSjc8vOiOjR+rQlmhlTwmR0JhCtc7O2PfkrhsSqh9z/J4kBkVEQqi39+N+ps4diDFJBY+M3DMMEEQTkZKHLlrToD7Z148AFZKqgu/Cj8WmFp978wl6ntIEc6v0uTWdtmr2IpT/m7k3ibUsW++8fmutvfdp7rn9jXujj8iIjIzMfPle5sv0y7L9bGx4ZbkKCaSaIBgAAoQYUBJCjACJSU1KCMSAAQMYlBhQBgTGYBeSG+xnu+zn12Tzss+MvrkRtz/3dPvsZq3F4Ftr7xPpzLLLZUtxpMyIuPecffZezbe+7//9v/9X1uSTAmcVjx7vMplNKH1B7Sv2Dw4lvvaO1GipwltfF2WdyZhZPqeTZnTWMym4yRLWV1e4eO4cL165yie3vmCSzyjzXJqvGhVVvdvntJbJ6QiUTNYRAOraAAAgAElEQVR8NsOqBT3FNGV95wyj01Omo1FAoB3FNOfR4yfsPzlgPpvIm+NpTRg2rVCZEVfSOxHJQFPMcorJDH+q21gVKOdzPr99m8rWOFcHpZuEra11lFacjkYYIz0HvG89Lx/c/PhgkTPRnIYq6OhBi8gHDQLnYG4d9GB9Y5n5NGM+mVEXVWisETaloknF+jiAgaYcw0bvEUxBWaJ+ojayO1S4LwVSHu09tqxwtWtoDQ1fJyzVdtfTMj1DZySllKRkv8w+dDT3q7SGNEFl0v3KBq9FaZEQw7mmzX3TS0I5qCUTpVByr0mC057aEsBti/cWF8RfTQjB/iZqB/aim6+UOgfsh58/Bi4tvO9i+NnXvjxIQw6vA1jVagto5/HGCSswsNF0nODo0j5jMVumWIMTEt7XhAs0gBEeVC10YLXAtPJBdNLXJY/u3ud4eEJRVyQh9l9a7pPPSh492cWkCa5ynJ6MmJ+bc3bnDCfjE9ZWB6wMVtBK83D3MePJFKUVGyvrjCcz5kXBmY0NHj/Zpypa9zTq5AmQpBqj6BKaajsViTRlzWQ0oigLQXgsISxylLMZlXVSTpxJMYm24hH4TLgBzoNJFNoJkw0I4pmgKtcYS+UFnS/nAoBqNFSOrJuxuboZ0n2aTtbh6PiY2UyUc7yVcfceEUpRAZh0QABJVTh1G2p0+D6vA6LtHWVe4Lpduv0UkmWmJyN8GUuQgzGxMs+x8Cjed1MMFP5OCCtdaCUX36xDRkqlCTpNMEaLh+gkc+IX9BClFDxQP13kP4S0ZyKYhTYG5xyqtlLmrTQ6VQ32pbMEixfqNOIJSpk4gISRGIOtapk3L526MSaoKYV7NZrEaOlujCNxoRFKYkhcuG79dSbgX8wI/N/Avwv8w/Dnbyz8/O8rpX4N+FvA6V+EB0Dke4fBDDr/jQCHDXLjoVjDSrDfkFTiJvGoBahTteWyzXeIm9XmiqP3oRojoIPmoAu/9waqsuTk8JiNnU3WVpc5c2aLJDM83H0iMtFJyng8ocxLksSwvDTg4tnzrK2sMJvNuXX/HofDY/JZTjfrcnB0xNHwhHw+56Xr1zg9nXA8H8qzBCB0UU47WLVAKoriIaLdj7OMT09bcAgXXOS4K4JXGlNxcTysuOHOKWxhgwsUUodh88X0mke1qsDOtQQa53G+Ynf3Kf2lLivLS5R1jTGaXr8nRVqxWxNizlTspBS9tHCdhTeJGYikLsLzW8vh8SkqM3SzDkm/S1lPWw5+nGQf43uaHPkiTKcaz9A/42ZLS3PAOWxRUlYVJigw6zQBH8RaSmm/JvRnceVdbcFaWX9JMK7OYm0JSUKSdkitFQakVjJuIeNki0rIWtFzUOIFeSenfWIMJjVBds2jkgSfaJwVbYGqqigKkUn3CrIsIc1SfO0AjU8UKjW4wv/VW5OHgfuq3gP/EPjflFL/AXAf+DfC2/8Jkh68haQI/72/zHeINxD+slAAItr+UdAhYANEhqAP5ApoFVnbC0ZWYZu/piGl+MCyagpf5EGbUyW62z5sggTNiy9c4+aN6/zcd36GJDX88N132VpfJ+t0+OlHn3D7zh3W1tZYWhpgjMF7uH3wgN39PbRRrG2soDHc333ELM9Z6i4xnxcoAyZVuFqhEiWy6xiSNKXM5xEjlPs0Gh2ah/rEBoFMGm26CHq2RlTQbhe59cE4esS9zDqp1JtXVjZ/bUWJJxhkYaCFa2UdIbGUVUteKismxyeUVZ9ZPmNelWht6PW6lEUZ6ufDvXsnqliRqhs3JC1/oKkzWAD+oh30zuJrx9x7et0eppPiZmULBDdTH1h0i5klwvwb4VP4IEkn+pQtXVO1UJ3oINYWpURdyKRJKxjjHNbWmDQJIZWRfpbe4+pSsiAa2axAt5vhracqCincUuDr0A/aSqrQCXtsISR2oJMgQCpqTtbL97ZktsB/cdJdq65qyRp0DVSOuhIAWZvmdPxzr79sduCreg8AfO8r3uuB//gvc90/99mI8IdyWwJV2CeqCRGaeDa8X6y4sKMUvmGAydpSTc42Hg8NgypeRMdNI4vEhsIiFI1rnWYZK6urXDp/kauXr/HW699mqddjZ+ss21ubDIcj9veOeLL7lLpyGJOwtrrG3sE+RV1w6eI5NlbWGI5GHBwdkZdz0iwFDQ+f7jIvCzkNCtkwJk0Y9PtsrK/zZHePqrbUZSnGSCs21jc4HY+oaivVapagaQCkwnvAtTnseFDGrZJ0MtJ+h06vw/aZM2RpxtHxkPFkSjHLhXHoCG22Wje62+3iccyrquUBKBmj+WTGLBcSkElSqkpani9WasaXgIAhjbhg4az3QezUtx9QwauJxVDWU/sa6yxpN2M+L8PzqQa1VwGdBxVAPhq0XuTI/cL8R3ZQCNYjvhBOlOhl1lWN9a5Jbcb7q53QuDFacIzg1ajGC1E4Z8mLOQqp7PMhhRnAEpxvQ9MGTIzejHNYHVrGB1AwUpPlewL9PZKVrKOiauj22gj/Y1HM9cuv54YxaKwn6rfhZRCleUZ4EO+aYo9n/TtwWo54E+rzfRTr9IEkFKraiHF0AEpUYkhNQhqAmWpeUNV1aAYpjUeNSjh36SKvv/Ya62vrTKZzxpOcuvacnuYMeiUnp2OMMVy9cpnZNGc8njaptEFvwIsvXOXqpct8cfc2P37/fY5OhjhlqV2Fd5p+t4NyUGWKejYnyzrsbJ5heTBguTegrCv29w8YT6e4oiRL06CSI6xHqxcopak0OyFUlDWstWg4gSTLuHLlCtvbWyg0eT5nks0Z2rE0EVUQy7ldVF0CyUCU9TN1DlECzHj5Hu9cw0KM4GD07BYIbzLVsdjKBc8EJbiEahc3HlztGnk2HyjJdVHT7/bQJpF4d9EZCPOuVKtEHKmLDUwXAbiFkOCZtyrawyRe1rbrL46pDt9HLc/dbM543eihetAmeKxOxrIVPYkkKlp6c7iSC4CplA63KewIiUnYZMDVjQFC0WhUyljrZ57jy6/nxgh4Aj+esEHr1lgrfKsLF33+Jp8XQsZEo3DNAhWteJmluJggLkTfhBsKTZZ26Pe6DDnFzjzdrENtaxKjWRos88rNm7z1xhtkSconX9zlvQ8+wdWWH77zDisrAwZLPZZ6S3TP9tg/POD+g0eMJmNGoxHdXofOS30uX7jKYGnA/uExzjnmRcHxcEi/t0yv08EozVwbpuMcldfURc3uaI9LFy7y5uvfYjg65ePPPmUymjCZTSlt2faYUyp0Bg6LI5BXlPUta3LhKC7LgnyaU80tR8ND9g73qWuLo8b5Gp+EMW68J1HxKYocytglIzLYaBlzHrJOh95gQO0cs+kUaitGObreeFFGd4G4FFKIMXnQSnW3FkNHzzDelpOwxXtP2u9Sjibt78LcNnF/2DGR3edNKCGvQ+t037r/0UR8bbFNwwkIDx08JUI2ISocLNYXKBAcRCEq0Z4m1Fpc+41EGbQGS6kGOJWUavt98ZMxTNIgAGAEKqP7Fzyuf5ZkwHNhBKJFk90cB8Rj4mQ2DSVUY6kbpLf5mcdqsf7RTvhAVwVPp9NleWnAeDqm9CWdtCO6+GUFKJb6PV66dg0PXL18hUQrqqLi8zsPGCwNcNaxPzxib/+A33zw2xwcHnA8PKHX67CyPOCFq5dJTMKj3SfcvneX0ekpVV6wtDzgg8FnbK6fYWNtwLkzO5zf3ubRkye8/+lHzOcla8srUrwzL/BGUZUFjx89prSW+Tjn/M4OFy9eQCMknNsP7vHBJx9TewvWY4KH5EEYY5EWnOjgAflo9wQVryoe3X/A/sEBNY6ingvLURlZdKlpj0Xr2wpGWz1T2owPIUPzdoVOUpz3VFUpIGLt0LUSgEoHNCfei4559wX3PFxXMgdKipQW3JhobGLTUDopZTQkz1xAXrG1txTlhEafmW6aqbg4dk3+/9l1+YwUffx+RQgj2tCgbQa7cEyHAqkYoiilscYK+BqMQXRhFlWhJYyR5/BxZMPmVkjHpehhNV5+E1mpkOoNI+F9w+r8utdzYQTiCR8OGPmRigSLMEAxdo+pvegZNODPgvUMg95wDYDl3hJXL17g8d5TDo9PuHnjBs457t57QFlW9Do9vvPtt3DOc/PGTa5ePo92jv/+f/pH3Lv/kLNbmzx4/ISynHFwdMSnn37C8uoqVV3icTx9uk+apdy6e4fpfIqtS1RVMR+e8umHH5IlhrNntxujcef+fbI0ZVJOyfMCrRW1rYU+bC2uKgDF6cmQz27fYTKbY13N5QvnuHrhEp989BkuqClJ/B6euxYjoDPdNuHUSvAVH2yiddS+op5Z0f4z4JGqym4nI8kMla2kKSZevLLoTcTTSYnnJlL+7clU5XPq+Zw6iHTr+L5axGObkx0EkItzqVXwGGIcTqiUpK3B8L5l7akQOtZW7ingBnKqygUasZBodJDx8UjYhBF6tF+M8xUNA5S4Hls2UvMeDw3FOIp6xg5OwDPNT1RoT7aoj0kAHxo/ZDGcWQA644FI8ARQvmlZpyov9SPBI3lmnwe6tnOiPrRAlP5zr+fDCLDo4SzQeyGoDKlmkzcDgm/prvHdcWyjJfY07DTvPPOiwNaOM5tn+MWf/VnWlwf87h/+EaPxlOXBMsv9ZWrveLp3xLUrV7hwfpOf+86b/PTjTxmPJ6wM+ly7dJ6jc0MePLhHXsxZ66xw9sw2qysDnu7tMzodQhKMmFIoW5GfnvDhu+/yWbfDlWsvcPbsDk8PD/DWkSYJp+NTrLN471CpFgAqPJBK5cT5/M4tTk+HPHr8mDQ11EXVHiQmnJiBDIUBnegmntRGev75yobTW8lJGBahVppulnHx3Hk6acbR8ITxbIJzJc47ySyomCaMp7mMcSP+SohtqYXxGckzxJBBXDflgnCn0Xin2gpL76UMOdyTj/F2U4EZ5tQhKtNZhveOylbNgRGNTINZEDCgaCBDpkPqF6zUbaSS0qu9C4VOzbe19QPQ9ido6MCt4QgrswkD4hgJoSdqGfggEy/UbBW9j7D4fVjbzdKNnomnERBp9or37f0EfMsnSmownAtqSDK2RovmY7QhX/V6boxA486pNiZr4h8tCKeyobW1b+mbskgiv0B+pr1qAJm0k2GUcMZ73R7bW2c4v3OOt775TbY311hbWSbPC9776FP6/R5JmvDDdz7g5OSEa5cv0O8v8Uvf/Xk0kprpJBnb21s8evN13v3gE+Z5zsbaKm+/+S3+9Ic/4rPPIZ8VKCUafSos9MnJUPTgpjMePn7MaTGm3+tw8dx5nh7uU0zKVoBDg0YLeFRZTscjRpMR49Ep49MRSQBK48nnjQqagDH2k1y0D16BDf5s04zCaNH88x5jDDtbZ/jGyy/z2ss3GY+nvP/xx+TzXEIsa8lnBY+f7FPPpogmEc2GaIxtcOMad3nhPdEIxC5TPlYZNh8LNf2hwMkpIBFMAy86k83ZaBQ6S0kyI9Wb1E2T0Hg/LoQn3nlMwEWkIC3eY1gvkaWYyNhFMxZP1IXHkle8T63D2C6EpI1UefvwKoY+0cOIa7whFwk42QyoUw3g6IHY3iiqsi2e5YJvtF6PeEzykaSWlnsoxcqgT+VqKueeb7VhqfSL/1LPrKE40J72ZFF+4R0hlNBGYbKUfrdHv9NjPJ1grePSufOkScbe4SHew9WLFzEmpSgraue5cvkKJycnFO/+lEe7T/AePvjoI+4vL1NWOa+98hKD/hIXz21xeHjI8HREUcxZW1lhbWWZWT7n3Nltvvv2W9y4dplOt8Pvff+PmRclKmmZWkp5jDbksxmTYo5PNWc3d7h87iJb6+vsHR5y58EDlLIia2Yl5rXacXh0iK3qAPxVAvyHE88HkMrjRMcegpxXqD5TXjyLBSPplVSdGq3BaQa9Zb7zrTd5+803uH3vHiYxzOcFWsPayhrOe37nD/6I+/fvkY8mLMbwX5Z6aNQ/F+Yuzln0rJvTrXGNw8+Dvr/WBK+AZ+J0pZQIsGhFog21r8TYa9+Aj3EDRaCMEE5qaGoyfCD6eOdk8zonA6LjQeQbY6p0wFUiGS2e4Ek46ReOV7cYzjTjofBmAUeJNSuNqxsmxKsF49Da1kieE/2y9ncQyFRKOCFxnLwSDQVtPXQ0aZqQz0rq6muYQjwnRgBkAJ8plFCw2JRSiu+DqQt4QDNQiOJuZlI2VtcY9PuYxKC85uUbN9jZOsOtu3dZ6vWZ5yXD0RE/eu99Xn3pRYwx/Mmf/Yj3PviQsrIYIzqCVVlwcHREmnRIU8OHH3/K0fEJ58+eIU00S/0uWTdjeXnAxfNnObtzhvNnd7j7YJf3PvyEx0+eQGJwaYIOmYql5WXmZYmdzvBzx/hkQl17vvnKt7gxnzKeTHm0+5iV5WV2ts5wOh5zcHBIUcwbuSgV9UuaxSJxuZxEgQjUkK2CWnPk//soBBq6OaUp3sLxyYijk1OK0A/g8oXLrK6s4GxFv7dEr99jdWWFH777Pt///h8yn+YhlSvHll48Oh3PhAHPpNmU1Mk3jEYWjYRvjUZwXzHRosjcO1EWwTmL0ZrSO8kSOKlijPewuAmtiYpAvqHrghicxoB5Lz0caqR+Y0HKPWIEvmEmLnhbKhrgeNQTdBOCgVZtlWRTJt+4NAvfHensC3yehikaOAFNoZRun0HcHteUHEdcyEUDVTlORxPq2qFDy7Kvej0XRkARu9UE6xvcNVFuERBKBbTYKYWKHVsW3FFfSYVEN+sy6C/RSTvEuvF+v8v2mTMkxtDplrz/wUc8fviQBw9eYWVlhb39fa5eusj+4SG3797njW99k9WVNe4/eMTRyTGvvfwC+XSI98scnZywsb7GubM7JB9+SpqmrCwvs7m1xXye0+t2GHS7VPOCJMtY2VxnbW2VurIkSUK3LJilGZNpzmg84fDwhM31Y7bW17h++Qp7+wcsdZd5/dVvcufBPU6OhlhfP7Nu4mnkGy5vO2ZxwURDupi+S7oZ/aU+SZqQlyVKGforXVaXlxmOhvz0o08xiWZjfYNO1qXbWWX/8JitrW1evvEyj3YP0GmG1XPSTgdbV+JSL2Yn4kJegGnCLYqIqvLtZoihgouRg2qvgxh3b5QEIOFCPrSMq6ylqKomd7/gfDSZo3gyOkODZ5AYEZkNGzrGJC7QsE3tRQ050HuBJj0vRi4IuWgl0u3R4LW1zAuv6ClIxaByEFWlorFScdnT/s57ZBNrGSi1UFasrQ9ArkLhFowF4fqI1mQlKta2bqozvnrz8ZwYATxNTOUWqK9NQwlCMZCKJ0hw/SKQE9yo1ZUVttY38N5xMh0xy+c83Tvgyd4BnSzj5PSUqqwYHR1TVyX/9PSUl1+9yb/6vV9mc22V9z75lAf3HnDns1ucvXQFQCrxDo548YVrOO/44x/8iKq2aG3od7scD0/59Ivb3Lr9gF63w7yoKGvhcoNCY0h1Qo2wxnrdLquDZZ7sHZCYhJ0zZ8jznD/4+FNMorh64QreKxQpy/1l6f1XW2xVtZ2OmkUf3MBmx9NuvrBIY1ytlObGizf4O7/6t1lfXeaH77zLJ5/dophXXL50mSsXLpLnM27de0iiM5JU6tV3dnY4Oh6xtrrEeDKhjErGZUlTcqskjtbxnhZp38gJ3qTqAqjltWpjfRONf/j8wppoOAZaibaid2RZD2etKB8Ft6gpFFpwA5pN9uxlm2y07EnV1l0YhwuYh/YK6wOYhzD8lJaKUe9DMWCI2Ymt5GJ44wX4XCT3LP65GM0qRwsjKIQmHw2GD4YwXkrLYSnrIFiRwKuIgjSxWMoHnELEVhWu+jpY8DkxAj66sIhFjTyA1t1vrZzw2WXgGnfOaJTX9HpSP39wdMTek33m84Kqrshnc3q9HscnxxRFJS6lgnldkudzLpzd4dWXXiTrdPj1X/8tRieH5GXJuYsX+MGPf4ytS37hb/0M1lo2NzdJkowHj/coypKyqDg6HvK7f/DHnAxPee/jj3iydyAMunnJuDhkejpCdVKMMVy7dIlL58+Jlv80RylFPst58OAh9XxOJ+uQLPX56YcfUduKQX+Juq4p5mWDbvugHtu21QpRYkMmgZhlaUhCWjEYDLh65SpvfOMbvP3tN7n/8CH/y//+f1EUJVVds7a6inP3ePej98lnc/LZjAuXLpFmGS9dv8qTvT1sbUmMwpfSwsRrRba8RDfLpOS5kJLvxc48ygdF5SDmr8K96gWyzeJGXTy0ogcY83C9fp9+r8dkPF1cQXz5423dvhif+D1NSbNaCEtciK2R+7beo6tAzw008rj+Ij8g0ZLFsVHII3w2xvjGGCRGkVClrp0wHxuq38Kde9m0GqFTe2jIVLK+g8fRiJrQpE+BBawsUsV9u6es5Zkv/IrXc2EEgCaN4yNH3BHiW3kAl6hGWtyzEG96jy6F4js8OORoewvnpUqrms7wCsYnQ8bjEZWr8bWU03ZXlnjjtde4+eJ1pnnJwckpn92+S1lUOA/z6ZTdJ0/JsoSHu7v86L0P2Ds4knLaqiTPc7TS1Lbmx+99yE8/+pRZPmMyHTMbjwTkCpNW2xxflZhul2k+J0lTjDaMJ2PqqqDbTcm6GbOTE6rJCDVKOd3dDQCUkWYpi2vHK0yakHZSqrLELoA+baEVRADKoVleXub6i9d49eZL3LhxnaXlZd7MczzwP//j/4Pf/+M/ZWtjiyTVlGXJcO8pZVXxxWiE6abk8zHntre4sLPNoye7rYOpE5YHq7z4wmXuPnzI/u5TVG0FwzIKZ4I0ukIAXa9a47SQyv3yMnVmYSP7sCkSRa/XxdeOWtIBC5s6XCpkCmI3n8a4hMKxCBR6rdvW4UTKcvynw4eCB60kxWadGD1jtHD943cGYdGoj6iNeeZkV4TyadqWoIvP2hjKYGTEwVVYpXBqAVCMQKwhpAIDSYywTxauk0S8w4d7sEHI9Gtez48R8DQEi2iVFSzwoX2bt9VImssoqaILHYlGx0Peefc90n6PsiqC2+epcFILjscnCt3JuHz5Epcvnae0Nb1+lw8//Zz/9dd/ExLDK6+/xmQy48HDx2TJMuPpjHc++IiDwyOUgqOTE4wyDPo9lNFYW3Lv8SOqci5hpnt2YUbwx2jDnbv3Odw74ORkyNb2Jq/cuE6nk3FyOubPjo7xs0q676Aw3uHLkOIySuiyXpDra1ev8sqN63xx5y73HjwkMZqqqqmLUjQEjVROKa1Jsy5Zd4Cmw/JgjfXNLUya0Mk6vPX6m7z3/sf81m//Lp989jk7O1uMx2PquiJbWqIuS8rJhN0Hj3jlxet8+5uv8bRpW67AOep8zt7+IbPZDB8KmDw0yrexGjC6t3HClRWvLirlLr4aT3Ah5ZaYBA9UVYULWgLSrMa3LqMX99gb+Sa9yKKxXuoagpEU0dBgACIZyn3JInnBD1Qi13YLmhQ63JvXNIw8STF6ObCCErLyiixJqXwt+gJKtCVj3O9tqA1QcnhlxqATQ+mEyCROjGrmUzwp2xjPGCYq30b/0YA6RLdgaTBgNF/0ntrXc2EElAdjHdZrSH1bwOHbTRRJH/IBAl1YLHIEfZzW5HVFPq4ETAnGQlJjqqns6vcG7O0d8luPf5+dnW0Oj4648+Ahe/v7bO9sc+3qZYbDEY+f7rO2scbTp/ucHJ2AkVZZ3nvm8xnYms3tM1RVhckMtZWOMWFpyP8Xbtk7y3w8ZzYc0Vnqc+P6Ncqq5v7jJ0wmU2okdm8/AWhNOuhT1RVKK7bW11BAliTsbG3z4NET+v0lVlaWGt748cEx+XQqsl9phtKGuq55ur/Hk6dPeaW6SVdrjg+PePR4lzRJePXllxhPxjx+9Dik4hLOnD3D6emIYp6gjeHw6Jj+oBdSVTo8k2d0MuR0dBrcUo/piL5fXZSSfgv8hzZ9FlZoQNKbBqsmku0X96FHqsnAWclu5PNcjKoH7xUu1UGKHFTtm9x9G1aEuddR6adNT/rGA5DTGk+j0ht5BVrF9KGXquNwabuo1whNKlEsjMeHzEVlvGAJ1mNSg1KiB+DDJveeRmItMZoszbB1TRWJU8GwAaKtYYyMSenkXqHpwhQ7NYMYV6th0O+zvbXFaG/3K/ffc2EEnnn5yCBr4yW18DsiaBJRUi1ZBKM03cESKjPk+UwIMmGRYS06MSwtLVGOc1ztKEZjXF3ycDLh3v27YBQGz/h0xK37D5jOZmjvODk4oprOqFyN6vWYTivSTgauxlnD8OSUqi5YWV3G9nsM9w8FQdaKeiF21zhsleOtJ02kJfed+4+YTHL2Dg+ZFZJ2k9NLHtoqhdeaM2e2WV8b0O11+e7bb/PC5UvM5zk//fhzHj99SlXX2Npx88aLbK6v88OfvMPDuznUHuct2iT0eymrKz3m85zR8Zh0S9Pvd7lw4TxLgyXSxHDl8kU+/nRKkeckaJRS9AY9dGbY3thEG8PB8RFVJanEWJbrtGyypNulnuXYukZnSTOPLtXthCqC/xpYgakhTVIR/qxEaLU5qRufW/5ufdDX87YF9r1vS84XjUc8CePPQibCN5f07e4lHjDSDMQGcE57jy+kM3PaF7XeCif3seDtJYnUXFi78IwEo6B9Q1wT8RAhevnaNcCnSkQbUHmwlaVWVtqxZYnoFtQOF1SyvQelpb7DKoUqbNP8ZBETkecRL6esKx49evS1W+6vbASUUjeRvgPxdQ34r4A14D8EQsdK/gvv/T/5S1zxGSUZ7WmknyJJwzTdWuT0iMKVdaogMwyWOiitKYoCq20ThSlAac18VkBRU5ZjVF1jvIQK1oWNWjvsaMK9u/cgMdiqZFqHDjhKUbsCbR22qjC1paCgqGtWVpZJ04x5XsgC1qqRRG96EwYhEG1rKOdMD2vGBwc8unW7UVkGu0Ab93hnuX7zJW6+coPz586ytblJr9Phm6+8Rl07zp27wHA05OR0yFKvz5PdA+rSkc9Lsn4fPy/odFJefeMbvHDlCvqUV6AAACAASURBVKnpsfv4kLt3HwBXUAoePNpjsNQnSTSdTka/16Wcz/G15enDR/TW18A7ZrM54+kEbWCwtsT4ZERD6A0hkC3LAEWIIAfaBTQ7oOVJ9G5oNAOs8/hgVIj8+vDXxgiE8UiNKBw3OpLhd7GzT6PG1Jwg0V03AVx2rZhtwAlUSGt4v5A2RA4YG67vaylT98rTSQwWKMoKh0ebRHokaJjOChER0a3noRMBNGM7OmtD1yAtA6dSTZLopvOex1PaUoywtK5qmKRuUWHFeqkW7UgptQvrTERmgndgPUnlqW2B7ne+duf9lY2A9/4z4A2ZMGUQHcFfR5SE/jvv/X/zz3M95aSfukU1YiEtdEprXV37/jgQxiR47zkdj0QRuK4g0FRRskh0orBlCcqThHSJzRLoJKiqDjGswmqPnc2oU40Op5KLJ4eB7tISqoJiNAFn8amhrGuq4xPyyURAJU3znw48budEukzbgBD7Sqok8SirGmA0wvuyRhUOw1tvfIdXb75Ir9shSRPW11fodDt88/VXePXlGxweHjA6HfOPfu3X+MFPfsp8PscWc3RVkS11ObO+zXJ/nc9u3cIrOH/hHEmasNRLKIsxRSEZkgvnt8mylB/84EdUrsDbmtPTIZ1uH61nnN4e8nNvv4W6Bu9++DFL/SWWBwP2njzBeieFPpHf48JJq+QE04XFZSJ//Uypq6dxsWNptAqAQJomOOuxgeijjQkbVtpyNWsnjKOxLYbQYDEemrYbkZBEdJsDih5Ud9RCqi8KkPqA/leFFSqzckIHD4+53F8iy1LmZUGSJGAk9PJKNr2rHSbRGKVFIFZJrYZoCgSmIrGjtSfriXJ1UZZS6oyc6l7IA0L3dj5UiyJhQewN6ZGCMOcxVSyr95LKXSAiffn11xUOfA+47b2/3yjJ/nO+4qAK8LeYMlIBXPLNKSEZo4AyVwIWpr0OzlmqopTW5RGECgdJbcUAoKVttFMKnUku3AfPwydBioygRRDNswrLKJBMGpkpBzhPkc+hLBpPRnomyH3asMpV5dFVXJTt8ymvGq57Y+iDC5ugeHj/Ad//pz/EO8O5nS1efuklXnv9dUGhtWbn3Hnq+ZyHd+9jrePB410+/vQztLMY75ienvLDP/0ByxtnyOc51sLHn95mZTAgSzepreHz2w9xtuIbN1/iwtkJJn2PsqrExaxrqnyG7aRMJ1Pe/+gTtnc26XeXWF9d5eL584yGp0ym08ZwKcLY6OYPyWPXHo3IYDWdgOKp7GkQfK+lpZmvHKoOVXRZQqeTkc/ycFh/9TqLTjDhLZGht/hujW+RsyZjwIKHIUCdUJk9KknpDwbUdYm1hXRPVopOmnDjhStM85zP79zFWUsn7aDShDTLmOWzgEH44N0qMWghFNFak4a17gJAORgscenseY5PhzzZ38N5T5ZIrwQb7y/uA62FLOU9Zh5dBBlrF9ZyrLWp5sXX7r2/LiPwbwL/eOHff18p9e8APwb+M+/9yV94BRX2aJDJaua4AW08UdZ5kRQiHkFNFVy4iCksUlaVB1XTqMy4QDF1NqgMK5pV4pVCm4Rut8t8MhWrq8OasTCfhQxANHa2arrKEkgwTa4+oM+EBRYBK+9jHb4YMlO3E9iUjhK8j2LCu++8Q5oYXn/tVVZW1/BW4kgAX3vqCmZFzfrqOq9/4zXu3n/AvBShDe88J/t7HB/soxQc7+3x+NEu9+49xHvPw8cPeLz3kKoq+OzOXebFnDoo28bhV2XFdDJCZwl7T55wODxma32TQW/A071DirKi2+ujlBJZsXkRcIB2HlwgOemqTZXp0APS26bsT/4X5YKdKAp1ej2ybkZVlBR52ZwN8UI+MU0fhmbJxOvF3H2zztrafReJSM3nFqJqJ01ZvFGoqoLaMVhaZjKqpSMw4J2irh2n41NMotEainmBSQydfkKVppRVqMSspeFrJ+tQVSVpIgVmMRWJB1c5jDesr62C9kznM4p5yXgyI7bPo/TNc2P9M5konIJYp5AIcKhsKPt27m+ONqyUyoB/HfjPw4/+B+AfyGPxD4D/Fvj3v+JzzzQfiSejNIqk5UobLYSRhcmK+d2mYEQBlcXXIG3IolcRFW1oTvgv3YTIWCnd0F8VHpSjmM3Egi+C9QYxNAGUbAyUa8tGgYV0WCidrVw7ccH1FVGItjpSjFWIR2PorDxaWaaHj/nxTxJM0mU8rVgdrGGM4eaNq+TTnP3DY6b5iKdPD/BW09EZeVXjVELa77G+sYqvKqajMV5VPH70Bb/x/xyCtVSuYGV9naKo2ds/gDh2C8PkDVDVqJ4h6Xfx85qjwxPyvGQ+n1LnBVZXkKVo1DMpM5mfaGHBZ6bRxPPx+I9knPAeFxAw7SHpdfDWMRmOcQrSfkJd1G1KL8TMXmuJJlTIyLsvbRClpN7fLXgLMtVEgY7Iumt0CbQJeEPFbDrFa411StJ4/Q62Fs7IoL9EPivo9DLGwynnz+6wsjrg/uNdjk9P0UqxvrnMlfPnKeuaT764hdKKbqBvRzaxdR5XOow2bKyuMRpP2Z8fNVBJxMjarGl7YEQGrQ/hZ9JJ8LaCWujlDefiK15/HZ7A3wXe8d7vyX3JnwBKqf8R+M2v+tCXm488U322SA1uiNsxziMcFgsFRyH+hoVrGOEbKNcOXvy9h4aYgfMSmxGo2xYcos2vYmVajFnjggsXbEA8L3FszA5FHbnmKwPq7xWSb1agEyM4ARrdS7FF2Rg3MQhhclEkxjM5eMj3f2/C2pltfvLe+5zb2eZ7v/iLGOP43d//PvuHR5TVnOlsxPLKCp0s43Q0Y3Vjk53tHZZ6GafjU5zzHA+PefrkiXQl0op8Ogs5dIktlfUx79oMvPeiwtPt9ijnY5jMmOSzkKExaOtwWKq6Ii09OoZQweVXC+xAFYQ2nXPgdCvGEX6vkXtIsgyTGKpxLgrM3UT09BdIMg1bUtFwKFCqES5RQdM/VqJqFwwurQwYSuYr0oe9j+GKE/BPAXVFPhzilQjBLvdXmc6mzIuSleVlymoPZx1GJ5zdPsvSUo/xdMp4OqHX6fHGzVfY3tzki/v3yUzC2uqyFIalQSMxhK/DyYRHu0/ZWF8h0ZpOYiiMoXJWuk0rST2idGijFzcB+CwepgFDyAyudjSCs1/z+uswAv8WC6HAl/oO/j3gw7/oAh5wSmFCXKSMbuSYG0AweswLE9+oDNGKRugQ4zcuefiCxfBiEYGHtsIMaJDj2AlWvlv+HtueSy13+J1zobccQVNOvkwBhIaffrExZ2A+xn5962trrK6sUFeOp3cf4KsyeCPhpuN9eEU9O2D/wZCDp4+4s7rOPJ/w82+/xbyacufRp7x68zp/51d+lYvnz3P7zj1+7w/+hCRNSZKU6XzOcDhhf3+f2hWysVONT0I7rrrCuNAGXklYBsELCAbNVY68nggXIlUYK52VIdTqz6tmyJWTCljCqew0uDQU71RV07g1LloWjJ4Kc2vrmqV0CbUEVV7gCtvgJzLMMpkqxgfOSb0JbbQg9+NoFwPCRwiehDcyD3gfOhrRCA/L1/imaE15hzJJCLEOSbMOvazL/YcPhEVae3Y2Rb3ZOkc+n9PNUpb7S6ytrtDpZUxmUxSKyxcu0ulkPNx9zO7eHiZJ6C1l9Lt9lgcr3HjhBtubQ37y/rucjifNMzXaGYqW/xKYtM1tBwzEAr5jQsj8N1Q7oJRaAn4F+I8WfvxfK6XeCPdz70u/+9qXU5EVR6vmEt39hYOpMQxho8byyfjjaPAE1GlDpfjppjdhBBwJLqFrU0w6qC+okJrRLqjWhPfKKbHg+sd7DauumQgfDEDoZqSUdPpxzkolpHMMen0219e5e+cB3tYNPqGC1VPxvrHhXmr8fMRpWfDjd9/h8PCIeTHhv/xP/xNuvngT7xMGS306SZ/peMbVy5e4cOE8t+/e5//97d/jYP8xta1DNyBDlqUsLw9IUsPx8QnVvMQrQdqluUkYEwf4svHAIjajLbjQrp0ibORYB6IUi0zACLw1zDwtBQRKaZQJbLxwIqKkNVmRF0I8Uh7dTUlLh7WVuP/Wh96DvnWNF77PG92W2oYz0xsTjJwUoAhoFzaW0ZK9AXw4aWWOF8p5yxq6mXACiorJVNz/e492yauc5dUVjk5Ocb7mxasvcGZzg+XBMkvdDpsb6xwPT5nPK25cvc7OmXXKck5d10znOUVesnVmi2+8fJOXX3yRfD7jZDxmPJszHI9w1jdK2cIeNCi1gCGVQq1WiWq9VaNll+df7wr8CxkB7/0U2PzSz/7tv+r1XHSXvZe4TNHE2Y1VDwdk7EgEYVGpeIr4xkPQjQfQ8tXjAm7icrkC0XwqH8IIYmWab+6jBSR9aKetmhy1gqYXvA4pLo+ELb4OdQTaB9AN6trjnePxg0egFFknQ/d6+NkE7zXOa0xjjdoHd7QTP9zf48OTI9546w22N8/z5OkJRVVy4ew2AOtra4gad8Lbb77J9pkNVtYH/OmPfsJkOmVlZZnLFy9w8dx5lpeW+PDzz7h97z55PqVOEMZhWGBuwdtxSgWgU4dGnRADex8BT9rF6qO2f3D7nTbNSawc4GyTRlCpbk5i7SFJEzFaOGqjyHoGlQsTT4f/gi8oZBsDLjFyai8QaNqBdI2ThQmepA/VijqkIp2c+hFAjgVOgU8EgElTVtdWWV9dY31tleFwTH40YTQcM2LCvMy5dvkSr964yebGOpPplNv37rK9dYbrlwvG4wk3r7/Ar/xL32NzY53f+aM/4Ivb93jt5stcv/oCO9s7bG9uorTm4e4jRuMx2mhq55t+DjqRGNoH8NkbhbJOUteJEhm6qOP4vPcdUEBSxwWisAH5bjCAL73XxXAgvie+YgVW/Hc4faVtdvv5+I9YiejlsG6knWLf+Sj7FDMDAcxuKrma60S3ZaGaK7rQRGqpbti2cl9BJdjOK45PTrh0/iKTac5slgs3QQTGIOhMNwU7CrxTKKT5Rn9lwPr6Krfu3uH0dMJb3/4mly9s002hyKf8yY/e4bPbd/jVf+WXufHCNf7l7/48B0fHHA9PObt1hrWVFbyFz2/fI8+LYHRboxjHzWvVZkCgya1TyAxoI22wRYvQhRBPblgHIoDTOgB6rhm3GLopQp7fSydq7RXZUh+vocgDCamyFNrT6XfwRYX30gNBe6knEeUgAVrFg/TNAomeX3A1WopvrDtofiXPHusKmkHXis7KMlob8skEP5vilvoUVUVRVfzyL/wiv53/fxztH0oo4z37hyeURc3x8ZiNjTWuXrpCPi/YfXLArbsPuHD+It967Ruc2djg8vlLbK2e4WfeeION9Q0219fZWF8nS1PpggSkWqESQwUoJc1O4u0pLUbbNsrPDh/VZWLx1Ne8ngsj0MaFsQmJD4suuoch3m6qImWhLqaEmkmO8x7SiYuVaPEEcKplJ/rw1Yttp5uFExaRW9Dul5NCrK5LtAh4qiguEa4R7i1iDVHtR0GjhedScEqjSsfo+JRPJlO8dygDCh02DgJShQ4iirDBVNAKqCqU0ywvLbO2MuDSxR1+5s1vsbGxTr+X4akpbcG9Bw+5//AuW+vLXDx7npevv8jewQFP9w4Zj6d4D493H+OUx9YOpQzeS4FKw8ZTMid4miIuCB2bHPi5hBjGmKbxiAk6gNYpDJKmWqB6BG/AhVNYNR2fFJD0O6jUMJvm0tg0jKfHUbka00mk8WdR4+o2jIreIJ42V27joREQh2gAkHoTFeZJaRUKjGi1G8IaSXVCmqSUZQlFhUozzp7dYbC0xNHxkLNbZ7l46SKj4xPcNKc7WGFrc4P3PvgYlOa7P/s2F89fYu9gn8ePn3Lv9l3efust1lZW+PTWLfDw2iuvcPniJZYHS4ynU05GY268cIOff/vn+N3v/z4nwxOU0WRKBSVoQvo5eNEGtJeGKt0sobIW7xxJYnDV1++/58IIANSZRlcx9vLxAGysdON2BzdxMWUIiEsHC3RTiD3qWl5JABhUG/97WkxAkCzfpIy0JbiYLYFJBbEG+f6ImLVMs6a0lSCHpaQoSCUGoxW+FF6B74h7kmUdts9sMRxPmOVTUb1VmqSwYBW1llx1spDkldNTDEI5m/Deex8w6C/z9/61v8vy8iorm1ssr69z+fp1ful7f5vJeMTtL77g8aMnDMcj1tdWmUyn5POc/YMjyjzHViVWCxNPadPY2njiNM9JdKW9iJ+oED87hHfvXCju0iQ6wSLCH25hriKHX4f5NE7CLiXeOkm/g+lkFOU80GzjkAZhD+tFbckj0uomEal16xquho5dpvzCnIZreGLdfXtpEwROF7EdtMYrIXn5omRyfCJ8hNrhdcK8rFlNUsajCb/3h38YTmRJVRrnuHv3EQ8fPsQkmosXL7G0NODzW/e49dnnKGcZjcaMJyO8K3njG9/k1Zdvcm7nLOubm3zw0Ud89vnnoCxrq0t0+13qE09qFBakEMk7rIraAZIR0IFKX9S1VCq6oI+QafgavtBzYwRirK++9O8mdg+u+mLO+VnhxWDgI2Dl/DNegDTNCJVWPlSD0YpfEk4apxVJJYSiSC/10WiEjRA/1KwhvXDf8T7iVycanaR0ez2W+l0m4wn5ZBpksRQrq6tcv3aNk9EpdV1zdHTMweEh1oKvBFizJvAlnABa2oPB011a4fqrNzl3dpvllR537z/gyuVLDFZWyHodTNYFYGV9ixsvpRTzmk++uENRVBwNRxwenzCZjVHOobRgKNp5bAOyONKlPnhPmedA+8yqAgyYJMEhaVZlAtc9iGcULpT7hpNXLQxO48V5hBtvxMgm3QzTyajqqulo3BhWraDy6ESsSExbKqNQWSpVe6GKsE6C92YD6t8IsvjmmgLdxNiAkHILPAdUk7oUrUbwOBGLUQqqkqODAyazCZPxlPloKBiG86A0VT7js/ffwStD0uvxJz/4IZ/fusXde/cophO63S4nwxPqquTFqy8IJ0EJvT3td+j2O/zZOz/iz959h+H4lKOTE6zzWCehgQn6myrx4gZoiQk8Qr6K9GIRSm3xmK96PTdGwET99FgetrDJY6QmhiHGdO22azTYFzyE6LbH1JMOPwMfpJqDaENwE1zITOjmO1UjyOlNDIR9SxcO+6Q5acJnIJ6UCpQiy6SUV3lPXYU+9aGAZqm/hHeK9376Md45rr9whe98+01+/N677O7uYhNCBx6Eb28UyqRgFabbYTab8ejuI9Kkx7UrKV/cvkOvu0Qn63Hp8gW6gy5Ka1xtKfOC6Uxan+0fH7G795TZbBLQ8OAdqTAO8eT3ku9fXlvhqK6wVRVCrIYWFXAXJOfvAxpNy5mIICxx/BdCDK9U0JGUMTSdlLTfoSgr0SuANiRUoEJnZR/k0kMbHzwa6618V4roKUTEv5OgHNICfCEVHO0c4fRvytT9wppSC8/gg+EIhDINlCcn5KMRFUHfP2QT4hqWrEWNm4z5+MP3WN5cRdWaRClsVXK4t4/SCUnWZfvMDhsbG3z66S2O/vQHHB4fMp7MGE+mnI4ndDpdel3DbJZT1xVV4HgkRqM7mqqscdYKP0JrUaoObFcU1P8MosBzYwSaVxj0GM/rRlfPt/l6opcgD+aCmqvoEkR8IHDyvYCDLhXkVEU3MRjPyFCMPIAYHggOEE6CcHRJ7KVwCdJQosEPQs2Bj9RmRZKmpFlKohPK+ZxpXjBjhnMhPWc9xbxgnhfSSchaPp7ljKczdra3Gc8mjE5G4r2kClVCv9tjfWuL0cmEqqqwzjI83ufDD3P2Dw7RKPYOj3j11eucP3+G+djSWxmgtKJ2NXk+ReEZTyY4Z0l7Gbm37eL2z2IkykE1nTOsLT72IPQEMpHMgYBWba8/p6SxhvY0PK9mQ4Upi4AgMe2KGBZjDGVVU1e14CNxiI2U9vpwcrvQ9LRxLZRgJyqWj6eBMosX4ldm0CoVIZLgHagwz1JAJBvXx8vFwiMV8IVw37r2zxQuOStdlkg1WBrVZRvWrgkXVEpqfCb5BD0uMcGqTWc5t+4+4mg4o9df5dVXtvEO9vYPGJ3OuHntOnVdsHd4wLntHU5HY9754ENORtKJ2VsXRLhlDJJE2q5b56lDr0ac4EfPUKe/9HpujEDTOXjBQkc9N60J1NW2M/EiQt+Sf0QkQgQlXACdYnoqiDGGE9+H0EJ51yyKJjUZUebm5uT+GtVaL6BTrDojeAONZQm33+/2qF0t7cW8k5BkwR123kmqyzvSXpeiKrn/8CHXrl5mZWWF0WiEKmkW9vb5c2gSYILNZxgN565cZmVtjce7T+hmHbrdlHPnztFfHWDrWvj8ZcV0OuNkOOLew12ePN2nqkJ3YeVDAwxB/50LQEiIenxtqWzkKNDKfsX0X5wLpQVHaZR84qnbBmvRS/NKvBontpokSVHaYPHUZYX3LmgqyHiqJEh8VVbKe3XEIsJ3STQhxCUrLrVPxbXxKoQbiQKdiOiItuIyN7wFMQwO1daJxDXoxcBoF1LOqIYdKkrGYb3U7Zh9uV+jR6Eq0XZoey9CWVa8/+FHXLlylaV+D+9qup0E52suXThPr5uxutxjNB6jgA8//wKTCPyvEo1BuBRl7UTCTIW2Y00dhSdNU/rLA8aj0+dbchylcJkhIYBQhJOpcdNkE9tUC1gXpJUgbEqnaHsQxiMoLNBAE9XBFXWLRT4LeeTFYiMfU4pWNWm/yDfw4RjzBHIToT1XcE99MF62qijyGd1+l/5yn2k+x+dSmUdwV3UdKhiN4czZsxwfHjGdTrjz8CGJNngnXoeqPWk3Q6PJZ3M63Yx+tsHq6jLffP11BoOBbCbTIUu6TKdz6lIg45KK8emY46NjTk5POB2fcjoecXp6SpnPA7/eoZUm63Up85zYzz7mn2PSBBUzI+EfQQVJJdLIQ+oA2jJw9eVVpwi59xA2eUiTFNPNqK3FljXaSoqwwYecRxUWH7IQKN/2MjTgaw/OYfGhsCu0G491GbISGjzIG1DG4GsFtQ319+F+A87TZBjicwQPhHiN+N8CXT2esz6uyWZdhdidCD6GFec8w/19PjcJq6urDIdDPvok5+j4gIODA37uO2+xub5Nr5sxnc3YffKU1ZUVBktLDMdjQARIhOIsE2Stb9Y7Xoz76soq169d4dMvvmA4Hn3l9ns+jID3UDvZfNa28uNhMzaxeiJSUsoK2cQpQtluO7CqlmIjlyyePDJZAvLF1SU/k4ydIMUupCUXS1BhISzBC1EnYA0ScxpMkqI8VGUBeIwRTbmVlQHbWxvMq4qHT54yLSqiqk7DmsOTZqKgO9RyL8V8TolCe/FUnJZjLp9Ja7CzOzssDwa88srLvP7aa+Asy4MeH39ymyd7e/zZn72Lqzznzm5RVzXD0SknwyFHx0Pu3X/I/uEhtiqfBcdsTTnPG+gvVv5pH9Nn4knJISlHuDIGH+JQh8MtNuhQNIDpQi1R4+l5pAOSNoayrKkrUVPWsTIu2P6mr6AVtSalAeNFlTfOf5P+kZ87F5V/gmfnFXUVUqwmlImnGm00+NC7IMb9DhpiWAgVW12C4BHq8DTh/rT32ERCFrOQ1o4hRXwp70lCeIr31MWM4/2nHB4d0Om8xvLyEvcf3mc0nvLw8S5L/T5rqyusLBseP9nlyf5TxpMJnSRDKcXcFdS+FjBQq6CjGT1kT7fTE2PoLL1ul+HXbL/nxAiACpLMhA3rTEsj9srjdKjcwz9T6y81A64JD31YqL6J1wkeQFtE0hJvVAMMxVPcJrqlLYecPd41p5p4ALSuH4put0Nd1hRekGqjFd2sw8baBud2zjObzzg6GcomdoQiJHF5vVNgNEYnaJ2Ip1FXeK8wKDFqacJgbcCNa1fJ5yVHJ6d4r9jfG1K+5NjZXOWtb75OajrcffCI3/n93+fOvft875d+AaMVj58+4eNPP+PH773H0709XFVjAnEnEk6UDl5OADSj8o9TSBwcN0gAR71GqtQAW1eNDr7gBQavrJSzugVaa9iXPgx9kkqPSBflynycX5qNFuc88j5iLCyUWTCJBrSELF5IOoAYIIukzZpQUajJWkPsgoxLGkKX4AVtQ49ogGKqeNH9X2y4or0cWNYYTOiFEAKNlucfwqModBNGhKzb4fKlC1y6eAFbV/R6XTbW1zm7c5ZrV1+g183YffKYOw8f8eFnnzOeTLl07jzbW1tSd/B0H+ushFa+HWPxYBx5nvNod+9rm5HCc2IEFNJE0RmkqwuBc97kpQNtEy3W33sh1QTdtQb3UAiIhqR3ogsrr5bgwjNS7IFUFKsSI1mEsBE04EMDUCAxCcpoqrpqLqJYwCXEO6Uoa0bjKZPJlE4vo9/vMxyPRXUmLOqYtSmqijzP6fX7TGZjOt2M7c1NukmHJ492mdY1Vy9f5tWbL/HhJ58znkwAxdHJMccnx1w+d4aT6Qxbe6bTnFl+xP7BPvfu3SdJEp7sPWV3f5fjkyMpaul1SZIE661Qcr2ndhaPoNxKBRFNJyepC+FY00GH8KxODESszdDhZ3hJY3ntW0OAbEiJscMWaFqUh99rIVABrTZgnNgwyMoJf95ihTZrlBhpJZ5aTL3KYSLhTOwkRCKGz9YStoj8lwoqQmG+rW2IhJJlcqGUVy30e/BttoYQ4ii5ZxtS0M9oYgB44XWw+DM83U6Hszvn6Ha6PNjb47MvbrG8NODVl1/mzTdfx1YFVT1Ha6GWdztdttY2eO2lG5RFwd7+ITYQuxquCuJR5sUcVYua9vbWFnt373zl/nsujABNjB4H3jc+ZMwCRK+sMaxKPkfoUhRfSmtatZiFD3yVMYifUVLeqryUtyaJiGRaa4PefMsYTLKEpJvhCx/koBVFUdDtd1nNVkV22zqKomA8mTSxonUOrQ1JApWvcBGhThNMopmXc1ZXV1hbX+baC1f45V/8Lpk2/J+/8Vt88sVt3n7z27xw5TJ7jH8ICAAAIABJREFUB0c83H3C8PiE/5+5N42xLEnP856IOOfcPfetsjKrsrq2Xqq7epme4WxkD0emRRsDWZJByAJsATJsGIYNCDAMC/5lWIChH14gA4ZhCYYl2ZIsmeaYOznDZchZu2d6r6qu7q4tK7Oycrv7epaI8I+Ic+6t4TQlmxyg74/uyu0u50R88X3v977vJ5G8+94NdJLyzvs32N3dpd/rsrl9lkH/lB/+4HUC5YdkSNBWY3RGRVUIVEAgA6fSE9Dpd0mSxNXYyl+wvOTxAJ211gl+Zk5MAV5+/ETiVQByVmjnMZAvUCUJkG4PKjnjK+DXQf57OWXX7+mCAObIk4V1lygisKMuGzs9oX2bo8Ca3e+49qK1/jmkkzy7SUACQjU9VABhlFO05p2AHAfxGn1rbIFzCHIGoivhjG8byrz8e2LNudeIxxOazTatVo+7D3Z5uP+IZy5fBgHj8YhkEiNlwOrSMpfPX+DchmZ9ZQUVBJRKJaIoIhnNtKrslDwUBCFbW1vUKjXKYfSJ2+/TEQRwjsG5fFUa48wQclJIvgCsLrKE2XaW+6M8Y7BF6ibEkwsJKGrVfMEVFFPjOQPWEoWhT5ON8zz0taVI/QBMa1BCEJVDSqUSo/GEhcYc66sr7B0c0O8PSOMEJSVLiwtY4QwmpRCEYcm1ljLHeIvCkEa9TqozxvGEr/7sl/mFn/85rl97HiXg6KjJo8MTLl54iquXLrA4v8hcY45//s/+OcmgTzyJOTw85OO7d5DCjdobDodkmSZLJ2SjBKKIoFoFwKQp4+GIcRwTSEmlVkGE0vnha5zU17v3FgpJ6YMqU7Wdwc3ts/gNO8PDnlV15ie/A+ucfLhUcqaXmdakaUou2p4ai9gpjXvGas7dr5zMYwvATwpBjh+rQHrX32lZAC6ACUBK4T3+LGnmSogcAM15ICKnC1tH0lJB4EhUvkviPpwbKqJT7bJOf4hZ6Viexl8z6fGsQghXRCT3nWQ85p2336Xd7NLunrC8uMjFC24m5e7DA2rVGlFYYXF+kQtb57j01GWWF5c4bZ1y0mxxd3eX0WhcjEUT/lA0QiCkYn11lYvnznHa7H7i3vtUBAGLLfj5FgfkAMXpaxFFymWnf+R/x/1/FiC0vkU1gxfN0Eb93/qFa/Pn8O0xiUOX01STWeMDv0CE7jgyGHSWIoEzq2usrCzx0b0HREHE8uIi7U6XcRyTpimj8Zhef8iFC9t88dVXef/WbQ6OjtybkKJgcaVJitaaUhQxHMYkE8sHt+6ysrxIHGsGgxHGGNZX1lCyRL02TwDEgx5HaUrz6JDMGhZXl1leWiSKSpyeNjHaIFWADCP3mmk+FcliMktqDVkSY8uO9edBjmnLVE6pkPnJnF8+fMvUMfKeTH1zcxQzo1yzvvUqpcIiSJIErVP3Hmfvj39BBw/MlALTNzKlA1uX8huMG7M+szaUBwixBQI0szZEIVvGeKqtwoHTemZt+dLDhJYgUKggQGt/ECBc1hlISF397YuDot53pawtJiPPEssM7prFccwH773Dg7t3WN/c4Auf/yybG2cYjiZMEsP2uVWytMHVy1dZXlplY22Nubl5hqNtjDXcefCAZrPlPqMVWO0UjsqDXQ/3Driwtc2XP/95fve3v85PenwqggAwra3An/YUkRQc/96qmfWQp4v51/50z1M98OkdM+lhXjcBhQOmFDOBwQX6JMvQSeYyiUAWJIxqpUqSTIjjhCiKKJdrbG6cod3po7VbiKVS6Hz3laIclRhPYi5s73BmfZU4Tun0ek4QU4pIJgnVaoVxb+hOlMYCH9z+iOOTlhsYsbrMezduEKfOeXYSpwwGA956+x2y1Jld2iR2GzmImIzGJPUG/f7IlSLem95og0jHkKWu9PEXIW+QOqDNn7rCBWKVu/Ha6bUWuQmId0y2PvvKPRTz0578WpObvvhsQHjHH+tOVZPfKL8Z8+0/bavlikMxxQhyJlJeXvuNCgbKgS/RLMJKN4QJ4eS0wmXy0tf+Jn+9vGSUwq0Hvy4KqzdrXStOG1QUeG2CLwPSzOkLPNonEchUOJelwGKDfNiJW6My8y5F+cGl3DU3OsZ0JjQV7O0/ZmVllWevPs3S4iLzS3NYW+dzn/8c1sJkNC4CuZLwh9/9DrfvfEQ24z68urxMvV6n2xkwX19gc2Obs5tnP3HvfTqCgM15+1AUmhKsyfvyOHmo7zm5BeiAmlwIRA4FQAEmPnHqT1/qiYzC/Zr7XaGcQEVbgwwCVpeWmJtvoJRgNBwRlcoMhkNOTk5JdcbxaZPNjXXOb23x4OE+pTDk3OYZDk+OqZQrXNg+x8baGo16g5WlZS5dOM+9hw9I04ylhXnSNHPW0xPNYDKge9pk0O1xcPiYq1cu0u426Q66XL54HoAHe/t8cPsO9+/ccdmIkG7UtwWMRusMbTL6/R7JeOSsqywQJ0+k8z7eIZSbhiMzjVF5j1+AcnVy4bQzexIXbDp38UTgBD1WT7kY5AxOk0tyKbIIJaU/tXPBl8Ao3+MvKL3T1qW7/e4FZaCcQMmn/tLYQpVJajFySsYx/kR2LuDSbXqBa/EKgbJgcnap31Qwm9FM14zwZh5GaUfSCaQbCKJxswyk9NfVOhNY4z9/ZgrtichL0rxsgGI6lkhcd6tULlOt1QjDiPNbW6ytrTjQUigWV5xtRzIek4zGCKFoNKqUooDFxQWMtbQ7XcIw5Iuf+Sxnz2zw+ptv8/TlZ/naL/4ip63mJ26/Pw+j0QdAH3c7MmvtZ4QQS7jBJDs4d6Ff+pc5DudpZY4CFyBgfljkNao/cYTyY6vyq2oEIqCY6mqKTMFF+HzumyjaX3a6G/wND6LQ4QHGUooqXLyww1PnthkMh3T7PVqdLpVyiSgM6fS6WKGJ45gz66scnZzS6Q4olUMnOY01URixvrpKrVphZWmRr37pi8TxhG/+0XfY3tykVq1w49ZHzgZKCib9PkIIork5Wu0e9XqVixfOc+XSUzRqdW59+DHf/P1v0WmdYpVy4H3x/o2blKstSTxxhKp0moK6azhts4n8aDS24C5Yle9Un4kVebEo2qgqM5hAFiQuISRWOI6HzuXeeRXhN1ZerwZ+WGeapg6DmNlo7oT3r2VmXjf/Ma58Ujl4yKyyU2KUwfjsRfhNKRCUohIGQ5Z5haMPYPlryBzw9K+Xq/CsL03z+ZcCsKkLlNIr+fK0NOcsWAM2FEVcyrEpjAMHpZgGmpxe7kRhFhEEzC+tcumpC1w4f56FhXmUFC7YB0GhlVFBQFiKUFFEnKZM4phIhVSqZbrtHpVyhfWVFZYWFllaXGZjY5XLFy+4mQif8PjzygS+Yq09nfn6bwO/b639u0KIv+2//i8+6Y+FcPWVyFxE1P5d5dNhpqOIZvjt2joA0ddzBbgT+MWk8RNj/Vo07oabHG/AU1xxyitjNHO1BttnN4nHE1QQEgUB5VLEaatNqVRmbSWg2e6wMN8oCB/D0Yijk1NG8YTJkZvmU4rKWB1Tq1XZObfF5sYaUkoqpSrPXrrM/Qd71Gp1yqXIedNNxhirEaFy/Xs0zfYJxi6y0GiQpoaHB4f83re+zQcffkBqUqdOtG4is/H19ng8IjlMSMZjHzB1EUzz0zP/8MIYx0fwR32gDcYKdCiKxMkT9J7AUyx4IY8s7s20s2MpbNclYEVxkqvQzQ3ItCaZISrNPvz0cddtsGDzIR8SyECkmqAcghJukIfna1jjEX5vV+8yJLeZq1U3x1HKjDTLXD1vcpBQekGY8BW827lCCK8a9GvK3WqnWchcS1EF0nWmrKN/F6CmEojUB6lAODHPRD8BDFrrrpHyLtQCizWabrtFp9Mljic83NunWolYWllifmkR5TexlJKwXKbTbvHRnY85Pm3S7fWJ44SluUXKpSqnrQ77j4/JtKXVbvPw4CHj+CcPI4WfXjnwl4DX/L//EfAt/pQggHCniMynqOTpk0/TCywgB1ZmFx1e124saIG1blqxzGs8XIQWOdfAp7dCSOYbcywuztPqduj1Bo6HoC3PPf00p60mvX6fTq/Hw4NH1Co1PnP9Gkru0ey0mW843namNQ/2HnHaalIqlZnEMWmasbG+wUsvXOf6tWsIY/iDb3+HbrdPqRSys32OD+/c4+D4mGazSZbFlCtl1tfWyDLN/HyDtZU1SlGJUqlMq9tnHGcgM1KROPOOXLGGQ7CtBJ0lkCVPYh2IqZzWTjdK0ccW05Mu9+wrUuKcHWemgUSHApl5EU7eHssxGp/Si5n3BhCEIeVyCW0NSZb601didC7Qyp/D/1UebGYkxvl0I51pB8jlu6kYue3buIjieaUx9Ns9TOD0CQtzC8RpzGAwQCnnmZB5ibFSika9jpSC3mCA0VkRM/PuCEIQhqHrRgSWUFiX/mbavaYFpF+fxvEnAm1RnqpTBFefWUmf/QoJGE3r5Jj3b95kfq7B/sEj7tz/mJdfvM5nPvMKlXrNXRtjyOKEd995h4ODQ6y2VP3pH0VlDg4Ouf3xXcrlKvMLizw+OuW3vvENev2fbhCwwDeEc8b8X7yV+PqM4/AhsP7jf/TE3AEhHeHEMwTdz2ersmlmj28POlVhvlj8PTIWJtptkpLy/HfPJ/GBRArhvfNgc2Odl55/jo/u3mEwjInCiFanw2QS06jX+fjeA2QgOTg8ZHVhhdNWB4OlNxiwsrhEkqYcnZx4Gq4myzS9fh9jLIGMOD5u02p2SdMJe/v7vPnuDUbjmHq9zuOjxyRpws6Fc5hMU6vVWVtd47TZIgwD2u0+y8shYWSI4wn3d3cZjCdU6lUGnS4im9KgC92Evx4m8AQoRLGBRS6AyWvvXFflgTZjHDNOeQKWCfLWnHti67MrpFNkYt1JJgXo3Nc+hw3yPWOdPXe9ViXJUuIkmZ7+RQuvOCN/jGKLX/NTrpvA8S2K17LWs/1cLi4RiDAkSxJnMoIbBmJCibGC0XiCwZG1nMO6AyqlFEQqZGN5DRlKhuMxcZq4TMZ/niIgGEi1RkhHrDLadRcKrZT/XNIC2o2880mRGytX0I4dMChn1JI6S+kPekgpmYxjvvEHf0wURrx4/XqxCbIko9tqoazkZ15+hY8+vs/Nj24zHk84PT1kPIrJGgYbGUyWUW7UuXf/gEn8051A9CVr7SMhxBrwTSHE7dkfWmutDxD82PencwdU4DMvNxm4WAp5ILAzN4EnvyegoKYaL6QQmUULg0I5XrvRLvILgTEaazTWSspRRBgoRqMJ1UqFqxcvcXf3ITduf4iUEqUC7j98SLffZTKcME4nbKyvkSYZR0enzDXqLMw1SNKUlaVFh95OYucY0+/zgx/+kDCA55+5wtnNTW7fuUt30OPB/inPXLnMyy9c49WXX8Fozd6jA95+7xb3H+yjAsnx8QmnzVPmF+ZZWV5iMOoRBJZGvcpg6OcgCoEOvOzZXwfwwWBKrnALUIknqdTC1dJS+qxJGKx1p5n0ijg3aCUPxuLHAq/jyyttvZvvNLvIbcgwEEah67gkKXm9Pj01xROnrLutbktIj6yj3RBRq6YBwkqH74QyIE00MldEAmQ6hxawAqJqBRW6NRAnYwcYWut/x2EapSjCpobjk1NUOSDVWcEhySdHGY8nZSYD7QwnbSjRWrsuiAAZSG/I6q+jna5XLQS6pFxwFgISi9SambiHUopAhbTaHYbDIZNJQqVScwQn/35loBAIrl65Sm1xjnK9wS9//et8/Td/l+OTNmurK8zPz7G6vEq31+Xk5JilxWWWl57wA37i8WcOAtbaR/7/x0KIrwOfBY7y+QNCiDPA8Z/2HFJKgjAkTZJpVojDq5SeSZ88sDPNFz3A4vmSVnmVodVOpqt9O8or5QLpLuDCwjI6gzjNePPG+9zb22NpbhmtLZMkRghJp9snTmImkzFWQGKdV36oAhr1GnsPDxhPJly5dAFjYDxKmGvUufT0U2RZysf3djk+PuLtd94nCiMWF+f4+dd+jrm5OkkSc35rm4vnL3DxymWCMKTdPGU0/t+58cFtN4jTWnrjPt1xn0enB6yvrnB+8yxhGHF4dAxMaawFUUrlp/80/S/uU867l6LIpoQQBFISKEkKZLjgITONzKW1gYf3tS16/RRpvOuBK4+Aezk/WrrNLKWgXCqRZU5jUFIhSZa5CUN5VydP633pl5sQmNyBxHoKsbGulFHuA2uMs0ILBFYLlHaEHZumDjMInBjMaEeHltaglCAQyndNrGcXCurVOksL8xhreHxyTJbpJzj40ubW9r7FF0iqlTJSKfp5mq0892CWpC9wg23zNqG2iNB1R0ymnfuVmCkT0pSHH37I0aNHNBYX+crP/izPP/MMURgVgLbJNNVKFVWKCGtVnr/2LN/5/vfY2T7PfGOZjbU1/rWvvMbmxhrfff11Or0+cRzzzJWnPnH//XnMHZDW2r7/9y8A/zXwa8DfAP6u//+v/ulPBOUoJI2nQpJcEOG+dhJMZx7izyUf6fOWV66cKkwjrXWcau2K4CAMUUoSSsVzTz/jxjwNenz3zR8xHo3p0KPT6WCM4cXrz/Pic9d467136Q1C+sMeSinqlRpKKsajCbmhRrlUZXMj4sHuQ3rdLtXLl1CyzvJyD6yh3Wtz2m6iwoDlpVWeuXyNa9eeRgpJICXVSo1AKarb5/jqaz/L7/zeH3L33gOGvT6UJEYaAhRWG1aXV5AqRCQ3HLElkMWGtsa6KT+hJ1vlJ7Rx9BUrBCrzLD+Zn3CikOAaTwX2YaRguWkFMgrc5sp5+YFyi9hfeB3gT8M8EzPIQNCo17DWMolTqlFIKQzpjZwlWBGfcg6CccWx8EFdaN/6zDMAz+hzk3dy7NcUisCcOFsEQ7+z0izFaigFEZWw5AatSokbX+jG0sdxQqnkFHudbo9W1nHP5uua2enSgZREYcDF8zusr63xxrvv0ul03fvLfGHjeQYqV7T6NQ6ABhNnM7yImU1gDXE8JhWC5Y0zXL18mTMb66SThDRJiUolhJRE9ZrXuliGI6cj+Ytf/Spbm1sgLGtrK1RLEfcfrrO5sUYQRvT6Pz3G4Drwdd++CIB/aq39HSHED4F/IYT494Fd4Jf+tCd50hraXxL75NdFNLVTCWmuM5B579oHAot1k3SMAw2tNFRrFTZWV2k2u8w35nn1pescHB5y8+OPGA0GTOIhVgtkauh0eszV61y9dJl2r8Odj+/QPWlyL77L4eI88XjCZDSkVKrwzvs3ObOxztkzWxweH3N82mJxYZ4oDDlzZoPNjXVevP48/f6A4+YJjx4fcf7ceeq1KuVqRK/dI8tSVtaXefmll7m48xRvvfkWIsmwmcJW3RF+1Dzh9scPikMzi5R35hEFecZa3CntWDJT+m7GlOSSM+Ks77MbTZblsx5tATjm90CmGQg75WN4UVGejFl8QM7bgVIirKRULlMqRV7DbkmzjChQhEqSZH7L2ulmde/HuxH77kKh1jSO9ZcPnLUYchtta5wpjAkFKraOoRe4D28iX1IYQalUplapEAYxUinSJHYGrmHIcDhmf/8xnW6X/nCYJ5mIvDjJcSQgTTTlqMziwgrlUpUoCN11kNYDfB6EVtbNxvRJj+sIWOREu6yFogIqsgFjBSoqs7C6xvzCAqnR7rDRBh1rwigiKEfFXjk9fMwP33iTlcU1VpaXKJdDHh0ckCYNhjqlXq2is4ynnjrP46OTT9x/f9bhI/eA6z/h+03cuPJ/xSeikA8XaexM/foEOCOm358CBDMPCUL7Feotn4yFQbPDXm+EKlfZPzjg4cYK9WqVpbl5Dh4/5tz2NnP1BvsPD+j1+9z+6GMuXrjAXL3O1tYWjVqDzBquX3uOVqvFD9/4IQvrDS4+tY21MI5HCAWjyZDSyF3WySRhbm6BrY0tGldqDAYjFuYXqFYjGg0n4kniMfv7+7RbbU5bLY6bx47mErrxWNZYMpHRHw54/8P3EUL6dFoULDdPqnOXJrXuG0oWp6cQTHn+5KWTcF0Tt/rcwpXCi2lc6l+QdiYaU/GAoGfkST8WW/g2mgvQTjCEb/lO4gSjDdpastSQ5Rx7pqVKMTTUYwgoCnt2kfsT+Ptqfa+uUqthM80knVC0jaSEEqixQSTeRUq78q1croKVRFGZ+fl5TlpNkkyTppq5RoVGrUS326HXb6G1xk1e8zoUgMwZqcpAQqIZ9ia0Wl1anZYfK+aC69QIxfCEb4X/j8iDnp0SK4uPLl3KkMYJ/U6HZr3G3fsPuHvvAVcvXaIchWRJShAppFK0Tk74tV//LZrtNleeusxw2Gdvv8fvf/tbnN1c5+L5C8w15plrzKGkZPvMmU/cfp8OxiBMx1Mz3fTFz/L/eMBAFIpMWyDcObIsDczikFJbZArYhISUclDm7r1d2p0WF86fo9MbsHVmiysXLxJPxjy0lm7X0YCXllZYWpijUq7w3Oevoo1gaWGBzTMb3L57F1TA9uZZWq0O3X6fjbV11tfWOD4+5ei4SbfbJwor7GxfYHNjg5PTJkYHlCtV6gvzCCBJJrS6bX73D77FOzducGf3LmFFkY2yolgMhMsGlNXEOsuFbOT91FC5STNFTNSOHitwwijyoR/aee3kvf9ZJmCBGQiKFTpbfskETNn9XOVONtnMNB//CFVApVolVIrxZIIbgOMCUOJHngtmX1c+4QdR3HDhP9/M9/IOhdaGMAiRoxGmHHig12EaYX5tHD2QerXGzvlzaGPQGibJiE6nSxAGzM01aNTrAMTZiEmSIQNBgCLR+QAVV5pInOdDWFaYzHLv4X1QBmM1YRQgBMTjzOOnbkqQDkBmFJPWrRBkgSAwhiAnDjGtdBQWazWhVCwtLFKv1TFWFHTryXBEPB7S6bV59/0b/O7v/SEf3bvPwnyD5599lvPb2xwendLtDkGHvHJ9kZdfeIFKvUq1UfuEnfdpCQICd3r46Dubyua9Z3AXTJI7/3g1oe/zuj+zU7zJZwyOeCQIo5qbdBsG1OZq7D58xDiZUK80WFxYoNXu8MGHHzHsj5FZhpWCdrvF+qpDVfv9IZcvXaHb6xFPYq48dZGP7tznzXduIgUMxxPicczHwV06nRbjJCbLMqqlEq1WiyQZs7u3z9HpCTvbWzTmGgSBot0eYg1EkeTR0R4n7VNnjDErWMOlmAqBFE7Mkqauts49/cTsBhYUqL/L/r0jjx/GIawbl164CuWHcY7QF3jMlPvvRqy73ykyESVcHezbj9V6jeWlBbIkYTSZECeJwxyk93gQTgNSsDmNl/Tmegbyt2Kn79nCbNiQxpBNxuhSBGHozVBmug0F0ce9xXGSMkm1oyoL4xSWqaFcDpivN1hZXiYzGUkywuoMrTUZTCm+AmwoUZFCKcnW9hnnCnz/Dkni8IUsSZ0BjM/KjMclnJWY8SUSU9HYDMnKX11nPurLKiUtl3bOc/25p6mVI6IoQAWKdqfJ/qMDvvWd7/L6W29zf3ePdqfL48MTms0Om2c2qFYrrCwvI5Vrz549t4Wq+MnHn/D4VAQBYy1BqAgjRRabAg/IU6iiVQiFgcVUXy4KuYEVAi2moKF7uD50VImo1esESrGyNEejVmMyidk6c4bFhQVOWiecPbPBg+wRSTdBZob9vYdooxmPJzQ7XZLUyYt39x662XOjEXfu3HG24pkmGQ1mApEgLJU4bZ7wjW/+Pl957Wc5t32WwWDIN7/1RxwcnhIGij/67rdpt5sEgXPRlfga2G/ESEpvmZ+TnQSZR+Nl4PzXPFt2CqRq49SOchpFZh1uLGAiiczw/fTc9WZGFOSDbeanDwuDy6ikRRvjjDyhCECVSoWFxTmGwwGDSYzQllIYEGtvHe71Ama23IM/Af6ImR/l6X9BvBdgA6cDkFojQ4USlkz5TEdJTGjBA6ACTZqOuX/3HkEYUa6WGY2GCCFIxilpahhNYmqVClsbZ3mMKwXTLClqe4QgCgIqlYqDQ6Sjlr/47DMkaco7N29hjNcR5O3OfIFKnElK/nkL0PrJ9V+Qo61rdadZQm/QxxhDrValXKvQ7fX4wZs/4u69+xwdN1EiZHlhmUFvzHHziHa7ycHJMevLy/T6PTrdLue2t2g2T2jMNdjf2//E/fepCAJYl7YqqchsOl0EOVI8c9Fm/eHz0y8fkglPklYKpFsbBu0eg8GYs5sbrC6tcH57k/1HR7zw3LOkWcYHH35Ip9f15JEAaxOyzLD34CFKKOJE0+8NWV1ZIk01k3hCpVJh1GqRTcZIa5lpZSOwqDBkdX2d3rDNr/3mb/LcM9f40hdeJSqH3L57k3Ob67z4wlO8f2NCt9dja3Od+7t3UdoyjF2DvqoCxlY7sZtw47zclCKBkhLtp+hK4aymC4/AfFN5OrUVuIEUXksPYIKcW+EW+yxFWPg2owmY9ug9KilwubcVgkqtSlSKiMKAdq9HlmVkmXvvkVRuIAhgvTDEzg7BmN3xxT13PwiCAOUNQNI0ne4tn+op5ejeGdqtEf/+rHATjXKwWFqNzkZoOSbWE6SARr3GpD/k8cN99vadzVm9ViUMvKuVBaVc798IhQhCMm2olKqUyyWuXHyKSxcusHdwwK2P7jAeTwrtQP65ntjoduYfwl1z7aXPKnWtTgSgQmQYMB6MuXfnDqetFkma0u91ef2HP+Bf/Mr/gzGSL3/us7z2xc+zu7/HG2+/y29/s0W/06Z9dMSo32dhYZnhcMw7799gfW2ZpYVFKpXyj++64vGpCAJSCkIp6Xtp5lSR7f6bG4UUa2Ym2LqsVfgT1IKQoAKs1a5dqBSVRp1SVCaQAWmmHYdcW3eiWojjMaPJmGa7CxqqCw2SOKFRK6OTjDAqo5Qk0ylHJ8fEScL8XIPJOEZKidB5qiWmSsYoIiyVWF6a56/+pV+kWon4/W99l/dv3uJzr77K1UvrXNjaQknJQmOB3/7mN1meX+Tc9jZ3794nqoYkmSazFpta133OmNxwAAAgAElEQVRSlkQblzkFLoWuhaoIiNrAJMv8LAV/tXLgDVHIbN1188WX3+CF4s+fS0KLYjy5Ubi2m/Y4Q+DSivm5OZCC8SRmOB476zRjKXkQKx5nTlHouQk2H/Wdp9n5/S+mFedpnEWnGUZLFucbSCHo9Pu+JQgY6UoirZ1oyN92gXufOsRZtQu8u7TDHWyaUapV3RwGq1lcmGdhecnJrq1GW7c2crGZFc6DMY5jKpUKi4vz/LV/6y9xfmuTucY8nf6QxJvDzD584oZSwhmc5GtVet5L7pVp/AGGA8RFGFCem8cmMctLy2xubnPz9sf8T//g7/O9199AyoC//lf/Cl/8mVeZjIc8fGRZmKuztrbMeDxCJwnZJKF5cMBoNCIKQ45OnJ7l7Jm1T9x/n4ogkD/KYUBsM4TOngCbZh+zpULuP5C3CZ2Axe0GaywyChwRKQhYXVmm0+3R63Y5Oj5le/MMr33x8ywtzvO9N95wKWKSUS2Veer8ObqDAVLCaBSzurKMzjS7j/YZdXsoCzpJ0Tojw6Wh0lIMn7C49PiF69d47plnOLO2w1e+9Hm++uXX+PXf+T2Ojh6zs71OuaTodHpYY1henueNt99hMBiRm6IYY71Dse+IeTsuN87bohRkxpAk2jsCy6lpphLkrjq5zwKz0t480E5jhHsY69LYSLrFmYIqwrEpiFmlaplSOeLktOWEWakmDJ3eXmvtDUU8j0EJ7/NvpqCfnAYqk2chRrgRY15uXCpHKKmQUnhDUTcYdDKYMMkMKowg9SIyK11bzlcOJnDli7SuznYTpiyTSUypFPLKqy/zhVc/S5Zqbt7+gNNmk6PTQ0cyE+7aW+sHnWQwGk7Qi5YLOzt8/uUXub+3x2nrZObi2QLbwqtUddGRmb3EPmM1LsPSwnVuwEKakI7HWCHZPzjkj7/9HU6bp3zn+99nNJpw/blnWFtZRgoneFtoNEiSMWEkCaLQWdipADNJqFdrlKKIW7c/ZHF+gcOjnMX/Jx+fiiBgraU7GFGvVtBak2ZTyiUwBWiYqfWlr8HMTE0lfMXr+6smM4TlEmlquH9vl2w0QUUR7U6fNIPBeESn1+bDu/eIgpCFxhzbm2cYDIcc7O4hyyEba+sszDVcn/tAMk4zdCDIJgM3dsrfe1nAVy4Fj0cT+p0xpyd93r1xk+2tM7z88vP85a/9Io/299ne3KReb6C14MM7d6mUKjTqNY5ap67GdFeGGRdvQBBKhQwExkKcuVRfKOVOa2Mcc82nTdJKr640+ZssrlNOGTaZeSKNtYopIok/9aUkCpSX6kIkBHP1OuNR7E74xKX/WZYhpZwOEc39/BIzTdlndoTI71dxXy1SOi5BGIRUShXK5RL98Yg0SYiiMpVShSzVZLEbDCoNWGkJZUBmDVZnvgQXWJmDyO65baYJSyFf+Nzn+Ot/5d8mCiP++Hvf59btj+kNujlDiiBU7hCRgmq1io41gQz53CsvUatWaXX7hCpgMhpRq1ScPsEPkikcj57Y/tMM1uDKFQcZAoFEaHf9rLFkaYYIAvYe3Ocf/6P/DRNK6rUGr7z4Ai9ee87Z0TXqbG6ssbi4yMNH+/zovffItNONa5NRW5xnY2OFpaUG9cYlFuYbvH/r/U/cf5+KIODaTc5vrqjpzewlzNN+pu0t8Aw3iitsxdQ3UFgQOiMbjpGVCJPEBCbDTgy9ToeDo8fc+uhD9vb2mZ+bY211iaPTJgeHRwRKkU1iBILJJGEwHPH48JB2uwWRk9AGpcD1hlPtTiP7pN+eTmJuvfcOo/GAzbNrGCNIU8vW9jm2zm8jVcTdj++w+3CXWq3M8vICz1y9wtu33ufR8MgFOQ+CEbiLYqQz0tTGoJSkrCRxahzFVttC2iqlAOXktFabIlDkkmmE9Omus9B2KzAvDxyQpfwuNViktGQ6Raf+b4WjGsf5nALhvSBS10+3QR61XXYmjHHj4SQQOLGY1aYA+3IKswoUYaBQwvv3aUunNyZOJhjtRqL1BjFGOLzCpqkb9ZWB8ZwBofCzARwXQghnAZ8Zg9WWUEWsLq0jZcB4Mubx0SG9wcBRy/1gVqHcNOJatc7Lz7/I4sI8Vy9d4q987WtsrK1xenrKL//6b/C73/oWvWHfGSobCdKNUM+7O090uvJopJwBi8WRtWTmtBECdxhmcYz2Y9hUqCiXSpQrZRYX51hbXWYUxzTbHZaWFoiigOvXnuX1t9/lww/voaSiWq0TxzEPDx5RrVb5mVde4uKFHQIluf3+jZ+4/T4VQSBHdqWUGJtbec+0vfx3CoH79DvuX8JOAWfLlEdgwZqM1HvBg0vbx70er7/xFtValV5/xPzcAu12j26zg8QioxBZrSKM5fTxEe1um3gycVrn3K/cQBRKCCVJkpFOIMiDkpAEtSqqVMJKyaODI9546z2MFuycP8v6mRXKUcjW9llKEhaXFpBS8d5776KzhO+9+SZvvneTOJsZBoLvxhmLVBAoQUlIRCAYZZn3D8jraheMco894UsD6/mrwlAsUuHBNASOWWgtSEEYKQc0ZhadmuJ+OPAt87PuBHgQ0DnreqBrxmNwts5XqVMF5hZbwjoWoFUSJRWhdMM6g0AhlKI/HjEZT5CZI/BISzEHUXhgoWAcCjcR2OYgqL9uOnSj3ZVSoDOYxBwfnzIYjmhUy86kMzPoTCMVLC8uYTG02x2y2NHCv/QzX+bqxUusr51lcXmBWq1GtRqxtryEMYbBcIgSAuOtkMMgIJPGWZv79zLbdjWh+xxS+y6Gr9ascCVfqVpmvlFnbn7OHZDakGUZ40nMaDjk+PiEC+e26feG7B8cMRpPkFKRZClnz67zmRde4Z1bN3ju2av8wmt/ASEyLl/Y4Z/903/yE/ffpyIIuIejZmpjnaWStJDoQvJabP0CJXb1n7QzHm7GBwC/F93swSlrUPosITCCZNBnMhxQrjdQgaA76BP4XqqWgiAqEZZLJJMxSTJxKjEhnBTBOqdcpaa9eOdX4AOTMeg4QaeGx/uPaLf6vPPuLb599Wm+8LlXeO1Ln+Xa9ecohyXObG4S1hpgDc8//yLndi7wlS9/yH/z9/5HvvejHxHHGRjDysoSO1vbPHi0z2mziRSCYd7Dt04DIAP3efGbFUuhQHPKPUtunmG1mVKB80xKAt4sJPFjwQ3TAOCaA65WHscxURigx7E/0O20PCrmOniALVBYqyH178nO4BIpRefClTgaGURksSZNM5QxhQu18CercHvNgX7KT4/yGYAyOaXcUaBVqslwmFGQajJh+eDmLcKwjNWamzduw2gMRiMrFerleTKT0o7bDHpt3nn7PebnlqhXq2ysrnB8dESWJSw2Fnnm0mWwlt1JzJmNDcbjMe1uD51plLCE5RBtNNK3MI3OpzRRXFO8NUL+qFQrPHXlIue3zjIeT9h7dMhco8r25lm2zmxw/flrrG+sIVTAe7dv8f033+TShR1WV9bY2z9krlHjS5/7DJ+5/hJv33iTL3/lC0gVEI/Hn7jzPjVBQGhDOQzIopDJKEaEQYG3FI886Bu34WeHvuV2TdLaYnKRy3Id8howNSPRgUeMrWASJ+wfHgIU4BHW1Y/jdIANyFc/KgCT3zUriBM3yUdltihNcoddaQ0qsOhkQj+N6XaOeXjvQz648Q5379zh1ZdfZnV5geevXeHMlqLcqFFfWaW+vEqpVOfla9dJkoSP7t7j9LhJMk45t7nFxsoKb926yePTY/KwY3z9KqTPqooNS8F5d7p8R+f1uCNSTwe0FMFBuXLG5GCVu+IUFFoPfiVpwtLcPKPBqDiNc0Wj8IFAWtDCOpJQ3vOXnkFnnX+BAGxmyGyMDJ1bz2CYTLGWQpPgrr0QrluRc0FMIJFWFDLozMuZpZ5mkTI1RGhXKtiM5uNdfv/xAdoYjBCoMECqCKSk2+u5cqNcRlrodjt0O220gYcHR9TrFdqdJvcfPWJ5cYmnL1+h1x9TjWpsrK7D7i6j8YQsS6nVqhw3m57h6XwHtJhOOLLWSYyVcVfXBIqgUsZaycHjUzc92ho2N9a5/NQOL1x7lueuP0djYZ5+u0WnO+Dmhx9Tq1X4hde+yn/+n/4tojBgYWmBRq1G8MuS3d09rjx7lahS+sS996kJAmDoDifUShGTOHGnv2dWzfZdhZAQCkSqi02eTygSOIWclVMOrJHOXCTMspnfz08VgTEZWktUqNycw8yANchy6ByHU42VEqkcSUcq6dFrS6o1WaoJE6959zRbGSiWz26ytLLC8eExmZ85MGiesHvnJv/H/gP+2S//Mp955QX+o7/5N/hypUx5NCaJnQPvjZvvc3Fni7NnVrn/cJdvv/4GH328y80PPmR1ZRms09JnOkXnzjQC54xk84GeAiEMxmiEkL6z4JkqBucYjN+EQhQqF6Et1ognOgi24BK4ayZwbjrtdhcllZP15m4+duZeWTdZygqL9htSaZe5aZkTorwATAus0dO2mRSFs5zxBqh56WJ9sHJv0ekrfhxAygd/5D6ExgqUpywbCZkyiAyiICSoVqnV3ElfiiKG4wmjao1Ot4fJMj746GPqje/w+c++yhc++wr9Xo/9RwdUyhHWWM6sr7H/6JA4mbC8uMSZ9ZAkjml1exg/NTgntmKd0CjPnpRf31o5/KV70qTb7FCu11hfX6VSKnNy2uTw+BipXKlkraXeqPOv//xrvHvzNrc/vkOtUuGZF65Qq9ZcIEbw7/6Nf4dk/MlmIvnj0xEE8hvoQS9rpgsONa3vXPsFN03WQbAF3lI0DbRDWXWAuxh+gnHhCi9nXtAaAm3RA4coCyXAjxzXvQGUJEoo5zXv+3SBEkSRG5ON9Z0uL9LJ77MUkvFgzF5nl1GvQ1StOmDOeH98Y4gnA+q1KlIJeoMR3//hO9y5u0eppAgCzdOXr3DxwjlanQ6vvPgKv/qbv8mvfeObHBwdEqcpaZY6G7Uc1zOOOJNbiot8swiw1rjOQV4m+JIpZwcW5B/wfo+i8AwopkJL8IwlhHDpbWI1c/MNJv0hqcmcks566Y2YukSRGQhc2074Ei5IbXEfBP5256i5xZ2OPmsoFI6FXt/xHYzyvXwxHXOOfy7/YYr1ZRAFgQgj/HxFgck0OkmIFhfY2dnh8oUL9Pp9fvCjN+kPhmSThEd7e0gZEIVlsJKHDx/wzvsf8MyVS7z0wrOcObNJpXyLbnfA0uI8m2fOsH12k//zV77uWILeDq1eqQAw8h4VWkmkNUVgU1YQ1SrU5hsszM2zsrTEaBzTanfYe3TAnTv3WFtZ4ezOOZQQ1OsNLu08xdvv3uDOvfukSYotW890tNTmalQbVb+1ZlPqJx+fjiCAW4yOIeZvllTOkVYIPx2IIlUXdkZKPH0GDyT6dNx4HoE/cTTOmLMwxcSJi3xVXSyyIoU0jqTjBCru+VUgndutcbVuYFzanA+ZzPnfOsvoN08RuNQvHY+dys3PtSONAcOtm7fZe3TK4sIpb79/g1/51V8n0xnXn3uao5NTzmxssriwysUdw9raGUpRRL/Xg0B5fYxA5eQaf48D5RD2cZK4XvdsnQ4FCq99Pxw8YCjz+4B7nzAtFfwXUgUOA9Han2yWUTqhXC8TaEuWZGRxgkzTgtmXA5ZSu3JL+/umfHtvWmrYIqObUpwduUZkthg/lkdaVxK6KKxDLx0PcvDIfWbtyztpQEuJtbIIBCLz5QSGslIszM0RBSGPD49ptttobUjGY9JhByElp6fHfOMPfo9O+4TTdpNmq0Wn0wdCXnnhJVYWHUh4fHLCcDghHiesriyzu7ePwGUclXKVOMlPZhe4klC5oBsoVBCxurbG9uYmjVqN0WjC0UmTOE7p9oectvsMRhlZrJHliM3tHf7aL/0Sw3HK4nzD8Rsy56KVX8AsdmY4Kvzpuw3/mR9WSbS1VEsBMgqxQhBGEVZrN3POp012Jro7c1E/QYYpIFio4/zf4E8fYafTcvJT0nh7bZEJdOjaVzKzhUWWSVNs4DaREZbMgNWaIHAgZm5rlgOUriz2DjSu2MaazGc3+fcsKqpy1Gryf339N3j0+IQfvvE6ew/u8czzV1lZmSOKAnb3HhGogOPTU05OvbBIupSwUauiAsVwPMbojHIYYjJDmvmBmrmxqHBGGCJH1IXAaO+8NJNgWQDpTD3Q2tGw84TM5tbebp5h0U2wkPppS4FUBKFEiQiT6ukUI39N8unSOSNRK9eukB5fKYxPc7wHCmUjuVCq6JS41mbePRJGzASxPLtw7U4rDCKzSJO3Q80TJq0iUGjg9LTJXqXK8vIixmSc29okHQ14NOqgLdRrFZZXllhYqBEnY5YXFjk8OeY73/8h8405zm2dY2N9nceHB/zuH/wBw8mYpy9f5p0bt0gmCVZktFptDIYwcKzYzLeTK7UqWxsbdHtDxqOEIAg4d3aTdrfLOJ4QRSWeuXqV1fUN6vNzRLUKQagIyvD0tcv8rf/4P+RHb73LvY/u8sL1F1AE5EFAa8OkN6ax1PjEvff/OwgIIbaBf4wzFrHA37fW/j0hxH8F/AdATqf6L621v/UvfULrwSuf3ivlqKGhKjEYTSDJ3PjyHGTJSUI2R4rdQpAzNE3h8QLpJ+bkIpz8tCmyCyGecNnN/fmlcT773kXOpciB8sCYduPFg7xEocg0xPQjTa9X/l6F5/drTTwa88YPvsPt27dI+h3mF+f4C6/9HF/+/OeJk4TxZMijx4+5cesWH3z8AVEo0aFiYb7B+a1t1laW2T884PD4BGst8SR205M8BC2FwzLCICAKAxCC4SQmzVmF+YkrXJZCTpf1piTOK8CbeFjQqTcDDbwqzhnqYa0lzVKy1A1dlZUIG6cobab2WRZUCsIbEhqRS5V9OZWL63xEEljXxsz9E2cCAExxB5dWA8oNNsE6SrBV/kYI58Po8Ak9DTBCIEoRtUaDpcXFIkPa3jrLcDjm7v1dBsMJICHLMHHKxZ0d6rU6N08/pHl0zOLaCh989BEvXb/Gzs45BIqjo1OuPfscy4uLnDSbXLyww97+I+dCrTNCqQikKoRVURiytrTM2vIKaWo4Pm3SanXQ5w1rKysIK2jMLfDS9Re5fOEpqtVyUeVordFpxtLSHFcuXiDzfofFfQXCKODg4Sn9UecTt96fJRPIgP/MWvuWEKIBvCmE+Kb/2f9grf1v/788mTAGYyRZlhHkpIlMMTaulxyUAtDOfiwfPJrvuMJJyJ/shbDIX6185PWTgjXrRUdM+Qh+MU5LDPdFXlpICybTjogj/QyDwogS8okUsngF/9l+/LNiIUuQKNfqax2DzZibP8dTO5eYn1ui1+swzEbsPnxEpVJh68wZmp2m0/Aj6PWHJElKrVbj6YtzfHj/HhZDFKmihNLaGXkkaVbU2s6yW0xrRJHXi35DSl9TSgqGYG7rxfRP3GsAUigX2PxzGCwyksig5EqDfAoPTBmL+Gua1/sFRmOLYJ1/1zIFCS3e6y9vg+YX2Wc4oXQzFbXWPoswxXphplLI94mSAWEQMR4nlEoRlUqFOE5pNtvcf/CA05OjIpC1To7YfbBPmmnGcUwcj13qLeH1H75NqEKM0fzx977Hz3/5i5zf2uLRwWMubp+nFIS0eh2OT5ueO+DET6Uw5OKFHS4/tcPeo8ekWcrCfB1jMu7tPmR9ddU5NJVLgGFxYY56teoON2tpH51wfHTiOQ4hUkqyNEVFARaDkBKtMzqdNv2HP4UWobcUf+z/3RdCfAB88sCzP/Xhbre1lvE4IYoCAhuQJBk2SR3LLBSIYNaAIj9yZ7zarJfCWuEBQoPxo8qUbxnlHYcp38A/l69bwZ1M1j9zMYorwM208gCb9Mw6a21hb22FLBZt7tRjjUVq45BpAa7P7nGLvE9kQQjJo92H/MN/+M/52tf+DTY3ltjd2+OkeUStWmNpcZHVpWXK4YjeYMRpu4kUksvnd5CRYjwak0wS8kEuBmek6Q9DMmN8m9Cj037Sh7GG2Ua1NdNWozPymwlh+WY01vERlPcE9ICdzT0MrKZULqMCRTqJMZP0ifQor4pcU8PXADyZORUv6cs3LZ2zUbH3c8BSO/m1axPaIpMsRtZ77AJfQkgvosJassmYltFEpRLra6ukWcbD/QN2d3dpn5z6DoovYbKE99/6Ebc+qmKUuywmzajUarz1+vf5+NZN1jc3MFjK5RICy8nJMfuHhwiheObyFZS6y8lJE2OhUa8zPzfP888+w7mzmywvLnLn3gPuP3yEsfDo8JhHh8dcv/Y8v/DZn2F+fo7HR4csrS4QmQgTa46P2xwdt5nEIzbWNwjDiDTRwMTRrKsRhwf73Lr9IQsLC5+4+/5cMAEhxA7wEvA68EXgPxFC/HvAj3DZwp8YQfbk3AFvECKsG1edpJSrZcJQoSd+FBQWSoHrbec3OM81tS0WgOsoTCWrMs//PfDk/s80iFhvujmzSvMMwQpbAFkYP6i84AGAzQzSM+Sc7bc/xSKF9PPvrGfW5VNv8jE7VlC4KAuLEyXFI370g29xeLDHq5/7LEfHR4SlkH/zL75Es9VE4BR7P3r3PXrDAWmccdrqUK5ESKsw44JV4wJeIAlCSRQ4QC8zwncBbNEoyCnYBX4Cnlrs0A2vwMFa60ZZCci8Zbjba14Y5K+pY306MlEpDKnVa6R27JBr8g3o32aezOX3ceb+uL3rv87vTN49yIVjPnOzwqJ9IJGeLm1mSzORl4Ci+MDu1jswOAwCjDEcHh4RJymdZhMTT1A+EDosw/X1JpMRQTmiVC7TajZRoxFYQ6fbotU94Qtf+FnOn9tmaXGB1ZVl6rUq7XaPLDPM1+c4u7FBFEWcnraYq89xdn2Dne1z7GydwxjBnft7tNpdl+przWm7SZomSAFHx8csHSwwGAww2tKoz7M4v8i7N27Q7fVYWlpyXgT9LpNJwmQ85I+/+x0e7j9mbeVPjP4oHn8eswjrwP8N/C1rbU8I8T8Dfwd33f8O8N8Bf/PH/2527oBQyuJPUaUEWQZPtDQs6CQDJdxcNuWifuFSI3Mii5jOG0y14/XnC85YyNsxQmCkW3Q5y9D4do327RyJZ9CJ6eLDmOl4amMQ2qH/JvC/mDshz1BbpRBFWuo/ShEAcqCyyGiURGHY3/2YR3u7xCbhmWsvcGZtg8sXzrNzbps//M73CMOQ565cpdXu0Gx10O2MTOvCF7CIe8YgrCzsCPPN7AKA2wlCSBcw/cFq7cyGtj5g+BPVGOMUfdLba+eKRH+NJa6EcNWDnzYUCqJGBUaSJI5dwJ4mcS4gkqf7PjBKpuWd51647g0eq5lyBorAjxtMgnLrqMjwKCgQU/4C+fu12CxGjwRjFZDECcPhkHF/UJTV1kon+gkjKvPz1LwicnF5gSRO6Q2HBGoejaUaRrz88ktcungJJQylqIRAEccx7XaX1mmXzbUzXLl0kTfeeptWu0uvP6RSLnPvwUNOT1sIIWg2WywsNHj66ad4+upTaKuJ44y5uUVanT6Hx02iMGJn+xwLi3Osra5wcPgYKRVZlrhZDEpyfHLM8XGXwWDMubM/JXsxIUSICwD/xFr7K7jFdTTz838A/Ma/0pNZ606pQLiZfEIwmcQFci2Nrwmt87C3TDNVN1/eLSMzI7sTgRfRWNzcduNLBDvtKCAl1rPqLHkLaVrNW5Hr7F0tb1KNtdZNLrZgQgWhQiIwxjgDYGOdsk45VuJsKzNf6LkQqsBwil+Q3oUqIwpDqtUKRyennD/3ImfWz/D+rdssLyxx8fwOO9uG1996k4f7+z47MtNhHl4glBqDTVKUlK4kyFmC4AOmv34+ZbZ5eWJnz2A8BmKwRiJ9gLPk0t0ZFMV4ZyV/vybJhEyFhJWQAIMZJTxht53DAf76uqzLYxI4FaPMXLYmtSP92MBnEMYFdx356ylBG+0zjpkypnAzmsqWXehx6X42GdGNE0QUOffiLHUZR3FP8t+EnXPn2VxfJQxDhuMxp+0W9Xodqy3zjTm2z27z4ME+1UqJ03aX927cJIljmq0W4/6AtyYxp+0Op60Wxhi6vT79/pDxZMKD/T063S7zc3VefOEaL1x7nsXFVdrdEcNRxtLSErQhTRPqtToL8/NEJUm1WqFcqoIVJLEnh4mA8Tih0ai7QFMJP3Hr/Vm6AwL4X4EPrLX//cz3z8yMIPvLwE+WLs0+8rXmVXKBb1WZzOTSgcI3bhbYcYWlBwkL11p3w43wSDfudBOBQKoAm2pH8Q1kAW45qrtb+dJKN7LazlqbTAODpwy49xSI6SkkXJ2sAoW0kMUZRrshHnklm+MM+CyggDWK15lZr9JSn5vn2nNXUf9ve2cWY1lynOcv8ix3qX3rvZuzT3OapIYURVISQYE2JFG0YVqAH2TAsh5s2A8WYD/4QbZe9GgbsAEbEAx4ESAbhgUYtmwZIi3ZJCWRAw7JITkznK33rbq79rpVdddzTmb4ITPPvT2aFklRw+rGVADdVXXr1L15TmZGRvzxR0SakuctGlnKY+97H1/8kxd49fW3aLdaNPMmjWaTsiiwIzsecVQ8VqkqSyXRHKjnKYzJj9sE4M8GJaqhD4AxEkqa2RqqszEyM+Hc1+8c5kED3uBdvBFVYsnzFCqHK0qvkKPbElyuOnwrvnmICr5WX2Iw1URDFVVcwCPSyocjSU1t4sfBRKtI7FjZysSsRkIVIeXajUYT100sS1VcWWLLijzLuXD+PEVZ0OsPOHPqFHNzcwyHBadPnmQ07POdV77D2sYmG9ubmEQoiiGlLTh3+jTzs3NsbG34StTT06zevsvvrn+BvJnRajZZWV5icWGRZ585z/LiMQajkr2DHfIsp6isT06qKlSF9c1N311LYH5+nnazSWKMj6YZYXlpic7uHvm5NnMzCw/cfj+MJfDTwC8D3xWRl8Nr/xT4myLyfHiGN4C//329m1JnurVamcff4ikhjMkn8VomXIaQyFe4QU8AACAASURBVCOhqq664OdrCG8FNLx0le8vn6QBIXehLVk8/RTNExCDjDyqXTfcFBPMz5AjL4zLboXwmk8wCqdxbqDyyqFmtYY1nASMY+Ks8oor3qsqzfYUJ0+f5oPPfYDjx08wGI4QlMfOnePppx7nldfe5Mq1a54g4nxrLVc/o2AGu0k3xGtPidgF1ARMfw8a2IZ44DKEMiXWLVZo5TllVfl6+OG1CcL2WPOYhNgaDghMZesLbuYJWlbeHSNQglPxG1UVl0qNlUSlpUl4NhowlPDeEp9XsAi0dCFhaRIMmCAaTSq+OB+BAzE+WOIqG7sb3pVwjHo97t5bZ6fT5bFzZzh5IsNHZHLu3tvg2MoxWo2E2Zk2o2pIZ3+X584/w5XrN9jtdHjuwgXe/8zTXLt5ky//yQusr22wduceg/6AZrvBydOnmZ2eA00Y9qsA+pVgDFk6YjgasbS4QKvRQMDzDpwlSxOajSaN5hyNvIkjYXdvm6IcsLKywvGV40xNtR649X6Y6MBX4f51HOR7cwLeScSz3fI8Jc9zOge9cYplNMedTw7SsHqFsOEjYSeTOrzl+3EEJF7GQJBqAM+sYAqfVOKn3IfItKy8MjFjHz8mJ/lDPOxWFz5bAgah1OW7YpprkvqBaAgauuCTS+UJLHEH1soubCyjjjRvMDs3z41bdzi+csK38jJNHj93jr/3K3+Lr3zt6/y33/vfbO9s0+nsY21F7LysUBcNJYwtSiK1A+KbswaTnvAsDRL69fnXnPMWkiihxFp4LqIBuKPe/N6jkLC5ovUkAXj1rkIaAOCxTzJ2BVDqMGVkD0448NHI86nBZVAymW9lLYWr2YAOHbdMi883jepKPLXZ1Q6IdxHCR0VQ0oV1pOENjHNQFHR2d/nWK9+l2xtw4vgxlpcWaDVTzp455TdzPsezzzzH7KwvZpskKUmScm9tjaos2dvbIzUJH7pwnrV7a2zt7FJVJaODLltr67RmZkEMb168RN5oYK3SbrdZXJxnenqKLDVMHTtOq5lTVZXvfoTSaDSZm1tgejpneanJxtYq12/c4PSps0iizM5OP3DrPTSMwcwY2lmGVaU3GlHF1tBxkWiNLRPt8biO1O8wnPUNIowJTTRs7ekHjnm4uNIa1DJVsDJC6p0JxTlcImju47kEgpKoN411YsHWrsKEOlTre9+KCHliyEI6b2kdBQ6b+IVsSp+w48JJJRqXJRRFQVEUbO9s8sZbb9Fqt5mdmePc42d56uknWVlcxpYlL7/xOldv3GT17j0GvR5EOrUP4teRDsD76eKToAT1xTtCZl7hAhX4bThA3Q9CCV19qS2Dsb08/ogYfosnqbzt2cSrYIIKbrwCrP38aGFETyya7aIk4jEJrfypbxKDVtYzQmGcjKTqFW0AGutspERCtGa88SPJyxlTWx0SLMNaKwRGJAK7nQ637qyy3z1gbWOGJx9/Hx/98IcY9AdsbG2DCKNhwVOPP8HiwgLtdpuiKphu+cpZb1y6xIcuXODsmdNcunqNWzdX2bo3otftYhoNjh07xqgc4KiwVUmzKTg3hTpLI89ot5o4Z+kPe1RlxagY+f4EiWGq3WJlcQF1yurdNWam5zi2cpxGI3/g3ns4lIAIU80mpbP0Y376RGZYLMZQVxpGiS2t61MkXOzrvocgmIBgAt89bHwTfA712j6xDhsLc4ZTXlASFJuktfMYF2Xk28dFrBCSRHzH2MQIZWVxQQlUBjI82KdhXBo49NYo6dBOoNZjF8EkKa1Wk/PPPAUYbq3e4fyzTzETmpY8+cQTfObTn2ZpcYEfu/ABvvmdl/naN75Bvz8IcXNqZeeS+kGFugoh9TgoRYEAYMZnFq2eQPElVNvRsfVyP/EqYAwCtaoWkOg6RbamjMcRN3itsITanVHia/HBSD2uzPiqSEUSCohUdtwsNVgeJowzKmmX+W5JdeTBP+wJdUeIGFFnmdblwY1BTMr09BQLC/P0RyWtZoOF+XmGoyHbuyXvf/YpFubneeapJ7i3vs7W1g4bmwOOLS9jHZw5dRZjEp598rHQITvl7toaZ0+fQgx0Onvs7HRQ6zAqNPMWMVP05KkVZufmAKHVbDA7M40Y2NndZmd311c6xlBWjsGwYHFunoW5aZrNBlmas7WzyxNPVDQbj0AqsTHCcFB68925yVIBtT8tY28ulMoOaiCAXt4gGIMJcSEpQpoYGnmKFUPaTukedL1rEcJ3EhB1dPwZUvnW0S4kpozj11qfPAoe2AobSu0Yq/BsXGVY+SzFyvm0WgnZbyTgsgB6TXDtvensq9xubnVot9rAPnmWIRjKYkSWZTz3wQucet9ZylHBB8+fJzEJf/SVr1IVRX2qmlDGS2Xs/1pVEgxpMIeima7JmIzjIrkpEa/EEkNVWWwENiYU79gH8bOlqn9qo0XPIbZPqv3tqGzkfsp1XRPBhbyPREjER1tcEfoPBjfD040hhjX9XAXF4RFKjzsYfCPXcAC83UDR4MZEurk1kKYJ7ak2K8dWOL6ywtb2Lq1Wk0YjI00NYgwL8wsszC9w+sQpmnmDfm/IrdU77Ox2WFleYjAqWF/fIk9TlhbnmZ2ZZWZmlqefeoKqqrClcuPKdahKurtb3KwsxiQ00pT94ycCPnScUydOkiaG4WDA+sYGu50Oqgnnzpyj1WyzsbXBmxcv0Wql7HZ2ef+z5xmOCjY2tzh5/F3kCfxFSWS3ZWGxvZMoUVOPTU6ZWIsQKggR4+V+5zqnNPKUqTyjIKHdnqIoLdWoRENRTA0maSyN5V8M+d5aOyHUAe34RUJES/HhNzc2L1FQo1R+pdfhN1eflOByb+HIyIYOwoKTlCzPeOzsWdLU8Mprr3P+6Wd48evfYmNjmyceO8383CyLK8dZOn6ScjAgNwk7nT32uwd89/XXGfQGSOJzKWJevTP++USz31cdCx17FCqCpUJQYMHiyiMfw7rxzRMtMK9cRBUbMQbiaa5jhN55ToUJocvasgq+uwYLJeYQ1Js7EINM9A4caGHr+gUxyUgghEUDtdh4DkhSaj1kY6mzB2Mo9b7EtBD6zLKMJEkpiwIxhpmZGR+JaTRoNZt0uz3W1tc5feokYhIq60/htY1t9vY7bG/vIqKkiTA/N82MdfzxV19gMOzxwfefxySGdmuGRj7D6RPzPP+BD/H5z/8Bvd0dnKvodTYA2FfY2t4kyRoMhud56vEnSUxCmmYMhiPWNjdYnF+i3W7y/qefpNVIGRUlFy9d4d7aXR47+z7S1HFw0OXajesP3HsPhRIQII1Mr5jGGg+Z+mh4G1A0acy5sV/uD2RXo0jBImVUVrjS0pz2ptL83Azb27s+xVdtWMDR5RjnEDjxLaYju80mIeMtXOdPHKkjDMaNr/VgIZ5ia2KsXceU1lB/T1wAqsIJ1Zib4/SZ07RaLYwkbG9v8fkr13jr4kX+0s98isXFzzC3tORDnuooioqissxOz/L8hQtsbGxyrXfD+7/i+wfE+4m4oMNvcl+2wbtClfU70Ab8Q/ywMeLLjUnpSOLGjXMQN3SYLO9aREttPL9Bn4z7KNZ2VHAhAnGq9rbinyu+qEuoxFFbXpWGEgeR2i11ARIJNQVdaaEMb2fHSgu8hRD9fiW4T1ZCTYSAlaigldLvDdh0O1TWsn/QY3dzi72dHWZnfcPP23fu8sZbl2g2c6qyZH1zk6nWNMtLS2RpiqhjOBzR2e/Q6/dYWlqitCVXr99gba1FUVkWllYY9Hq4YoBx1j+IJIMspbQlN65d5eKlc7TabfIsZWNjk5s3bzHoDzh39gyI5cSJZQ66PYpixOqddXr9Aa3GFGdOnaGzu//A/fdwKAERKqe+4KNaT97R8ekxWTzzPj+OoL2dYioNtfbHvIE6/xylsv6ks/2+Tx+1Fucqj+AXNmzcOkeNeoHK+BON836li0ATPuRmNPiTTmtLpU55hnElYCO0Z1qkaUp/MKAsJpuLxtPTK7C5+XnWNtYZjYbMzc3wxsWLmDzh+uodbt6+x8zsLKfaTRQYjUasb27x7VdfY31zm3ZzysfhVeva9rEbbjSinIaGJoB1isXnGsStosFSaJo0Ds+z9RTfkMRQh239cxpjGrVZHxQw4gONiYl83wmeP/gTOeqRGM6LHxqVqXrXKkm8wnKJzwchKGRVfK8Bk5DG1uwTsxnfB5lQiJHhqcFqAawKtqp8cZuyAgfd/X2GxYhhMaQoS0b9LtWgyyvfeYUTZ86QpgnHlxc9uFhZdjsdNrc2WVycw1rLpWvXWL1zh5npaYajilarzdzMDJ39Pd64+Aa93gGtqQZZu41LU6phH1+50oSDxNHd2+Vb3/4Wuwd7TLUbdLs9Nre2GQxGHD92nfm5aWanp9nb3ydNUpqNFpevXueZJ5+i1U4py+KB+++hUAKqykG3j2NsBSj4OHy9QCZtcMKpr/VCIYA5td8eq87EMBXU1X32DvZR69DKkqQ5LrHUjq1OgF5hH9vUf5NWnmsulWA1+JniT3gAFyINpvInbvz7CLg5B0tzi5w+dYJ7G+scdHv0hgNGvVEdkhR8953O3gEnji+TZinbO3s0m74X3vrWNutbm3xs6nlA0MpSFgVZnlGUI964fJm9vT2SPEPLyhdGSUPlHcK9Wa1TAwqUwjnPBvSTUbM081D9d1gXKY2PW0ksWMR3JtKw4WRsAcTtF10DDXwLddHvHudOmMTXO8DamqkoFh++dV7zOPDkq9odk5rW7aKVGGpDVM75pK7gBtUSbiBaJjVYyYTSNh5YjrkMYpQkT2k0cqyrPG06b2DLkvWNdTQRnn3qCd8qTZR799bpHvTY2duhPxjQbJ5mZXmF0ydP8MZbl5mZmmGqNUW17HCuYjQa8VMf/yhLi7N86Ssvcvnqde7duoUrBgji8ziqElXLvbW7jGzJ/PwcU6020+0ZRAzrGxtcvHyVuZlpRkXBVKtNs9GgLCxvXnqL3qiDm0wEe5s8NErA6rhiUE00CaanBD56TLbRyY0fnEdN4iaS0EdOPJfcqqfUpkLazFiYmWVnf4/SudCAw2Aj4OjGBmo8PWqOf8xtt9Q1C6pAbSXx0QESfy8Eay4CXy5UN1IRDnp99va6vO/0WZaXF+js7fPSd16ht9UhwW8OOxyweu0mzz79NPt7fa5eu06SGJLUU0EPDvoYUjbXttnZ3mZ3Z5fbt9dZ39zh7r17VNaStZqUOoRhQWJ1PAZ/e77yrREq8YpLXYjnW0iykONrYahVqFokSOJPcltZrxCrMGdJxAJCnCa6VjEngbH10QjJTDqBvUTswSTjTE4IwKaoJxkJdQptDFO6gMcQaknUqeVhg8dcEVEZV0COWim6dv6TvLJIpE5AQn0F5yxvcPrUSaanp7h7d51er09regbrHEVRkiQpr7/xFltbO8zPzXL9xk3ECKdOLlOUFWXpS4Vvbe5w+9Z1VB3bOx1EhMfOnaLb7/HBCxd4/zMfYHunS6PRQBRWb97AiZBmGWBxmlABuzu7FEUR+j9mLC4usnp3lXaria1WGAwH3C3usLJ8jFarweVrV3nr8lsszC09cP89FEqg3tDBR410Up+cotQlBzUeAsFvj1aCG79R/VZAYvyJHRc3Vn1SSwDqCE0mxsW0vE8IUpNJTEgxlvC5sUFq7SU49RhEEsKARupy6DXL0Y1LkHW3dlkdFqwsL3P21Fk++bGznD55kt//n1+gt9uJqfGUruDFb7yEKUqGezuYrEG/P+Ly5St8qdGkkbY4dmye1Tu3+D9f/DJbW9usrt1mMBwgJoHSoc5CMlZiXql5EpF1UFSeTWlEauwkQWgkaeADKaPCRzZMmgSClE9UqlJfyDMp/f3ZTOoHL0SCUjS3x9aBsxMMTcbYCYRWYQGsi6E8V0+oYO7v+DqWRGquiIbn7d/L1GXIjBu7NH5MYxfARbA5aG0/HAvOYjDkWYNm3iRNUowkPP3k4+R5zqXrNxgOh9xeXeXGrVvMzc2i6lhaXGB6apq9vX1e/ObLdPs9NtbXcEUJ4rh196anctuSoiz50ldeZKrZ5ubtW+RZi4WlJXb2OgwGQxaW5pluN9nZ2afb2acqC4oiw6njYP+AwaBHkmakaUpRFaCOXm/E9s4uChwcHHBr9Q6d+e4Dt9/DoQQAUGIdCA2uo8qEOxA21X0ZaDFm7TyYo+J9b2shaWSeExDSfHGCLSq6+11cZev6dCUxbXa88Op6AyHOHP3UeMD4a2U8DvX05Oi+xDHGcce23xKc2+Kgx8H+Ac1Gi2Mrx/mZn/wkl9+6zre/8XXUKmmziWk02Fm7i1QlRh0UQ7bu3sZkDV7s7rO9s8XHPvI8VVXwxT/+IuVoSKTuWl9MwQ+i7gZEbWprwDJssDxAyU2KMQmxe4fxiCGNhq+xWKjFxX6CwRS3ARyNitIJnu2H9VmA8RmgJIl/f59SHPlXPvoSarHUfQuCGgmrwr9mrQtlGt04TyQqiOAiaIzkCFCFCE1CfYgo1KXgYvKWhrkchycDg7JypIWjKnvcWb3D7tQ0JkkwxnDn9l1OnzvL6RMnuXtvjd3OHqgy7HZJ203SNOOgO+DM6ZO88eZlVu+tU1UWcRWdnT3a09MM+3263T5lWXH16g0EmF2Y5eSJE7SabRYWFkmzfeZmfOHSqekO10ZXqLpdrLOk6rfuwcGAmemEnZ0OqfEu4ezMDJ1Oj7LyyUTNvMnG5vYDd95DpASo/f0xq1Tv+3VdI7BG5ScuiWtd/KZzha13oklSSPHddKKJqupPy8nDpV5bWvvQ0SKxiU9wifa0T3iR8UnltLYEEHxSC2MLJubGYwSyHMWQZQ2qquLS1evs7u0RyldQliOkqjDFCBKfhAPghvvYUc52OWJpd56t7TXmZ6b5+E88z7dfeZX93T1MfBBRWYVT1J94OgYq8Ju8Np/Dw3Qu+uuKMZAZwYqgwxIpffRAU8CJLx+e+oYsokpaAkapssSDtNbPV5Il5M0GVVlRFWUdu5eg2CeVZT3E8HOiYHGezOPun24ghBzetg4URNSzQcVbczbxY1bxGI9abyFEZWDCs/B5GKEakgLO0dvbY7B/QNZoYBEGnT26vR6z83PE2iamKqEoGJYjdhDu3LuLqKEoLZ29DsNBjwSlt9/Booz291FncSbDZBlVMaJfDMjzBmKEPG8wPzePdcru7gGNvMncwqJ3T41vBWcSQ5bn5I2GL8CDr/Vw+85qKNJr/P34DrYP3HYPkRKIJtmEqe1f9id9YPJ53IDxteEyl4b4c/A3Y06BWMWEdN9KKzJJsM7W2EM8RZJgSkbfXeOm520Ls/Y3w8llpC7draEUtz+BfOjKnz7xfXy9Q+Msx5YXSBLHlWtX+IMvf5lbN26QhOiDWoexFTU2EUTDSf5TP/lRfvGvfIa52Rmm223OnjnBQbfHS99+2Z/zE2xLwj3VmyOy9ybNdXxHpSxJQxUxrZnAVvEnyiRi76iTvUDQzCtEE4qNJJXDJQZnfAXp9lSbIhRBrVMn4/P3yKq/Vzt2tSJNWPDzUEXcJVppEQ9IjOc6FFVtxXnSk4AJrmBQhJp4098ELgGV5xGIc2go9oL1rpvKBJGrcog4bFXV1ml/d4dyOKA5O+t5IQoGRxpKmCcm5cWXXmJ+fpZG3kBDdytJUxqNJiPpYl2J5AaHw6IMiwEbW+u1IppqtWk1m4zKEcOiZHZunvbUFAfdLr1eDxHDwvw8czPz9Po9BoM+aZrS6Rz4Tl7G1yGsbPW2lXS/PDRKwKPDE6Z++CpxUfurYMJnrEknEMqAe6ROa96A+C696shIaLTbnD52km73gM3dHaphwQTBEOMUm459aJcEMzp007nvIIWazaZBMWEga+TglHJUeBJR3CjhxHWkLC0uMDc3z5lTZ7h6/Tp7B/uUReH1UUCz6/8VkIAxqDC7OMenP/VJfvInfoKV5UXSLGN5eYVL127y+sXLFMMhVFVdYtygdchS6jcO+Ed8TIgvQV6UJKEwKdZvWmcdGmKJEgk2Gr2GYEmYYBHZcbEPqZTEQNpMMWnK4GDfP7BorgeWZ3g63laRsTvlfzF2KWKTjkgHr63GwtX4QrQobBrMt/g3JhQaCVq8Xl4SDxS8tnOMmaqT/Aepn5g/WMIAqn6fblH4sA+hrL04hr0Drt+8SZYZiqqge9CnwpACrqzY7+xhywJH4jsl5xmnzxzn9MkTpGlOt9dn/6DLcFQgkqAK/cGAmZkZ8izFiNBsNHB5xtTUFAuLc2R5Qrd7QFEUpFlKORxQlb5T8nA0Ik0ehZLjApoLppgwA8SfCJqYus20qTx5xygBxANPF5UxelxPGhjju++cOHac5aVlGlnOmZMn6L36CnvDov4cmwmpbwfgTUi/Y+uNE81IkxpMqb6hRnh90nBJTOLBp7IKpbsnb9LfT5LmdPb2+coLL7K+tU673WTp5DH29g8YjoYIWrevvv8BgZJwauUE87O+Rt3U7Bz7+72QITnB008FU0VK9P3WlUf0vWJwSp1JmYhBK0dhC99MNFheMfnGm+4yLvCSyjh5yAg0Ul8V2sZdamjkeeAIiA8puriJGHskSRI4HRMma+BdxLoLRr07UJOdUj8xUoQzbgLwS4c+SUtcsBDBYxkTroOp4r1JbeHF9OQ4oSqgmUHSUG+g9FiSWK88XJp6N0h9cNs1m2jly6gd9LscXz7G6VMnuHzxKljPWjLVCFd6l8hkhqSR0m41uXD+PE8/+QSdTo/N7R2mp/bp7O3RPehTlpbBoEdVFczMzHoilDoaSQN1yv5Bh2ExoLIVo6IM+ElKVYxAIM8b5FnG6OCd997DoQSEgNoKNpfxKS4TLN2wPlwCmib1ieLnUXw1lcjAq5RYKtuWJQtLS3zouQvMzc7wtZe+RVGWDAYjssxTPnE6bn2lngjjOxgJUmqNMyTWRxJsbnwvgkhIIpwmzjHs++7FseuMWt/9J/YENLaks7XFzu4OMzNNGs2Mv/zTP83N26u88PVvMhwOqW+MqARbOHE4rVhYXuBgMKA9Pc/0/Dx2VGFMytmzZ1hamOfOrdukofaB7/gj9Ul5n9sshCKoQKIkWYpT32DFqkNR8sRQqcdRxBC4F6CW0FUXCBvCZAZpCDRyv/nUhfArVMMi+BxurNiClRfv1aFI4jGACMoq1AVH6rFrBCANWZLgjK8U5Ssja/33aRXWikjdpFY00MoDtnNf4lYsWBJcjLTVIMlS0lCubFQVlKhvNkNMHlPSYAUIiklTTLOJqwpsf8D63bvs7+0z3N3xh4r4DEYPJAskhmarzfTsFMOh5crVu6xvbODUsjA3y8zULP3ekP7wgCzLaLWaVFVJkiY+UmEMg2GP3b0R+wdd5mbniLH0LEmQRoNRMfT4cHYI7oCIfAb413jS7H9Q1X/2Z1ztIbHA0a/NxBBzrwG/eHng+cdOOQT/0bPeIPrSU+1ppqZmWFyYJzUJvV6PtbV1ut3e/eZRPJgSSIrY3kpqvnpUUjX6XwbeOmOswPMIFDU65imowyQpzUaDUydOsrKwwK1bq+TNJn/tsz/PX/25n2VUFPS7Xf7oha/x1W9+y4ctnWITQ+I8ICatFgsz0wxGBVZT2q0p+oOKqoS797a4vbpGI8355Mc/xh/Zkq2NbTT87RgHHPPnJi0Xf0oSLCDnG65G18UpYnx+gRGDq5yv2JT4BUzpamDNhXwPYxLPPFTvhoxsgVTgiipMa6xfED0TX8C1PgjMBABb04S5L2xsHJ7wZJQ8SynVt4mTxI+ZMm5MPzka1gzhXk0V/Yd6z8Qf/bUB38lynytw0DugKqtaSbg0zLmbyJhUYDDAIt5/LB2OIQfdLjHu69QrpejKYoSqsvR7Ja++9iZOLeosrXabyhZeORllbm6KsrSMipI8T30dxzB5WZqhgxHOKt1ul1azQZ4nFGVFu92iKEtsVXjS0QPkXVECIpIAvwn8LLAKfFNEfk9V33jnv9BxJq9Q+/31atGQIx9JKXEVhzJksVptTUBRIDGcf/ZZfvbTn2ZxdobO3h7ffetNZmbnaLba7O93QlcbQ+lsQFEDWQnxxJ9QWciz13y7KKsWkQQjPpxmbTXWTurHbMvCuyiJYWlxiU985MM8fu4c7eYUd55ax1rlwvkP8NRTT1ONhty8cZ3dvQ69QQ+TpqhzlAacFRRD0zqyLGM4HLGxtc7V6zd54rFVOp0DXn7tNW7eukGewY9/6MdYvbtGZ3MXF3sNhDTpMTg5tl5cIMhIGpK2FB9eDOFPa5WpPKVMlFERWZX+S13xuX4B1IpvxY1gSLAjiyZKIinGCRHVux9bCRs1+P++S9A4d0OAKoC00eePNeeqyiPgeeb9GauhkErl38sm43qCEW8wbmzyR6zJhoSjCKiqOsrBgKIY0csy38cglGs3QfOPC8GMx0lVgfGl0ckkKJsa8QiKWKmSFBWDlI6R65EkHsAzkqAI/f6ADascW1nixLHjjEYF99Y3KMuhr8CtSmJ8gdgkTT1xKPdUdOcsrVaLVrOJSQytVoNBX6liD4l3kHfLEvgYcEVVrwGIyO8AnwPeWQk4QojNJxBRx4ujZp64LjG+xXSgsmpE9bW+GhRmZmcpipLLV65w4fzTLCzM8fwHnmOq1Wan0+Gl73ybQb9PGbIIJ/PpjVNPASb48OFU8uMzqDokSRETIg2Jz9BLQkqw0/HpJWqYas3SzKcYFRVV6RfG4uICebvN1Nwsx4sRIr44hlrr691nfhFnQ4vrdxkMpkmzHFsU/OGXvsLm1j7FaERlRywuTrG0uMjuepft3Q4jrTC5D+FFwlWs1BvBsBqYC+nOCEjquQMJ+DLlDjKBYeXieVq/nyhoKl4ZMGmm+Q1SnzxCqPWoNfCqsRQcOk5icsH1qV3DiZRuiY7DmLrsxONHtihxDYNmEoDAeIXULiXWBa4G4ygH0ZITj38kfg3EfpTeI1GKsgjdru4XqS0BgjtDjRvZVHBOyJyrnIOFXwAABbxJREFUlW/ElzQcaL4nhe8baEyGMRnqLEVRUpYVw+EQZyv2Ovv0ej1KW9JqtzzmpKHiE8pw2EdVyfMGo9HIA4NpRrvt8RjVGCr8EVsC+CYktyd+XgU+PnnBn+o7EPxCI4LJpSZ+OKvEjEANSLx4GzV0gHABkTb1TJnK0TQZnb19vvTHL/DCCy9y7MQKw8GAUTXi2LEVWq0mB92e7+8Hkw5iXdkWgguQhYKbCKkY39e+GPmwUDRT4ylCOLUqB8bQSBtcv3mLi1euMd2eotVskmQ5d+6s09s/YGFliROnz/KJj/44v/v7X+DO3bseayjwERP1YcPu/j5a+hZjr7/1Gju7G7SaLfr9IbNzM3z4g89x9eZNbt3yj13Fs/rq5x3DcTA+BRNvoqLe/7eivp1a4qv2GjFYYsffiNUQgRjGdnBtmvlTMrRHN4AxoYlpYvxnOYXU5+lnaUJRhXLpEzTiaJ5HVwC5vySZ8+mDYeOCDCqcJsRiIKby13hQMUGJitrndSgB/3GegowEBRHBQkBywaSB3uzG1kvc1EYhJqvG1mkYD0SKMaFlnowxjcDIjE11ASQxkBj6gx7OQrvVwpfedySJoT8actDr1sk/lY2WT0ZZljhVmuSU1lIUI/Ksga0cjTwjSRLSPKWVtHDWcdB7cIty+bNaFv95RUT+BvAZVf274edfBj6uqr/6gOs3gR6w9Rc+mB+dLPNojx8e/Xt41McP7+49vE9VV97+4rtlCdwBzk78fCa89o6iqisi8pKqfvRdGs+7Lo/6+OHRv4dHffxwOPdgvvclfy75JvC0iDwuIjnwS8DvvUufdSRHciQ/hLwrloCqViLyq8Af4EOEv6Wqr78bn3UkR3IkP5y8azwBVf08P1gPgn/3bo3lRySP+vjh0b+HR338cAj38K4Ag0dyJEfy6Mi7hQkcyZEcySMiR0rgSI7kPS6HrgRE5DMiclFErojIrx32eL5fEZEbIvJdEXlZRF4Kry2KyP8Vkcvh64NbwR6CiMhviciGiLw28do7jlm8/JswL6+KyEcOb+T1WN9p/L8hInfCPLwsIp+d+N0/CeO/KCI/fzijHouInBWRL4vIGyLyuoj8w/D64c7BZArqj/ofPnJwFXgCyIFXgOcOc0w/wNhvAMtve+1fAL8Wvv814J8f9jjfNr5PAR8BXvteYwY+C3wBT977BPD1h3T8vwH843e49rmwnhrA42GdJYc8/pPAR8L3M8ClMM5DnYPDtgTqHANVLYCYY/CoyueA3w7f/zbw1w9xLH9KVPVPgJ23vfygMX8O+E/q5UVgXkRO/mhG+s7ygPE/SD4H/I6qjlT1OnAFv94OTVT1nqp+O3x/ALyJp9gf6hwcthJ4pxyD04c0lh9UFPhDEflWyIMAOK6q98L3a8CDG8A9PPKgMT9Kc/OrwVz+rQkX7KEev4g8BnwY+DqHPAeHrQQeZfmkqn4E+AXgH4jIpyZ/qd6ee6Tir4/imIF/CzwJPA/cA/7l4Q7ne4uITAP/HfhHqnpff7DDmIPDVgI/UI7BwySqeid83QB+F29qrkdzLXzdOLwRft/yoDE/EnOjquuqatW3l/73jE3+h3L8IpLhFcB/UdX/EV4+1Dk4bCXwSOYYiMiUiMzE74GfA17Dj/1XwmW/AvyvwxnhDyQPGvPvAX87INSfAPYmTNaHRt7mI/8ifh7Aj/+XRKQhIo8DTwPf+FGPb1JERID/CLypqv9q4leHOweHiZZOIKCX8Ojtrx/2eL7PMT+BR55fAV6P4waWgC8Cl4H/Bywe9ljfNu7/ijeZS7x/+XceNGY8Iv2bYV6+C3z0IR3/fw7jezVsmpMT1/96GP9F4BcegvF/Em/qvwq8HP599rDn4Ig2fCRH8h6Xw3YHjuRIjuSQ5UgJHMmRvMflSAkcyZG8x+VICRzJkbzH5UgJHMmRvMflSAkcyZG8x+VICRzJkbzH5f8DliWjUep4jYEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# path for landslide \n",
     "#./data/reized/WithLandslide/25_94_Large_cropped_6.TIF\n",
@@ -647,9 +2118,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-12-6c336b480794>:2: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use Model.predict, which supports generators.\n",
+      "Classification Report\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "    Negative       0.57      0.58      0.57       122\n",
+      "    Positive       0.57      0.55      0.56       121\n",
+      "\n",
+      "    accuracy                           0.57       243\n",
+      "   macro avg       0.57      0.57      0.57       243\n",
+      "weighted avg       0.57      0.57      0.57       243\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "test_generator.reset()\n",
     "predictions = model.predict_generator(test_generator, steps=len(test_generator))\n",
@@ -669,9 +2160,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f966c6f5690>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 5760x5760 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print('Confusion Matrix')\n",
     "cm = confusion_matrix(test_generator.classes, y)\n",
@@ -691,10 +2212,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "functools.partial(<function top_k_categorical_accuracy at 0x7fac570aa200>, k=1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(top1_acc)\n",
+    "plt.title('Top accuracy')\n",
+    "plt.plot(params.epoch, params.history['top1_acc'], label='Top-1 accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb b/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb
index 95bbcb4..87b6a1c 100644
--- a/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb
+++ b/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb
@@ -35,8 +35,8 @@
      "output_type": "stream",
      "text": [
       "py 3.7.7\n",
-      "tf 2.2.0\n",
-      "keras 2.3.0-tf\n",
+      "tf 2.3.0\n",
+      "keras 2.4.0\n",
       "mem 128555.12109375\n",
       "cpu 40\n"
      ]
@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "['Fri Sep  4 06:59:06 2020       ',\n",
+       "['Sun Sep  6 03:44:54 2020       ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
        " '| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |',\n",
        " '|-------------------------------+----------------------+----------------------+',\n",
@@ -52,28 +52,28 @@
        " '| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |',\n",
        " '|===============================+======================+======================|',\n",
        " '|   0  Quadro RTX 4000     Off  | 00000000:1D:00.0 Off |                  N/A |',\n",
-       " '| 30%   32C    P8     9W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   50C    P8    15W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
-       " '| 30%   31C    P8     6W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   43C    P8    11W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
-       " '| 30%   30C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   40C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
-       " '| 30%   31C    P8    10W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   45C    P8    18W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
-       " '| 25%   30C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 24%   34C    P5    12W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
-       " '| 20%   20C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   28C    P8     9W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
-       " '| 20%   28C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   32C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
-       " '| 20%   27C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   31C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '                                                                               ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
@@ -264,7 +264,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 976 images belonging to 2 classes.\n",
+      "Found 977 images belonging to 2 classes.\n",
       "Found 243 images belonging to 2 classes.\n"
      ]
     }
@@ -316,13 +316,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "./img_data/resized/Negative/3844923_landsat_8_rgb.tif\n"
+      "./img_data/resized/Positive/3838780_landsat_8_rgb.tif\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f740d463b10>"
+       "<matplotlib.image.AxesImage at 0x7fb6c4e57890>"
       ]
      },
      "execution_count": 4,
@@ -331,7 +331,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -474,7 +474,7 @@
       "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
       "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
       "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
-      "Model: \"model\"\n",
+      "Model: \"functional_1\"\n",
       "__________________________________________________________________________________________________\n",
       "Layer (type)                    Output Shape         Param #     Connected to                     \n",
       "==================================================================================================\n",
@@ -844,7 +844,7 @@
     "\n",
     "    model.compile(optimizer=optimizer,\n",
     "                  loss=loss,\n",
-    "                  metrics=[\"accuracy\"])\n",
+    "                  metrics=[\"accuracy\", \"top_k_categorical_accuracy\", tf.keras.metrics.MeanIoU(num_classes=2)])\n",
     "\n",
     "    model.summary()\n",
     "    # for i, layer in enumerate(model.layers):\n",
@@ -872,1249 +872,738 @@
       "Please use Model.fit, which supports generators.\n",
       "Learning rate:  1e-04\n",
       "Epoch 1/250\n",
+      "WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.data.Iterator.get_next_as_optional()` instead.\n",
       "INFO:tensorflow:batch_all_reduce: 164 all-reduces with algorithm = nccl, num_packs = 1\n",
       "INFO:tensorflow:batch_all_reduce: 164 all-reduces with algorithm = nccl, num_packs = 1\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.8840 - accuracy: 0.4969\n",
-      "Epoch 00001: val_accuracy improved from -inf to 0.56379, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 29s 4s/step - loss: 0.8840 - accuracy: 0.4969 - val_loss: 0.6823 - val_accuracy: 0.5638 - lr: 1.0000e-04\n",
+      "1/7 [===>..........................] - ETA: 0s - loss: 0.9579 - accuracy: 0.5200 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
+      "Instructions for updating:\n",
+      "use `tf.profiler.experimental.stop` instead.\n",
+      "2/7 [=======>......................] - ETA: 2s - loss: 0.9027 - accuracy: 0.5067 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2517s vs `on_train_batch_end` time: 0.5751s). Check your callbacks.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.9008 - accuracy: 0.5087 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00001: val_accuracy improved from -inf to 0.53086, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 25s 4s/step - loss: 0.9008 - accuracy: 0.5087 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7172 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 2/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.7176 - accuracy: 0.6117\n",
-      "Epoch 00002: val_accuracy improved from 0.56379 to 0.57613, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.7176 - accuracy: 0.6117 - val_loss: 0.6878 - val_accuracy: 0.5761 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.7596 - accuracy: 0.6018 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00002: val_accuracy improved from 0.53086 to 0.55556, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.7596 - accuracy: 0.6018 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7029 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 3/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.6583 - accuracy: 0.6506\n",
-      "Epoch 00003: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.6583 - accuracy: 0.6506 - val_loss: 0.7125 - val_accuracy: 0.5391 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.6723 - accuracy: 0.6325 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00003: val_accuracy did not improve from 0.55556\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.6723 - accuracy: 0.6325 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7010 - val_accuracy: 0.5185 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 4/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5778 - accuracy: 0.7111\n",
-      "Epoch 00004: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5778 - accuracy: 0.7111 - val_loss: 0.7584 - val_accuracy: 0.5062 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.6027 - accuracy: 0.6909 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00004: val_accuracy did not improve from 0.55556\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.6027 - accuracy: 0.6909 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7228 - val_accuracy: 0.5144 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 5/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5703 - accuracy: 0.7029\n",
-      "Epoch 00005: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5703 - accuracy: 0.7029 - val_loss: 0.7618 - val_accuracy: 0.5185 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5557 - accuracy: 0.7083 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00005: val_accuracy improved from 0.55556 to 0.56379, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.5557 - accuracy: 0.7083 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7036 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 6/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5017 - accuracy: 0.7623\n",
-      "Epoch 00006: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5017 - accuracy: 0.7623 - val_loss: 0.7213 - val_accuracy: 0.5144 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5393 - accuracy: 0.7329 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00006: val_accuracy did not improve from 0.56379\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5393 - accuracy: 0.7329 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7225 - val_accuracy: 0.4979 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 7/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4817 - accuracy: 0.7633\n",
-      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
-      "\n",
-      "Epoch 00007: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4817 - accuracy: 0.7633 - val_loss: 0.7202 - val_accuracy: 0.5473 - lr: 1.0000e-04\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4988 - accuracy: 0.7574 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00007: val_accuracy improved from 0.56379 to 0.58025, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4988 - accuracy: 0.7574 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7127 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 8/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4416 - accuracy: 0.7848\n",
-      "Epoch 00008: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4416 - accuracy: 0.7848 - val_loss: 0.7294 - val_accuracy: 0.5226 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4578 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00008: val_accuracy did not improve from 0.58025\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4578 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7574 - val_accuracy: 0.5021 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 9/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4365 - accuracy: 0.7982\n",
-      "Epoch 00009: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4365 - accuracy: 0.7982 - val_loss: 0.7206 - val_accuracy: 0.5514 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4561 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00009: val_accuracy improved from 0.58025 to 0.60082, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4561 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7078 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 10/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4622 - accuracy: 0.7828\n",
-      "Epoch 00010: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4622 - accuracy: 0.7828 - val_loss: 0.7285 - val_accuracy: 0.5432 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4322 - accuracy: 0.8004 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00010: val_accuracy did not improve from 0.60082\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4322 - accuracy: 0.8004 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7696 - val_accuracy: 0.5350 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 11/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4217 - accuracy: 0.8125\n",
-      "Epoch 00011: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4217 - accuracy: 0.8125 - val_loss: 0.7168 - val_accuracy: 0.5556 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3810 - accuracy: 0.8229 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00011: val_accuracy did not improve from 0.60082\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3810 - accuracy: 0.8229 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7463 - val_accuracy: 0.5391 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 12/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4217 - accuracy: 0.8135\n",
-      "Epoch 00012: val_accuracy improved from 0.57613 to 0.60082, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.4217 - accuracy: 0.8135 - val_loss: 0.7080 - val_accuracy: 0.6008 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3898 - accuracy: 0.8332 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00012: val_accuracy did not improve from 0.60082\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3898 - accuracy: 0.8332 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7329 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 13/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4051 - accuracy: 0.8125\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3554 - accuracy: 0.8567 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00013: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4051 - accuracy: 0.8125 - val_loss: 0.6941 - val_accuracy: 0.6008 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3554 - accuracy: 0.8567 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7269 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 14/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3902 - accuracy: 0.8299\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3270 - accuracy: 0.8608 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
+      "\n",
       "Epoch 00014: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3902 - accuracy: 0.8299 - val_loss: 0.7317 - val_accuracy: 0.5844 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3270 - accuracy: 0.8608 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7890 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 15/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3942 - accuracy: 0.8289\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3290 - accuracy: 0.8557 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00015: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3942 - accuracy: 0.8289 - val_loss: 0.7435 - val_accuracy: 0.5679 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3290 - accuracy: 0.8557 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7621 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 16/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3917 - accuracy: 0.8371\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2933 - accuracy: 0.8782 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00016: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3917 - accuracy: 0.8371 - val_loss: 0.7178 - val_accuracy: 0.5761 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2933 - accuracy: 0.8782 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7800 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 17/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3561 - accuracy: 0.8689\n",
-      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3225 - accuracy: 0.8741 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00017: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3561 - accuracy: 0.8689 - val_loss: 0.7266 - val_accuracy: 0.5226 - lr: 3.1623e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3225 - accuracy: 0.8741 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7712 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 18/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3558 - accuracy: 0.8473\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2935 - accuracy: 0.8762 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00018: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3558 - accuracy: 0.8473 - val_loss: 0.7349 - val_accuracy: 0.5679 - lr: 1.0000e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2935 - accuracy: 0.8762 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7659 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 19/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8617\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2720 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
+      "\n",
       "Epoch 00019: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3350 - accuracy: 0.8617 - val_loss: 0.7516 - val_accuracy: 0.5720 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2720 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7624 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 20/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3553 - accuracy: 0.8432\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2723 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00020: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3553 - accuracy: 0.8432 - val_loss: 0.7550 - val_accuracy: 0.5556 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2723 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7938 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 21/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3665 - accuracy: 0.8443\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2831 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00021: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3665 - accuracy: 0.8443 - val_loss: 0.7539 - val_accuracy: 0.5597 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2831 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8037 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 22/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3450 - accuracy: 0.8504\n",
-      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2909 - accuracy: 0.8772 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00022: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3450 - accuracy: 0.8504 - val_loss: 0.7437 - val_accuracy: 0.6008 - lr: 1.0000e-05\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2909 - accuracy: 0.8772 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7662 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 23/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8576\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2655 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00023: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3418 - accuracy: 0.8576 - val_loss: 0.7462 - val_accuracy: 0.5844 - lr: 3.1623e-06\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2655 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7987 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 24/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3498 - accuracy: 0.8668\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2466 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
+      "\n",
       "Epoch 00024: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3498 - accuracy: 0.8668 - val_loss: 0.7556 - val_accuracy: 0.5885 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2466 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7649 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-06\n",
       "Epoch 25/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3420 - accuracy: 0.8555\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2553 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00025: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3420 - accuracy: 0.8555 - val_loss: 0.7625 - val_accuracy: 0.5926 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2553 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8101 - val_accuracy: 0.5473 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-06\n",
       "Epoch 26/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3466 - accuracy: 0.8596\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2549 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00026: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3466 - accuracy: 0.8596 - val_loss: 0.7788 - val_accuracy: 0.5597 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2549 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8154 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-06\n",
       "Epoch 27/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3507 - accuracy: 0.8586\n",
-      "Epoch 00027: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2751 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00027: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3507 - accuracy: 0.8586 - val_loss: 0.7723 - val_accuracy: 0.5844 - lr: 3.1623e-06\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2751 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8256 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 28/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8596\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2848 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00028: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3350 - accuracy: 0.8596 - val_loss: 0.7818 - val_accuracy: 0.5844 - lr: 1.0000e-06\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2848 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7880 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 29/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3689 - accuracy: 0.8402\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00029: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
+      "\n",
       "Epoch 00029: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3689 - accuracy: 0.8402 - val_loss: 0.8125 - val_accuracy: 0.5597 - lr: 1.0000e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2691 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8155 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-07\n",
       "Epoch 30/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8576\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00030: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3417 - accuracy: 0.8576 - val_loss: 0.8187 - val_accuracy: 0.5514 - lr: 1.0000e-06\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2677 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8631 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-07\n",
       "Epoch 31/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3539 - accuracy: 0.8555\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2532 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00031: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3539 - accuracy: 0.8555 - val_loss: 0.7862 - val_accuracy: 0.5885 - lr: 1.0000e-06\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2532 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8359 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-07\n",
       "Epoch 32/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3225 - accuracy: 0.8760\n",
-      "Epoch 00032: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2851 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00032: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3225 - accuracy: 0.8760 - val_loss: 0.7727 - val_accuracy: 0.5679 - lr: 1.0000e-06\n",
-      "Learning rate:  3.1622773e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2851 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8274 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 33/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3303 - accuracy: 0.8648\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2681 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00033: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3303 - accuracy: 0.8648 - val_loss: 0.8077 - val_accuracy: 0.5638 - lr: 3.1623e-07\n",
-      "Learning rate:  3.1622773e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2681 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8248 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 34/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3472 - accuracy: 0.8350\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2566 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00034: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
+      "\n",
       "Epoch 00034: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3472 - accuracy: 0.8350 - val_loss: 0.7864 - val_accuracy: 0.5844 - lr: 3.1623e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2566 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8946 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622773e-07\n",
       "Epoch 35/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3492 - accuracy: 0.8566\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2848 - accuracy: 0.8792 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00035: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3492 - accuracy: 0.8566 - val_loss: 0.7581 - val_accuracy: 0.5885 - lr: 3.1623e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2848 - accuracy: 0.8792 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8544 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622773e-07\n",
       "Epoch 36/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8494\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2602 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00036: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3585 - accuracy: 0.8494 - val_loss: 0.8202 - val_accuracy: 0.5556 - lr: 3.1623e-07\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2602 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9250 - val_accuracy: 0.5267 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622773e-07\n",
       "Epoch 37/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3504 - accuracy: 0.8535\n",
-      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00037: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3504 - accuracy: 0.8535 - val_loss: 0.7828 - val_accuracy: 0.5761 - lr: 3.1623e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2454 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00037: val_accuracy improved from 0.60082 to 0.61317, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2454 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8472 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 38/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8566\n",
-      "Epoch 00038: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3443 - accuracy: 0.8566 - val_loss: 0.8223 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2681 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00038: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2681 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8657 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 39/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3502 - accuracy: 0.8545\n",
-      "Epoch 00039: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3502 - accuracy: 0.8545 - val_loss: 0.8064 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2700 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00039: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2700 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8675 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 40/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3374 - accuracy: 0.8566\n",
-      "Epoch 00040: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3374 - accuracy: 0.8566 - val_loss: 0.8015 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2498 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00040: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2498 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9167 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 41/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8535\n",
-      "Epoch 00041: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3417 - accuracy: 0.8535 - val_loss: 0.8394 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2834 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00041: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 20s 3s/step - loss: 0.2834 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8890 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 42/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8658\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2431 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00042: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
-      "Epoch 00042: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3356 - accuracy: 0.8658 - val_loss: 0.7922 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "Epoch 00042: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2431 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8516 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 43/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8596\n",
-      "Epoch 00043: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8596 - val_loss: 0.8201 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2744 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00043: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2744 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9049 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 44/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3276 - accuracy: 0.8648\n",
-      "Epoch 00044: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3276 - accuracy: 0.8648 - val_loss: 0.8857 - val_accuracy: 0.5226 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2485 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00044: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2485 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9200 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 45/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8463\n",
-      "Epoch 00045: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3509 - accuracy: 0.8463 - val_loss: 0.8262 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2621 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00045: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2621 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8810 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 46/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3441 - accuracy: 0.8637\n",
-      "Epoch 00046: val_accuracy improved from 0.60082 to 0.62551, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3441 - accuracy: 0.8637 - val_loss: 0.7952 - val_accuracy: 0.6255 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2686 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00046: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2686 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9243 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 47/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3377 - accuracy: 0.8566\n",
-      "Epoch 00047: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3377 - accuracy: 0.8566 - val_loss: 0.8026 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00047: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00047: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2483 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8165 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 48/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3397 - accuracy: 0.8689\n",
-      "Epoch 00048: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3397 - accuracy: 0.8689 - val_loss: 0.7874 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2606 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00048: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2606 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8416 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 49/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3257 - accuracy: 0.8719\n",
-      "Epoch 00049: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3257 - accuracy: 0.8719 - val_loss: 0.8117 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2521 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00049: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2521 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8861 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 50/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3692 - accuracy: 0.8371\n",
-      "Epoch 00050: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3692 - accuracy: 0.8371 - val_loss: 0.8433 - val_accuracy: 0.5432 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2644 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00050: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2644 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8572 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 51/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3274 - accuracy: 0.8699\n",
-      "Epoch 00051: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00051: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3274 - accuracy: 0.8699 - val_loss: 0.8842 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2575 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00051: val_accuracy improved from 0.61317 to 0.62551, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2575 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9291 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 52/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3373 - accuracy: 0.8596\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2404 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00052: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3373 - accuracy: 0.8596 - val_loss: 0.8531 - val_accuracy: 0.5309 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2404 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8885 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 53/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.8525\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2611 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00053: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3467 - accuracy: 0.8525 - val_loss: 0.8459 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2611 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8284 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 54/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8443\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2821 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00054: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3509 - accuracy: 0.8443 - val_loss: 0.8005 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2821 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8625 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 55/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3343 - accuracy: 0.8689\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00055: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3343 - accuracy: 0.8689 - val_loss: 0.8281 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2465 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8588 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 56/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3468 - accuracy: 0.8637\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2387 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00056: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
       "Epoch 00056: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3468 - accuracy: 0.8637 - val_loss: 0.8438 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2387 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8780 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 57/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3403 - accuracy: 0.8627\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00057: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3403 - accuracy: 0.8627 - val_loss: 0.8118 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2561 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8342 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 58/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3415 - accuracy: 0.8494\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2373 - accuracy: 0.9181 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00058: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3415 - accuracy: 0.8494 - val_loss: 0.8626 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2373 - accuracy: 0.9181 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8639 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 59/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.8576\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2502 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00059: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 16s 2s/step - loss: 0.3380 - accuracy: 0.8576 - val_loss: 0.8285 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2502 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9016 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 60/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3292 - accuracy: 0.8709\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2665 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00060: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3292 - accuracy: 0.8709 - val_loss: 0.8302 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2665 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8504 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 61/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3455 - accuracy: 0.8586\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2498 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00061: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
       "Epoch 00061: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3455 - accuracy: 0.8586 - val_loss: 0.8335 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2498 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8339 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 62/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3670 - accuracy: 0.8381\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2538 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00062: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3670 - accuracy: 0.8381 - val_loss: 0.8174 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2538 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8285 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 63/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.8678\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00063: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 15s 2s/step - loss: 0.3401 - accuracy: 0.8678 - val_loss: 0.8149 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2561 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8794 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 64/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3288 - accuracy: 0.8648\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2461 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00064: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3288 - accuracy: 0.8648 - val_loss: 0.8413 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2461 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8995 - val_accuracy: 0.5473 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 65/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8699\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2630 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00065: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8699 - val_loss: 0.8298 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2630 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9266 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 66/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3437 - accuracy: 0.8566\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2652 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00066: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
       "Epoch 00066: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3437 - accuracy: 0.8566 - val_loss: 0.8331 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2652 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9469 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 67/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3523 - accuracy: 0.8453\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2535 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00067: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3523 - accuracy: 0.8453 - val_loss: 0.8204 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2535 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8645 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 68/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3448 - accuracy: 0.8607\n",
-      "Epoch 00068: val_accuracy improved from 0.62551 to 0.62963, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3448 - accuracy: 0.8607 - val_loss: 0.8085 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2710 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00068: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2710 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8442 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 69/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8535\n",
-      "Epoch 00069: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3509 - accuracy: 0.8535 - val_loss: 0.8605 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2524 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00069: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2524 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8830 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 70/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8648\n",
-      "Epoch 00070: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3356 - accuracy: 0.8648 - val_loss: 0.8139 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2496 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00070: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2496 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8736 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 71/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8678\n",
-      "Epoch 00071: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8678 - val_loss: 0.8081 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2547 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00071: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00071: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2547 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9161 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 72/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3324 - accuracy: 0.8607\n",
-      "Epoch 00072: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3324 - accuracy: 0.8607 - val_loss: 0.7934 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2770 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00072: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.2770 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8405 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 73/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3378 - accuracy: 0.8637\n",
-      "Epoch 00073: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00073: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3378 - accuracy: 0.8637 - val_loss: 0.7900 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2588 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00073: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2588 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8699 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 74/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3243 - accuracy: 0.8740\n",
-      "Epoch 00074: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3243 - accuracy: 0.8740 - val_loss: 0.8318 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2712 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00074: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2712 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9141 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 75/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3292 - accuracy: 0.8627\n",
-      "Epoch 00075: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3292 - accuracy: 0.8627 - val_loss: 0.8205 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2478 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00075: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2478 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9195 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 76/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8791\n",
-      "Epoch 00076: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8791 - val_loss: 0.8387 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2534 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00076: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00076: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2534 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8758 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 77/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3494 - accuracy: 0.8586\n",
-      "Epoch 00077: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3494 - accuracy: 0.8586 - val_loss: 0.8618 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2490 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00077: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2490 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8574 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 78/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3365 - accuracy: 0.8545\n",
-      "Epoch 00078: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00078: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3365 - accuracy: 0.8545 - val_loss: 0.7830 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2579 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00078: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2579 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8573 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 79/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3340 - accuracy: 0.8617\n",
-      "Epoch 00079: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3340 - accuracy: 0.8617 - val_loss: 0.8720 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2362 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00079: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2362 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8933 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 80/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.2953 - accuracy: 0.8996\n",
-      "Epoch 00080: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.2953 - accuracy: 0.8996 - val_loss: 0.8033 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2506 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00080: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2506 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8688 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 81/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3548 - accuracy: 0.8576\n",
-      "Epoch 00081: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3548 - accuracy: 0.8576 - val_loss: 0.8233 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2462 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00081: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00081: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2462 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9496 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 82/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3332 - accuracy: 0.8535\n",
-      "Epoch 00082: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3332 - accuracy: 0.8535 - val_loss: 0.8475 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2467 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00082: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2467 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9060 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 83/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3520 - accuracy: 0.8566\n",
-      "Epoch 00083: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00083: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3520 - accuracy: 0.8566 - val_loss: 0.7635 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2609 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00083: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2609 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8878 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 84/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3538 - accuracy: 0.8422\n",
-      "Epoch 00084: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3538 - accuracy: 0.8422 - val_loss: 0.8541 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2333 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00084: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2333 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8473 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 85/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3264 - accuracy: 0.8576\n",
-      "Epoch 00085: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3264 - accuracy: 0.8576 - val_loss: 0.8340 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2428 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00085: val_accuracy improved from 0.62551 to 0.67490, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.2428 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7651 - val_accuracy: 0.6749 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 86/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3362 - accuracy: 0.8566\n",
-      "Epoch 00086: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3362 - accuracy: 0.8566 - val_loss: 0.8032 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2787 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00086: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2787 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8720 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 87/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3799 - accuracy: 0.8258\n",
-      "Epoch 00087: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3799 - accuracy: 0.8258 - val_loss: 0.8162 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2600 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00087: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2600 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9121 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 88/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8412\n",
-      "Epoch 00088: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00088: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3360 - accuracy: 0.8412 - val_loss: 0.8562 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2557 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00088: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2557 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8430 - val_accuracy: 0.6584 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 89/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3454 - accuracy: 0.8566\n",
-      "Epoch 00089: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3454 - accuracy: 0.8566 - val_loss: 0.8215 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00089: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2465 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9231 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 90/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3404 - accuracy: 0.8607\n",
-      "Epoch 00090: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3404 - accuracy: 0.8607 - val_loss: 0.7802 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2845 - accuracy: 0.8813 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00090: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00090: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2845 - accuracy: 0.8813 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8547 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 91/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3394 - accuracy: 0.8545\n",
-      "Epoch 00091: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3394 - accuracy: 0.8545 - val_loss: 0.8864 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2529 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00091: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2529 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8629 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 92/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3339 - accuracy: 0.8627\n",
-      "Epoch 00092: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3339 - accuracy: 0.8627 - val_loss: 0.7640 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2735 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00092: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2735 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8381 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 93/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3519 - accuracy: 0.8514\n",
-      "Epoch 00093: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00093: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3519 - accuracy: 0.8514 - val_loss: 0.8168 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2427 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00093: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2427 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8898 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 94/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3547 - accuracy: 0.8555\n",
-      "Epoch 00094: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3547 - accuracy: 0.8555 - val_loss: 0.8271 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2581 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00094: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2581 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8540 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 95/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8555\n",
-      "Epoch 00095: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3443 - accuracy: 0.8555 - val_loss: 0.8949 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2364 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00095: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00095: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2364 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8577 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 96/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3225 - accuracy: 0.8678\n",
-      "Epoch 00096: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3225 - accuracy: 0.8678 - val_loss: 0.8717 - val_accuracy: 0.5391 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2671 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00096: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2671 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8913 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 97/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3499 - accuracy: 0.8504\n",
-      "Epoch 00097: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3499 - accuracy: 0.8504 - val_loss: 0.7670 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2411 - accuracy: 0.9130 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00097: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2411 - accuracy: 0.9130 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8336 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 98/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3553 - accuracy: 0.8453\n",
-      "Epoch 00098: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00098: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3553 - accuracy: 0.8453 - val_loss: 0.8293 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2513 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00098: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2513 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8305 - val_accuracy: 0.6379 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 99/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3542 - accuracy: 0.8525\n",
-      "Epoch 00099: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3542 - accuracy: 0.8525 - val_loss: 0.7910 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2472 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00099: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2472 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8513 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 100/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3505 - accuracy: 0.8494\n",
-      "Epoch 00100: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3505 - accuracy: 0.8494 - val_loss: 0.8450 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2588 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00100: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00100: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2588 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8374 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 101/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8617\n",
-      "Epoch 00101: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8617 - val_loss: 0.8090 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00101: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2677 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8531 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 102/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3257 - accuracy: 0.8627\n",
-      "Epoch 00102: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3257 - accuracy: 0.8627 - val_loss: 0.8293 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2635 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00102: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2635 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8723 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 103/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3474 - accuracy: 0.8627\n",
-      "Epoch 00103: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00103: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3474 - accuracy: 0.8627 - val_loss: 0.8259 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2688 - accuracy: 0.8936 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00103: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.2688 - accuracy: 0.8936 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8420 - val_accuracy: 0.6543 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 104/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3433 - accuracy: 0.8535\n",
-      "Epoch 00104: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3433 - accuracy: 0.8535 - val_loss: 0.7913 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2489 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00104: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2489 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9124 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 105/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3361 - accuracy: 0.8525\n",
-      "Epoch 00105: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3361 - accuracy: 0.8525 - val_loss: 0.8182 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2649 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00105: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00105: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2649 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9089 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 106/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3295 - accuracy: 0.8607\n",
-      "Epoch 00106: val_accuracy improved from 0.62963 to 0.64609, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3295 - accuracy: 0.8607 - val_loss: 0.7872 - val_accuracy: 0.6461 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2654 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00106: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2654 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8723 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 107/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3345 - accuracy: 0.8514\n",
-      "Epoch 00107: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3345 - accuracy: 0.8514 - val_loss: 0.7980 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2536 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00107: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2536 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9203 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 108/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3518 - accuracy: 0.8484\n",
-      "Epoch 00108: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3518 - accuracy: 0.8484 - val_loss: 0.8269 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2511 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00108: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2511 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8540 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 109/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3631 - accuracy: 0.8463\n",
-      "Epoch 00109: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3631 - accuracy: 0.8463 - val_loss: 0.7967 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2493 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00109: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2493 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8497 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 110/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3256 - accuracy: 0.8811\n",
-      "Epoch 00110: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3256 - accuracy: 0.8811 - val_loss: 0.7855 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2351 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00110: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00110: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2351 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8849 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 111/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3424 - accuracy: 0.8596\n",
-      "Epoch 00111: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00111: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3424 - accuracy: 0.8596 - val_loss: 0.8009 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2592 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00111: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2592 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8466 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 112/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3269 - accuracy: 0.8545\n",
-      "Epoch 00112: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3269 - accuracy: 0.8545 - val_loss: 0.8064 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2705 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00112: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2705 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9399 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 113/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3537 - accuracy: 0.8473\n",
-      "Epoch 00113: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3537 - accuracy: 0.8473 - val_loss: 0.8147 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2710 - accuracy: 0.8864 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00113: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2710 - accuracy: 0.8864 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8628 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 114/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3551 - accuracy: 0.8453\n",
-      "Epoch 00114: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3551 - accuracy: 0.8453 - val_loss: 0.8217 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2526 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00114: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2526 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9157 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 115/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8740\n",
-      "Epoch 00115: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3360 - accuracy: 0.8740 - val_loss: 0.8076 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2657 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00115: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00115: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2657 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8460 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 116/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.8494\n",
-      "Epoch 00116: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00116: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3467 - accuracy: 0.8494 - val_loss: 0.8377 - val_accuracy: 0.5514 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2513 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00116: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2513 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9235 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 117/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3400 - accuracy: 0.8576\n",
-      "Epoch 00117: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3400 - accuracy: 0.8576 - val_loss: 0.7742 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2696 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00117: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2696 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8443 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 118/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3250 - accuracy: 0.8689\n",
-      "Epoch 00118: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3250 - accuracy: 0.8689 - val_loss: 0.8126 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2493 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00118: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2493 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8471 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 119/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3344 - accuracy: 0.8658\n",
-      "Epoch 00119: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3344 - accuracy: 0.8658 - val_loss: 0.8570 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2434 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00119: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2434 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8549 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 120/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3520 - accuracy: 0.8525\n",
-      "Epoch 00120: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3520 - accuracy: 0.8525 - val_loss: 0.8003 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2296 - accuracy: 0.9222 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00120: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00120: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2296 - accuracy: 0.9222 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8200 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 121/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8596\n",
-      "Epoch 00121: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00121: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8596 - val_loss: 0.8445 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2687 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00121: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2687 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9070 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 122/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3434 - accuracy: 0.8514\n",
-      "Epoch 00122: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3434 - accuracy: 0.8514 - val_loss: 0.8889 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2371 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00122: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2371 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8041 - val_accuracy: 0.6379 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 123/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3447 - accuracy: 0.8627\n",
-      "Epoch 00123: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3447 - accuracy: 0.8627 - val_loss: 0.8424 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2795 - accuracy: 0.8802 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00123: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2795 - accuracy: 0.8802 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8686 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 124/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3465 - accuracy: 0.8494\n",
-      "Epoch 00124: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3465 - accuracy: 0.8494 - val_loss: 0.7666 - val_accuracy: 0.6255 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2578 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00124: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2578 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8294 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 125/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3563 - accuracy: 0.8535\n",
-      "Epoch 00125: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3563 - accuracy: 0.8535 - val_loss: 0.7835 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00125: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00125: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2691 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8685 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 126/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3456 - accuracy: 0.8596\n",
-      "Epoch 00126: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00126: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3456 - accuracy: 0.8596 - val_loss: 0.8848 - val_accuracy: 0.5432 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2678 - accuracy: 0.9069 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00126: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2678 - accuracy: 0.9069 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9145 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 127/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3229 - accuracy: 0.8678\n",
-      "Epoch 00127: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3229 - accuracy: 0.8678 - val_loss: 0.7758 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2786 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00127: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2786 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8757 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 128/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3519 - accuracy: 0.8555\n",
-      "Epoch 00128: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3519 - accuracy: 0.8555 - val_loss: 0.8406 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2633 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00128: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2633 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8155 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 129/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8637\n",
-      "Epoch 00129: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3350 - accuracy: 0.8637 - val_loss: 0.8846 - val_accuracy: 0.5103 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2695 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00129: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2695 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8042 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 130/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3508 - accuracy: 0.8494\n",
-      "Epoch 00130: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3508 - accuracy: 0.8494 - val_loss: 0.7418 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.9161 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00130: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00130: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2287 - accuracy: 0.9161 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8985 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 131/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3476 - accuracy: 0.8443\n",
-      "Epoch 00131: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00131: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3476 - accuracy: 0.8443 - val_loss: 0.8043 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2657 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00131: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2657 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9389 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 132/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8586\n",
-      "Epoch 00132: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8586 - val_loss: 0.7864 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2565 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00132: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2565 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8627 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 133/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3460 - accuracy: 0.8637\n",
-      "Epoch 00133: val_accuracy improved from 0.64609 to 0.65021, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3460 - accuracy: 0.8637 - val_loss: 0.7554 - val_accuracy: 0.6502 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2487 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00133: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2487 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8624 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 134/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3703 - accuracy: 0.8289\n",
-      "Epoch 00134: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3703 - accuracy: 0.8289 - val_loss: 0.8519 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2576 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00134: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2576 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8436 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 135/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3421 - accuracy: 0.8545\n",
-      "Epoch 00135: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3421 - accuracy: 0.8545 - val_loss: 0.8661 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 136/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3481 - accuracy: 0.8607\n",
-      "Epoch 00136: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3481 - accuracy: 0.8607 - val_loss: 0.7809 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 137/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3487 - accuracy: 0.8566\n",
-      "Epoch 00137: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3487 - accuracy: 0.8566 - val_loss: 0.7846 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 138/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3199 - accuracy: 0.8719\n",
-      "Epoch 00138: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00138: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3199 - accuracy: 0.8719 - val_loss: 0.8131 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 139/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3505 - accuracy: 0.8525\n",
-      "Epoch 00139: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3505 - accuracy: 0.8525 - val_loss: 0.7995 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 140/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3507 - accuracy: 0.8463\n",
-      "Epoch 00140: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3507 - accuracy: 0.8463 - val_loss: 0.8360 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 141/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8668\n",
-      "Epoch 00141: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3314 - accuracy: 0.8668 - val_loss: 0.8132 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 142/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3645 - accuracy: 0.8463\n",
-      "Epoch 00142: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3645 - accuracy: 0.8463 - val_loss: 0.7943 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 143/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3629 - accuracy: 0.8361\n",
-      "Epoch 00143: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00143: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3629 - accuracy: 0.8361 - val_loss: 0.8071 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 144/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3274 - accuracy: 0.8719\n",
-      "Epoch 00144: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3274 - accuracy: 0.8719 - val_loss: 0.7615 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 145/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3324 - accuracy: 0.8689\n",
-      "Epoch 00145: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3324 - accuracy: 0.8689 - val_loss: 0.8242 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 146/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.8617\n",
-      "Epoch 00146: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3401 - accuracy: 0.8617 - val_loss: 0.8306 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 147/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3069 - accuracy: 0.8760\n",
-      "Epoch 00147: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3069 - accuracy: 0.8760 - val_loss: 0.8724 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 148/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3335 - accuracy: 0.8514\n",
-      "Epoch 00148: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00148: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3335 - accuracy: 0.8514 - val_loss: 0.8213 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 149/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8576\n",
-      "Epoch 00149: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3585 - accuracy: 0.8576 - val_loss: 0.8113 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 150/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3130 - accuracy: 0.8842\n",
-      "Epoch 00150: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3130 - accuracy: 0.8842 - val_loss: 0.7828 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 151/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3313 - accuracy: 0.8699\n",
-      "Epoch 00151: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3313 - accuracy: 0.8699 - val_loss: 0.7748 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 152/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3463 - accuracy: 0.8576\n",
-      "Epoch 00152: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3463 - accuracy: 0.8576 - val_loss: 0.8466 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 153/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3499 - accuracy: 0.8514\n",
-      "Epoch 00153: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00153: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3499 - accuracy: 0.8514 - val_loss: 0.7810 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 154/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3410 - accuracy: 0.8658\n",
-      "Epoch 00154: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3410 - accuracy: 0.8658 - val_loss: 0.8127 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 155/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3354 - accuracy: 0.8596\n",
-      "Epoch 00155: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3354 - accuracy: 0.8596 - val_loss: 0.8085 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 156/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3388 - accuracy: 0.8576\n",
-      "Epoch 00156: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3388 - accuracy: 0.8576 - val_loss: 0.8126 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 157/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8617\n",
-      "Epoch 00157: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3356 - accuracy: 0.8617 - val_loss: 0.7967 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 158/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8525\n",
-      "Epoch 00158: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00158: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3418 - accuracy: 0.8525 - val_loss: 0.8095 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 159/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3320 - accuracy: 0.8596\n",
-      "Epoch 00159: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3320 - accuracy: 0.8596 - val_loss: 0.8208 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 160/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3282 - accuracy: 0.8627\n",
-      "Epoch 00160: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3282 - accuracy: 0.8627 - val_loss: 0.7915 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 161/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3433 - accuracy: 0.8586\n",
-      "Epoch 00161: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3433 - accuracy: 0.8586 - val_loss: 0.8482 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 162/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3383 - accuracy: 0.8566\n",
-      "Epoch 00162: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3383 - accuracy: 0.8566 - val_loss: 0.8515 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 163/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3337 - accuracy: 0.8586\n",
-      "Epoch 00163: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00163: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3337 - accuracy: 0.8586 - val_loss: 0.8165 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 164/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3628 - accuracy: 0.8381\n",
-      "Epoch 00164: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3628 - accuracy: 0.8381 - val_loss: 0.8404 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 165/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3454 - accuracy: 0.8514\n",
-      "Epoch 00165: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3454 - accuracy: 0.8514 - val_loss: 0.7863 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 166/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3599 - accuracy: 0.8432\n",
-      "Epoch 00166: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3599 - accuracy: 0.8432 - val_loss: 0.8026 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 167/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3413 - accuracy: 0.8617\n",
-      "Epoch 00167: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3413 - accuracy: 0.8617 - val_loss: 0.8487 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 168/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3302 - accuracy: 0.8576\n",
-      "Epoch 00168: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00168: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3302 - accuracy: 0.8576 - val_loss: 0.8030 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 169/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3368 - accuracy: 0.8617\n",
-      "Epoch 00169: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3368 - accuracy: 0.8617 - val_loss: 0.7739 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 170/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8566\n",
-      "Epoch 00170: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8566 - val_loss: 0.8133 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 171/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3275 - accuracy: 0.8648\n",
-      "Epoch 00171: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3275 - accuracy: 0.8648 - val_loss: 0.8054 - val_accuracy: 0.6255 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 172/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3114 - accuracy: 0.8801\n",
-      "Epoch 00172: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3114 - accuracy: 0.8801 - val_loss: 0.7767 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 173/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8730\n",
-      "Epoch 00173: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00173: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3417 - accuracy: 0.8730 - val_loss: 0.8136 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 174/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3456 - accuracy: 0.8473\n",
-      "Epoch 00174: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3456 - accuracy: 0.8473 - val_loss: 0.8065 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 175/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3391 - accuracy: 0.8617\n",
-      "Epoch 00175: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3391 - accuracy: 0.8617 - val_loss: 0.7652 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 176/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3233 - accuracy: 0.8760\n",
-      "Epoch 00176: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3233 - accuracy: 0.8760 - val_loss: 0.8337 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 177/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3381 - accuracy: 0.8545\n",
-      "Epoch 00177: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3381 - accuracy: 0.8545 - val_loss: 0.8460 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 178/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3451 - accuracy: 0.8555\n",
-      "Epoch 00178: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00178: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3451 - accuracy: 0.8555 - val_loss: 0.7691 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 179/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3523 - accuracy: 0.8607\n",
-      "Epoch 00179: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3523 - accuracy: 0.8607 - val_loss: 0.8522 - val_accuracy: 0.5514 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 180/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3392 - accuracy: 0.8689\n",
-      "Epoch 00180: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3392 - accuracy: 0.8689 - val_loss: 0.8080 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 181/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3372 - accuracy: 0.8494\n",
-      "Epoch 00181: val_accuracy improved from 0.65021 to 0.65432, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3372 - accuracy: 0.8494 - val_loss: 0.7812 - val_accuracy: 0.6543 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 182/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3379 - accuracy: 0.8566\n",
-      "Epoch 00182: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3379 - accuracy: 0.8566 - val_loss: 0.8265 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 183/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3379 - accuracy: 0.8719\n",
-      "Epoch 00183: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3379 - accuracy: 0.8719 - val_loss: 0.8376 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 184/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3142 - accuracy: 0.8914\n",
-      "Epoch 00184: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3142 - accuracy: 0.8914 - val_loss: 0.7535 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 185/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3440 - accuracy: 0.8484\n",
-      "Epoch 00185: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3440 - accuracy: 0.8484 - val_loss: 0.8232 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 186/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3387 - accuracy: 0.8596\n",
-      "Epoch 00186: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00186: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3387 - accuracy: 0.8596 - val_loss: 0.8657 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 187/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3224 - accuracy: 0.8730\n",
-      "Epoch 00187: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3224 - accuracy: 0.8730 - val_loss: 0.8465 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 188/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3504 - accuracy: 0.8525\n",
-      "Epoch 00188: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3504 - accuracy: 0.8525 - val_loss: 0.8408 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 189/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3392 - accuracy: 0.8494\n",
-      "Epoch 00189: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3392 - accuracy: 0.8494 - val_loss: 0.8138 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 190/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3628 - accuracy: 0.8494\n",
-      "Epoch 00190: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3628 - accuracy: 0.8494 - val_loss: 0.7445 - val_accuracy: 0.6214 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 191/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3643 - accuracy: 0.8381\n",
-      "Epoch 00191: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00191: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3643 - accuracy: 0.8381 - val_loss: 0.8351 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 192/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3472 - accuracy: 0.8432\n",
-      "Epoch 00192: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3472 - accuracy: 0.8432 - val_loss: 0.8194 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 193/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3203 - accuracy: 0.8555\n",
-      "Epoch 00193: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3203 - accuracy: 0.8555 - val_loss: 0.7819 - val_accuracy: 0.6214 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 194/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3186 - accuracy: 0.8689\n",
-      "Epoch 00194: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3186 - accuracy: 0.8689 - val_loss: 0.8079 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 195/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3369 - accuracy: 0.8596\n",
-      "Epoch 00195: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3369 - accuracy: 0.8596 - val_loss: 0.8143 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 196/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3404 - accuracy: 0.8617\n",
-      "Epoch 00196: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00196: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3404 - accuracy: 0.8617 - val_loss: 0.8363 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 197/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8637\n",
-      "Epoch 00197: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3314 - accuracy: 0.8637 - val_loss: 0.8147 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 198/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3301 - accuracy: 0.8658\n",
-      "Epoch 00198: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3301 - accuracy: 0.8658 - val_loss: 0.7487 - val_accuracy: 0.6420 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 199/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3347 - accuracy: 0.8648\n",
-      "Epoch 00199: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3347 - accuracy: 0.8648 - val_loss: 0.8443 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 200/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3373 - accuracy: 0.8545\n",
-      "Epoch 00200: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3373 - accuracy: 0.8545 - val_loss: 0.7283 - val_accuracy: 0.6337 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 201/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3538 - accuracy: 0.8463\n",
-      "Epoch 00201: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00201: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3538 - accuracy: 0.8463 - val_loss: 0.7399 - val_accuracy: 0.6543 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 202/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8617\n",
-      "Epoch 00202: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3360 - accuracy: 0.8617 - val_loss: 0.7823 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 203/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8637\n",
-      "Epoch 00203: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3314 - accuracy: 0.8637 - val_loss: 0.8322 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 204/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3562 - accuracy: 0.8484\n",
-      "Epoch 00204: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3562 - accuracy: 0.8484 - val_loss: 0.8452 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 205/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3645 - accuracy: 0.8350\n",
-      "Epoch 00205: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3645 - accuracy: 0.8350 - val_loss: 0.8371 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 206/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3402 - accuracy: 0.8699\n",
-      "Epoch 00206: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00206: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3402 - accuracy: 0.8699 - val_loss: 0.7699 - val_accuracy: 0.6337 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 207/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3450 - accuracy: 0.8494\n",
-      "Epoch 00207: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3450 - accuracy: 0.8494 - val_loss: 0.8715 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 208/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3338 - accuracy: 0.8637\n",
-      "Epoch 00208: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3338 - accuracy: 0.8637 - val_loss: 0.8463 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 209/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3543 - accuracy: 0.8484\n",
-      "Epoch 00209: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3543 - accuracy: 0.8484 - val_loss: 0.7413 - val_accuracy: 0.6420 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 210/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3538 - accuracy: 0.8494\n",
-      "Epoch 00210: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3538 - accuracy: 0.8494 - val_loss: 0.7978 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 211/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3495 - accuracy: 0.8514\n",
-      "Epoch 00211: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00211: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3495 - accuracy: 0.8514 - val_loss: 0.8447 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 212/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3260 - accuracy: 0.8699\n",
-      "Epoch 00212: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3260 - accuracy: 0.8699 - val_loss: 0.7830 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 213/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3152 - accuracy: 0.8791\n",
-      "Epoch 00213: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3152 - accuracy: 0.8791 - val_loss: 0.8196 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 214/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3361 - accuracy: 0.8658\n",
-      "Epoch 00214: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3361 - accuracy: 0.8658 - val_loss: 0.7904 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 215/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3276 - accuracy: 0.8709\n",
-      "Epoch 00215: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3276 - accuracy: 0.8709 - val_loss: 0.8031 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 216/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3286 - accuracy: 0.8750\n",
-      "Epoch 00216: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00216: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3286 - accuracy: 0.8750 - val_loss: 0.7734 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 217/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3250 - accuracy: 0.8689\n",
-      "Epoch 00217: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3250 - accuracy: 0.8689 - val_loss: 0.8654 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 218/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3349 - accuracy: 0.8535\n",
-      "Epoch 00218: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3349 - accuracy: 0.8535 - val_loss: 0.8674 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 219/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3589 - accuracy: 0.8443\n",
-      "Epoch 00219: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3589 - accuracy: 0.8443 - val_loss: 0.8791 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 220/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3187 - accuracy: 0.8709\n",
-      "Epoch 00220: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3187 - accuracy: 0.8709 - val_loss: 0.7750 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 221/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3134 - accuracy: 0.8811\n",
-      "Epoch 00221: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00221: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3134 - accuracy: 0.8811 - val_loss: 0.8630 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 222/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3344 - accuracy: 0.8607\n",
-      "Epoch 00222: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3344 - accuracy: 0.8607 - val_loss: 0.7966 - val_accuracy: 0.6214 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 223/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3545 - accuracy: 0.8443\n",
-      "Epoch 00223: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3545 - accuracy: 0.8443 - val_loss: 0.7761 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 224/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3256 - accuracy: 0.8566\n",
-      "Epoch 00224: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3256 - accuracy: 0.8566 - val_loss: 0.8158 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 225/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3730 - accuracy: 0.8350\n",
-      "Epoch 00225: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3730 - accuracy: 0.8350 - val_loss: 0.7728 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 226/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3567 - accuracy: 0.8535\n",
-      "Epoch 00226: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00226: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3567 - accuracy: 0.8535 - val_loss: 0.8453 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 227/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3599 - accuracy: 0.8463\n",
-      "Epoch 00227: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3599 - accuracy: 0.8463 - val_loss: 0.8074 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 228/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8371\n",
-      "Epoch 00228: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3585 - accuracy: 0.8371 - val_loss: 0.8320 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 229/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3459 - accuracy: 0.8545\n",
-      "Epoch 00229: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3459 - accuracy: 0.8545 - val_loss: 0.8538 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 230/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3548 - accuracy: 0.8340\n",
-      "Epoch 00230: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3548 - accuracy: 0.8340 - val_loss: 0.7723 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 231/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3195 - accuracy: 0.8770\n",
-      "Epoch 00231: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2692 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00135: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "Restoring model weights from the end of the best epoch.\n",
       "\n",
-      "Epoch 00231: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3195 - accuracy: 0.8770 - val_loss: 0.8239 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Epoch 00231: early stopping\n",
-      "CPU times: user 2h 2min 9s, sys: 9min 10s, total: 2h 11min 20s\n",
-      "Wall time: 1h 11min 7s\n"
+      "Epoch 00135: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 20s 3s/step - loss: 0.2692 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8920 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Epoch 00135: early stopping\n",
+      "CPU times: user 57min 39s, sys: 5min 37s, total: 1h 3min 17s\n",
+      "Wall time: 46min 24s\n"
      ]
     }
    ],
@@ -2151,12 +1640,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2168,7 +1657,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2184,6 +1673,9 @@
     "plt.title('Training and test accuracy')\n",
     "plt.plot(params.epoch, params.history['accuracy'], label='Training accuracy')\n",
     "plt.plot(params.epoch, params.history['val_accuracy'], label='Test accuracy')\n",
+    "plt.plot(params.epoch, params.history['top_k_categorical_accuracy'], label='Top-5 accuracy')\n",
+    "plt.plot(params.epoch, params.history['mean_io_u'], label='Mean IoU') #tf.keras.metrics.Mean IoU\n",
+    "\n",
     "plt.legend()\n",
     "plt.show()\n",
     "\n",
@@ -2205,22 +1697,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "predict with test number:  67\n",
-      "./img_data/resized/Negative/3838203_landsat_8_rgb.tif\n",
-      "0.9843275   :   (0, 'Negative')\n",
-      "0.015672525   :   (1, 'Positive')\n"
+      "predict with test number:  23\n",
+      "./img_data/resized/Negative/3837676_sentinel_2_rgb.tif\n",
+      "0.8859837   :   (1, 'Positive')\n",
+      "0.11401627   :   (0, 'Negative')\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2260,20 +1752,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9843275   :   (0, 'Negative')\n",
-      "0.015672525   :   (1, 'Positive')\n"
+      "0.8859837   :   (1, 'Positive')\n",
+      "0.11401627   :   (0, 'Negative')\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2308,25 +1800,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From <ipython-input-12-6c336b480794>:2: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use Model.predict, which supports generators.\n",
       "Classification Report\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "    Negative       0.63      0.58      0.61       122\n",
-      "    Positive       0.61      0.66      0.63       121\n",
+      "    Negative       0.60      0.60      0.60       122\n",
+      "    Positive       0.60      0.60      0.60       121\n",
       "\n",
-      "    accuracy                           0.62       243\n",
-      "   macro avg       0.62      0.62      0.62       243\n",
-      "weighted avg       0.62      0.62      0.62       243\n",
+      "    accuracy                           0.60       243\n",
+      "   macro avg       0.60      0.60      0.60       243\n",
+      "weighted avg       0.60      0.60      0.60       243\n",
       "\n"
      ]
     }
@@ -2350,7 +1839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2363,16 +1852,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ebc088d10>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1a6f23e50>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 5760x5760 with 2 Axes>"
       ]
diff --git a/train-efficientNet.ipynb b/train-efficientNet.ipynb
index 924ce98..fe1d348 100644
--- a/train-efficientNet.ipynb
+++ b/train-efficientNet.ipynb
@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "['Sat Sep  5 08:09:42 2020       ',\n",
+       "['Sat Sep  5 09:16:35 2020       ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
        " '| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |',\n",
        " '|-------------------------------+----------------------+----------------------+',\n",
@@ -52,28 +52,28 @@
        " '| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |',\n",
        " '|===============================+======================+======================|',\n",
        " '|   0  Quadro RTX 4000     Off  | 00000000:1D:00.0 Off |                  N/A |',\n",
-       " '| 30%   56C    P0    37W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   43C    P0    35W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
-       " '| 30%   51C    P0    37W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 31%   42C    P0    36W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
-       " '| 31%   50C    P0    35W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   40C    P0    34W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
-       " '| 31%   55C    P0    44W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 29%   43C    P0    39W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
-       " '| 23%   29C    P0    60W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 24%   37C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
-       " '| 18%   22C    P0    60W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 19%   30C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
-       " '| 17%   28C    P0    61W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 18%   35C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
-       " '| 15%   26C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 17%   32C    P0    58W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '                                                                               ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
@@ -166,7 +166,14 @@
     "\n",
     "data_dir = \"../LandslideDataset/\"\n",
     "resized_dir = \"./img_data/resized/\"\n",
-    "test_dir = \"./img_data/test/\""
+    "test_dir = \"./img_data/test/\"\n",
+    "\n",
+    "import functools\n",
+    "top1_acc = functools.partial(tf.keras.metrics.top_k_categorical_accuracy, k=1)\n",
+    "top1_acc.__name__ = 'top1_acc'\n",
+    "\n",
+    "top5_acc = functools.partial(tf.keras.metrics.top_k_categorical_accuracy, k=5)\n",
+    "top5_acc.__name__ = 'top5_acc'"
    ]
   },
   {
@@ -316,13 +323,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "./img_data/resized/Positive/3845306_landsat_8_rgb.tif\n"
+      "./img_data/resized/Positive/3851114_sentinel_2_rgb.tif\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f325bf66550>"
+       "<matplotlib.image.AxesImage at 0x7face618c390>"
       ]
      },
      "execution_count": 4,
@@ -331,7 +338,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1023,7 +1030,7 @@
     "\n",
     "    model.compile(optimizer=optimizer,\n",
     "                  loss=loss,\n",
-    "                  metrics=[\"accuracy\"])\n",
+    "                  metrics=[\"accuracy\", top1_acc, top5_acc])\n",
     "\n",
     "    model.summary()\n",
     "    # for i, layer in enumerate(model.layers):\n",
@@ -1056,510 +1063,868 @@
       "Use `tf.data.Iterator.get_next_as_optional()` instead.\n",
       "INFO:tensorflow:batch_all_reduce: 219 all-reduces with algorithm = nccl, num_packs = 1\n",
       "INFO:tensorflow:batch_all_reduce: 219 all-reduces with algorithm = nccl, num_packs = 1\n",
-      "1/7 [===>..........................] - ETA: 0s - loss: 0.9618 - accuracy: 0.4667WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
+      "1/7 [===>..........................] - ETA: 0s - loss: 0.8930 - accuracy: 0.4800 - top1_acc: 0.4800 - top5_acc: 1.0000WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
       "Instructions for updating:\n",
       "use `tf.profiler.experimental.stop` instead.\n",
-      "2/7 [=======>......................] - ETA: 2s - loss: 0.9111 - accuracy: 0.5233WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2578s vs `on_train_batch_end` time: 0.7181s). Check your callbacks.\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.8946 - accuracy: 0.5343\n",
-      "Epoch 00001: val_accuracy improved from -inf to 0.55556, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 29s 4s/step - loss: 0.8946 - accuracy: 0.5343 - val_loss: 0.6839 - val_accuracy: 0.5556\n",
+      "2/7 [=======>......................] - ETA: 2s - loss: 0.8422 - accuracy: 0.5167 - top1_acc: 0.5167 - top5_acc: 1.0000WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2884s vs `on_train_batch_end` time: 0.7248s). Check your callbacks.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.8544 - accuracy: 0.5210 - top1_acc: 0.5210 - top5_acc: 1.0000\n",
+      "Epoch 00001: val_accuracy improved from -inf to 0.54321, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 30s 4s/step - loss: 0.8544 - accuracy: 0.5210 - top1_acc: 0.5210 - top5_acc: 1.0000 - val_loss: 0.7015 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-04\n",
       "Epoch 2/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.7732 - accuracy: 0.5865\n",
-      "Epoch 00002: val_accuracy did not improve from 0.55556\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.7732 - accuracy: 0.5865 - val_loss: 0.6934 - val_accuracy: 0.4897\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.7470 - accuracy: 0.6070 - top1_acc: 0.6070 - top5_acc: 1.0000\n",
+      "Epoch 00002: val_accuracy did not improve from 0.54321\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.7470 - accuracy: 0.6070 - top1_acc: 0.6070 - top5_acc: 1.0000 - val_loss: 0.6948 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-04\n",
       "Epoch 3/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.7128 - accuracy: 0.6233\n",
-      "Epoch 00003: val_accuracy did not improve from 0.55556\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.7128 - accuracy: 0.6233 - val_loss: 0.6856 - val_accuracy: 0.5185\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.7023 - accuracy: 0.6213 - top1_acc: 0.6213 - top5_acc: 1.0000\n",
+      "Epoch 00003: val_accuracy improved from 0.54321 to 0.54733, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.7023 - accuracy: 0.6213 - top1_acc: 0.6213 - top5_acc: 1.0000 - val_loss: 0.6978 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-04\n",
       "Epoch 4/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.6337 - accuracy: 0.6643\n",
-      "Epoch 00004: val_accuracy did not improve from 0.55556\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.6337 - accuracy: 0.6643 - val_loss: 0.6879 - val_accuracy: 0.5350\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.6226 - accuracy: 0.6725 - top1_acc: 0.6725 - top5_acc: 1.0000\n",
+      "Epoch 00004: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.6226 - accuracy: 0.6725 - top1_acc: 0.6725 - top5_acc: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-04\n",
       "Epoch 5/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5885 - accuracy: 0.6776\n",
-      "Epoch 00005: val_accuracy did not improve from 0.55556\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.5885 - accuracy: 0.6776 - val_loss: 0.6848 - val_accuracy: 0.5144\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5617 - accuracy: 0.7124 - top1_acc: 0.7124 - top5_acc: 1.0000\n",
+      "Epoch 00005: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.5617 - accuracy: 0.7124 - top1_acc: 0.7124 - top5_acc: 1.0000 - val_loss: 0.7006 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-04\n",
       "Epoch 6/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5838 - accuracy: 0.6868\n",
-      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
-      "\n",
-      "Epoch 00006: val_accuracy did not improve from 0.55556\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.5838 - accuracy: 0.6868 - val_loss: 0.6899 - val_accuracy: 0.5350\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5630 - accuracy: 0.7073 - top1_acc: 0.7073 - top5_acc: 1.0000\n",
+      "Epoch 00006: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5630 - accuracy: 0.7073 - top1_acc: 0.7073 - top5_acc: 1.0000 - val_loss: 0.6960 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
       "Epoch 7/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5815 - accuracy: 0.6888\n",
-      "Epoch 00007: val_accuracy improved from 0.55556 to 0.55967, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5815 - accuracy: 0.6888 - val_loss: 0.6896 - val_accuracy: 0.5597\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5514 - accuracy: 0.7165 - top1_acc: 0.7165 - top5_acc: 1.0000\n",
+      "Epoch 00007: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5514 - accuracy: 0.7165 - top1_acc: 0.7165 - top5_acc: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-04\n",
       "Epoch 8/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5106 - accuracy: 0.7482\n",
-      "Epoch 00008: val_accuracy improved from 0.55967 to 0.56379, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5106 - accuracy: 0.7482 - val_loss: 0.6744 - val_accuracy: 0.5638\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5193 - accuracy: 0.7482 - top1_acc: 0.7482 - top5_acc: 1.0000\n",
+      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
+      "\n",
+      "Epoch 00008: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5193 - accuracy: 0.7482 - top1_acc: 0.7482 - top5_acc: 1.0000 - val_loss: 0.6879 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 9/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5210 - accuracy: 0.7359\n",
-      "Epoch 00009: val_accuracy did not improve from 0.56379\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.5210 - accuracy: 0.7359 - val_loss: 0.6896 - val_accuracy: 0.5597\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5036 - accuracy: 0.7441 - top1_acc: 0.7441 - top5_acc: 1.0000\n",
+      "Epoch 00009: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5036 - accuracy: 0.7441 - top1_acc: 0.7441 - top5_acc: 1.0000 - val_loss: 0.6943 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 10/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5299 - accuracy: 0.7359\n",
-      "Epoch 00010: val_accuracy did not improve from 0.56379\n",
-      "7/7 [==============================] - 16s 2s/step - loss: 0.5299 - accuracy: 0.7359 - val_loss: 0.6798 - val_accuracy: 0.5432\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4857 - accuracy: 0.7605 - top1_acc: 0.7605 - top5_acc: 1.0000\n",
+      "Epoch 00010: val_accuracy did not improve from 0.54733\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4857 - accuracy: 0.7605 - top1_acc: 0.7605 - top5_acc: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.5185 - val_top1_acc: 0.5185 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 11/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5234 - accuracy: 0.7318\n",
-      "Epoch 00011: val_accuracy improved from 0.56379 to 0.56790, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.5234 - accuracy: 0.7318 - val_loss: 0.6690 - val_accuracy: 0.5679\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4699 - accuracy: 0.7748 - top1_acc: 0.7748 - top5_acc: 1.0000\n",
+      "Epoch 00011: val_accuracy improved from 0.54733 to 0.57613, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4699 - accuracy: 0.7748 - top1_acc: 0.7748 - top5_acc: 1.0000 - val_loss: 0.6857 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 12/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4963 - accuracy: 0.7584\n",
-      "Epoch 00012: val_accuracy improved from 0.56790 to 0.58436, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4963 - accuracy: 0.7584 - val_loss: 0.6804 - val_accuracy: 0.5844\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4876 - accuracy: 0.7646 - top1_acc: 0.7646 - top5_acc: 1.0000\n",
+      "Epoch 00012: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4876 - accuracy: 0.7646 - top1_acc: 0.7646 - top5_acc: 1.0000 - val_loss: 0.6970 - val_accuracy: 0.5226 - val_top1_acc: 0.5226 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 13/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4953 - accuracy: 0.7666\n",
-      "Epoch 00013: val_accuracy did not improve from 0.58436\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4953 - accuracy: 0.7666 - val_loss: 0.6750 - val_accuracy: 0.5761\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4397 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000\n",
+      "Epoch 00013: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4397 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000 - val_loss: 0.7062 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 14/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5023 - accuracy: 0.7574\n",
-      "Epoch 00014: val_accuracy improved from 0.58436 to 0.59671, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5023 - accuracy: 0.7574 - val_loss: 0.6676 - val_accuracy: 0.5967\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4570 - accuracy: 0.7779 - top1_acc: 0.7779 - top5_acc: 1.0000\n",
+      "Epoch 00014: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4570 - accuracy: 0.7779 - top1_acc: 0.7779 - top5_acc: 1.0000 - val_loss: 0.7150 - val_accuracy: 0.5021 - val_top1_acc: 0.5021 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 15/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4606 - accuracy: 0.7820\n",
-      "Epoch 00015: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4606 - accuracy: 0.7820 - val_loss: 0.6714 - val_accuracy: 0.5844\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4587 - accuracy: 0.7820 - top1_acc: 0.7820 - top5_acc: 1.0000\n",
+      "Epoch 00015: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4587 - accuracy: 0.7820 - top1_acc: 0.7820 - top5_acc: 1.0000 - val_loss: 0.7155 - val_accuracy: 0.5226 - val_top1_acc: 0.5226 - val_top5_acc: 1.0000\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 16/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4728 - accuracy: 0.7656\n",
-      "Epoch 00016: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4728 - accuracy: 0.7656 - val_loss: 0.6766 - val_accuracy: 0.5802\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4452 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00016: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
+      "\n",
+      "Epoch 00016: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4452 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7078 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 17/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4856 - accuracy: 0.7482\n",
-      "Epoch 00017: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4856 - accuracy: 0.7482 - val_loss: 0.6624 - val_accuracy: 0.5720\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4542 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000\n",
+      "Epoch 00017: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4542 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000 - val_loss: 0.7360 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 18/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4566 - accuracy: 0.7840\n",
-      "Epoch 00018: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4566 - accuracy: 0.7840 - val_loss: 0.6750 - val_accuracy: 0.5844\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4282 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00018: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4282 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7230 - val_accuracy: 0.4938 - val_top1_acc: 0.4938 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 19/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4445 - accuracy: 0.7953\n",
-      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
-      "\n",
-      "Epoch 00019: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4445 - accuracy: 0.7953 - val_loss: 0.6752 - val_accuracy: 0.5802\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00019: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4227 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.7254 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 20/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4634 - accuracy: 0.7840\n",
-      "Epoch 00020: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4634 - accuracy: 0.7840 - val_loss: 0.6677 - val_accuracy: 0.5844\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4014 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
+      "Epoch 00020: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4014 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.7454 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 21/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4558 - accuracy: 0.7881\n",
-      "Epoch 00021: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4558 - accuracy: 0.7881 - val_loss: 0.6820 - val_accuracy: 0.5638\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4401 - accuracy: 0.7892 - top1_acc: 0.7892 - top5_acc: 1.0000\n",
+      "Epoch 00021: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
+      "\n",
+      "Epoch 00021: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4401 - accuracy: 0.7892 - top1_acc: 0.7892 - top5_acc: 1.0000 - val_loss: 0.7532 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 22/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4318 - accuracy: 0.8014\n",
-      "Epoch 00022: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4318 - accuracy: 0.8014 - val_loss: 0.6801 - val_accuracy: 0.5844\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4413 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000\n",
+      "Epoch 00022: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4413 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000 - val_loss: 0.7500 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 23/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4447 - accuracy: 0.7881\n",
-      "Epoch 00023: val_accuracy did not improve from 0.59671\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4447 - accuracy: 0.7881 - val_loss: 0.6786 - val_accuracy: 0.5844\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4212 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00023: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4212 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7354 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 24/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4239 - accuracy: 0.8045\n",
-      "Epoch 00024: val_accuracy improved from 0.59671 to 0.60082, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 20s 3s/step - loss: 0.4239 - accuracy: 0.8045 - val_loss: 0.6825 - val_accuracy: 0.6008\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4280 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00024: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4280 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7625 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 25/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4219 - accuracy: 0.7963\n",
-      "Epoch 00025: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4219 - accuracy: 0.7963 - val_loss: 0.6636 - val_accuracy: 0.5926\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4185 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00025: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4185 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.7314 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 26/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4195 - accuracy: 0.8158\n",
-      "Epoch 00026: val_accuracy improved from 0.60082 to 0.60494, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4195 - accuracy: 0.8158 - val_loss: 0.6655 - val_accuracy: 0.6049\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4405 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000\n",
+      "Epoch 00026: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
+      "\n",
+      "Epoch 00026: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4405 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000 - val_loss: 0.7579 - val_accuracy: 0.5062 - val_top1_acc: 0.5062 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 27/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4462 - accuracy: 0.7851\n",
-      "Epoch 00027: val_accuracy did not improve from 0.60494\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4462 - accuracy: 0.7851 - val_loss: 0.7090 - val_accuracy: 0.5720\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4327 - accuracy: 0.7922 - top1_acc: 0.7922 - top5_acc: 1.0000\n",
+      "Epoch 00027: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4327 - accuracy: 0.7922 - top1_acc: 0.7922 - top5_acc: 1.0000 - val_loss: 0.7568 - val_accuracy: 0.5185 - val_top1_acc: 0.5185 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 28/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4349 - accuracy: 0.7943\n",
-      "Epoch 00028: val_accuracy did not improve from 0.60494\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4349 - accuracy: 0.7943 - val_loss: 0.6608 - val_accuracy: 0.6008\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4133 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00028: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4133 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.7752 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 29/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4398 - accuracy: 0.8096\n",
-      "Epoch 00029: val_accuracy improved from 0.60494 to 0.60905, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.4398 - accuracy: 0.8096 - val_loss: 0.6639 - val_accuracy: 0.6091\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4155 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
+      "Epoch 00029: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4155 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.7434 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 30/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4310 - accuracy: 0.7963\n",
-      "Epoch 00030: val_accuracy did not improve from 0.60905\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4310 - accuracy: 0.7963 - val_loss: 0.6727 - val_accuracy: 0.6049\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4042 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00030: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4042 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.7319 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 31/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4301 - accuracy: 0.8025\n",
-      "Epoch 00031: val_accuracy did not improve from 0.60905\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4301 - accuracy: 0.8025 - val_loss: 0.6597 - val_accuracy: 0.5967\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4174 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
+      "\n",
+      "Epoch 00031: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4174 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7586 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 32/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4001 - accuracy: 0.8229\n",
-      "Epoch 00032: val_accuracy did not improve from 0.60905\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4001 - accuracy: 0.8229 - val_loss: 0.6829 - val_accuracy: 0.6008\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3909 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000\n",
+      "Epoch 00032: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3909 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000 - val_loss: 0.7856 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 33/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4018 - accuracy: 0.8178\n",
-      "Epoch 00033: val_accuracy did not improve from 0.60905\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4018 - accuracy: 0.8178 - val_loss: 0.6630 - val_accuracy: 0.6049\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4224 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00033: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4224 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.7467 - val_accuracy: 0.5391 - val_top1_acc: 0.5391 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 34/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4160 - accuracy: 0.8127\n",
-      "Epoch 00034: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
-      "\n",
-      "Epoch 00034: val_accuracy did not improve from 0.60905\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4160 - accuracy: 0.8127 - val_loss: 0.7117 - val_accuracy: 0.5885\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4180 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00034: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4180 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.7897 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 35/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3969 - accuracy: 0.8168\n",
-      "Epoch 00035: val_accuracy improved from 0.60905 to 0.62140, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3969 - accuracy: 0.8168 - val_loss: 0.6538 - val_accuracy: 0.6214\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00035: val_accuracy did not improve from 0.57613\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4165 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.7593 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 36/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4183 - accuracy: 0.7963\n",
-      "Epoch 00036: val_accuracy did not improve from 0.62140\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4183 - accuracy: 0.7963 - val_loss: 0.6839 - val_accuracy: 0.6091\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4153 - accuracy: 0.7994 - top1_acc: 0.7994 - top5_acc: 1.0000\n",
+      "Epoch 00036: val_accuracy improved from 0.57613 to 0.58025, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4153 - accuracy: 0.7994 - top1_acc: 0.7994 - top5_acc: 1.0000 - val_loss: 0.7498 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 37/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4095 - accuracy: 0.8117\n",
-      "Epoch 00037: val_accuracy did not improve from 0.62140\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4095 - accuracy: 0.8117 - val_loss: 0.6883 - val_accuracy: 0.6049\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4172 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00037: val_accuracy did not improve from 0.58025\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4172 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.7429 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 38/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4058 - accuracy: 0.8137\n",
-      "Epoch 00038: val_accuracy did not improve from 0.62140\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4058 - accuracy: 0.8137 - val_loss: 0.6945 - val_accuracy: 0.6049\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4241 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
+      "Epoch 00038: val_accuracy improved from 0.58025 to 0.60494, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4241 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.7420 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 39/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3956 - accuracy: 0.8219\n",
-      "Epoch 00039: val_accuracy did not improve from 0.62140\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3956 - accuracy: 0.8219 - val_loss: 0.6719 - val_accuracy: 0.6008\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4132 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
+      "Epoch 00039: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4132 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.7824 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 40/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4194 - accuracy: 0.8137\n",
-      "Epoch 00040: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
-      "\n",
-      "Epoch 00040: val_accuracy did not improve from 0.62140\n",
-      "7/7 [==============================] - 16s 2s/step - loss: 0.4194 - accuracy: 0.8137 - val_loss: 0.6765 - val_accuracy: 0.5802\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4212 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
+      "Epoch 00040: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4212 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.7775 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 41/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3938 - accuracy: 0.8209\n",
-      "Epoch 00041: val_accuracy did not improve from 0.62140\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3938 - accuracy: 0.8209 - val_loss: 0.6770 - val_accuracy: 0.6214\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4015 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00041: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4015 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.7802 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 42/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4061 - accuracy: 0.8137\n",
-      "Epoch 00042: val_accuracy did not improve from 0.62140\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4061 - accuracy: 0.8137 - val_loss: 0.7056 - val_accuracy: 0.5885\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4230 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
+      "Epoch 00042: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4230 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.7686 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 43/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4000 - accuracy: 0.8188\n",
-      "Epoch 00043: val_accuracy improved from 0.62140 to 0.63374, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4000 - accuracy: 0.8188 - val_loss: 0.6821 - val_accuracy: 0.6337\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4070 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00043: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00043: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4070 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.7759 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 44/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4037 - accuracy: 0.8219\n",
-      "Epoch 00044: val_accuracy improved from 0.63374 to 0.64198, saving model to landslide_classifier_efficientnet.h5\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4037 - accuracy: 0.8219 - val_loss: 0.6747 - val_accuracy: 0.6420\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4361 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00044: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4361 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7949 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 45/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4048 - accuracy: 0.8250\n",
-      "Epoch 00045: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4048 - accuracy: 0.8250 - val_loss: 0.7113 - val_accuracy: 0.6091\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3897 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00045: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3897 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8084 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 46/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4142 - accuracy: 0.8147\n",
-      "Epoch 00046: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4142 - accuracy: 0.8147 - val_loss: 0.6964 - val_accuracy: 0.6091\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8260 - top1_acc: 0.8260 - top5_acc: 1.0000\n",
+      "Epoch 00046: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8260 - top1_acc: 0.8260 - top5_acc: 1.0000 - val_loss: 0.7577 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 47/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4017 - accuracy: 0.8291\n",
-      "Epoch 00047: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4017 - accuracy: 0.8291 - val_loss: 0.7184 - val_accuracy: 0.6214\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4213 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00047: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4213 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7974 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 48/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4044 - accuracy: 0.8168\n",
-      "Epoch 00048: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4044 - accuracy: 0.8168 - val_loss: 0.6885 - val_accuracy: 0.6173\n",
-      "Learning rate:  9.999999e-07\n",
-      "Epoch 49/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4198 - accuracy: 0.8147\n",
-      "Epoch 00049: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4049 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00048: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
-      "Epoch 00049: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 16s 2s/step - loss: 0.4198 - accuracy: 0.8147 - val_loss: 0.7109 - val_accuracy: 0.6132\n",
-      "Learning rate:  3.1622773e-07\n",
+      "Epoch 00048: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4049 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.7643 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 49/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4162 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00049: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4162 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.7960 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 50/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4332 - accuracy: 0.7984\n",
-      "Epoch 00050: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 16s 2s/step - loss: 0.4332 - accuracy: 0.7984 - val_loss: 0.7424 - val_accuracy: 0.5926\n",
-      "Learning rate:  3.1622773e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4137 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00050: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4137 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8056 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 51/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4124 - accuracy: 0.8158\n",
-      "Epoch 00051: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4124 - accuracy: 0.8158 - val_loss: 0.7412 - val_accuracy: 0.6091\n",
-      "Learning rate:  3.1622773e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4371 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00051: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4371 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7826 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 52/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3775 - accuracy: 0.8393\n",
-      "Epoch 00052: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3775 - accuracy: 0.8393 - val_loss: 0.7200 - val_accuracy: 0.5967\n",
-      "Learning rate:  3.1622773e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4073 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00052: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4073 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7567 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
       "Epoch 53/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4312 - accuracy: 0.8127\n",
-      "Epoch 00053: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4312 - accuracy: 0.8127 - val_loss: 0.7281 - val_accuracy: 0.6255\n",
-      "Learning rate:  3.1622773e-07\n",
-      "Epoch 54/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4147 - accuracy: 0.8096\n",
-      "Epoch 00054: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4333 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00053: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
-      "Epoch 00054: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4147 - accuracy: 0.8096 - val_loss: 0.7341 - val_accuracy: 0.6132\n",
+      "Epoch 00053: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4333 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.7879 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 54/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4300 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00054: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4300 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7981 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 55/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.8199\n",
-      "Epoch 00055: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3992 - accuracy: 0.8199 - val_loss: 0.7466 - val_accuracy: 0.5967\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4194 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00055: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4194 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7958 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 56/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4053 - accuracy: 0.8168\n",
-      "Epoch 00056: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4053 - accuracy: 0.8168 - val_loss: 0.7434 - val_accuracy: 0.6008\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4072 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
+      "Epoch 00056: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4072 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.8397 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 57/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4415 - accuracy: 0.7902\n",
-      "Epoch 00057: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4415 - accuracy: 0.7902 - val_loss: 0.7350 - val_accuracy: 0.5926\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4341 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000\n",
+      "Epoch 00057: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4341 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000 - val_loss: 0.8151 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 58/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4186 - accuracy: 0.7984\n",
-      "Epoch 00058: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4186 - accuracy: 0.7984 - val_loss: 0.7106 - val_accuracy: 0.6132\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4187 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00058: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00058: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4187 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7572 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 59/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3893 - accuracy: 0.8311\n",
-      "Epoch 00059: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00059: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3893 - accuracy: 0.8311 - val_loss: 0.7145 - val_accuracy: 0.6173\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8352 - top1_acc: 0.8352 - top5_acc: 1.0000\n",
+      "Epoch 00059: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3963 - accuracy: 0.8352 - top1_acc: 0.8352 - top5_acc: 1.0000 - val_loss: 0.7702 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 60/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4178 - accuracy: 0.8158\n",
-      "Epoch 00060: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4178 - accuracy: 0.8158 - val_loss: 0.7655 - val_accuracy: 0.5720\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4447 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000\n",
+      "Epoch 00060: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4447 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000 - val_loss: 0.7811 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 61/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3980 - accuracy: 0.8321\n",
-      "Epoch 00061: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3980 - accuracy: 0.8321 - val_loss: 0.7604 - val_accuracy: 0.5844\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4151 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00061: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4151 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8052 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 62/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3982 - accuracy: 0.8373\n",
-      "Epoch 00062: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3982 - accuracy: 0.8373 - val_loss: 0.7696 - val_accuracy: 0.6008\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00062: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4145 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.7830 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 63/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4226 - accuracy: 0.8158\n",
-      "Epoch 00063: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4226 - accuracy: 0.8158 - val_loss: 0.7523 - val_accuracy: 0.5885\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4266 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000\n",
+      "Epoch 00063: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00063: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4266 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000 - val_loss: 0.7984 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 64/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3858 - accuracy: 0.8332\n",
-      "Epoch 00064: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00064: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3858 - accuracy: 0.8332 - val_loss: 0.7783 - val_accuracy: 0.5844\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4071 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
+      "Epoch 00064: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4071 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.8233 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 65/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4130 - accuracy: 0.8188\n",
-      "Epoch 00065: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4130 - accuracy: 0.8188 - val_loss: 0.7293 - val_accuracy: 0.6008\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3957 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
+      "Epoch 00065: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3957 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.8013 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 66/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4034 - accuracy: 0.8127\n",
-      "Epoch 00066: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4034 - accuracy: 0.8127 - val_loss: 0.7832 - val_accuracy: 0.5679\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4045 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000\n",
+      "Epoch 00066: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4045 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000 - val_loss: 0.8545 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 67/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3852 - accuracy: 0.8280\n",
-      "Epoch 00067: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3852 - accuracy: 0.8280 - val_loss: 0.7835 - val_accuracy: 0.5926\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4061 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
+      "Epoch 00067: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4061 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.8196 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 68/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4121 - accuracy: 0.8096\n",
-      "Epoch 00068: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4121 - accuracy: 0.8096 - val_loss: 0.7964 - val_accuracy: 0.6008\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4111 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00068: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00068: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4111 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7886 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 69/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4096 - accuracy: 0.8332\n",
-      "Epoch 00069: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00069: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4096 - accuracy: 0.8332 - val_loss: 0.7396 - val_accuracy: 0.6132\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4065 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
+      "Epoch 00069: val_accuracy did not improve from 0.60494\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4065 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.8384 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 70/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3991 - accuracy: 0.8332\n",
-      "Epoch 00070: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3991 - accuracy: 0.8332 - val_loss: 0.7554 - val_accuracy: 0.6132\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4313 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
+      "Epoch 00070: val_accuracy improved from 0.60494 to 0.61728, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4313 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7754 - val_accuracy: 0.6173 - val_top1_acc: 0.6173 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 71/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3936 - accuracy: 0.8250\n",
-      "Epoch 00071: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3936 - accuracy: 0.8250 - val_loss: 0.8029 - val_accuracy: 0.5761\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4128 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00071: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4128 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.7718 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 72/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4035 - accuracy: 0.8127\n",
-      "Epoch 00072: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4035 - accuracy: 0.8127 - val_loss: 0.7665 - val_accuracy: 0.6091\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4139 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00072: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4139 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8216 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 73/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8158\n",
-      "Epoch 00073: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3963 - accuracy: 0.8158 - val_loss: 0.7956 - val_accuracy: 0.5761\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4000 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
+      "Epoch 00073: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4000 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.8411 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 74/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4006 - accuracy: 0.8240\n",
-      "Epoch 00074: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00074: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4006 - accuracy: 0.8240 - val_loss: 0.7401 - val_accuracy: 0.6008\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4125 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00074: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4125 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.8011 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 75/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4034 - accuracy: 0.8158\n",
-      "Epoch 00075: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4034 - accuracy: 0.8158 - val_loss: 0.7964 - val_accuracy: 0.6049\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3860 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
+      "Epoch 00075: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00075: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3860 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.8244 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 76/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3894 - accuracy: 0.8414\n",
-      "Epoch 00076: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3894 - accuracy: 0.8414 - val_loss: 0.7509 - val_accuracy: 0.6296\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4269 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000\n",
+      "Epoch 00076: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4269 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000 - val_loss: 0.8367 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 77/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4001 - accuracy: 0.8301\n",
-      "Epoch 00077: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4001 - accuracy: 0.8301 - val_loss: 0.7482 - val_accuracy: 0.6049\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00077: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8050 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 78/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3971 - accuracy: 0.8291\n",
-      "Epoch 00078: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3971 - accuracy: 0.8291 - val_loss: 0.7640 - val_accuracy: 0.5802\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00078: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4055 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8203 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 79/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3986 - accuracy: 0.8260\n",
-      "Epoch 00079: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00079: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3986 - accuracy: 0.8260 - val_loss: 0.7588 - val_accuracy: 0.6255\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4268 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00079: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4268 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8736 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 80/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4001 - accuracy: 0.8188\n",
-      "Epoch 00080: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4001 - accuracy: 0.8188 - val_loss: 0.7593 - val_accuracy: 0.5967\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4295 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00080: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00080: val_accuracy did not improve from 0.61728\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4295 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8296 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 81/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3972 - accuracy: 0.8321\n",
-      "Epoch 00081: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 16s 2s/step - loss: 0.3972 - accuracy: 0.8321 - val_loss: 0.7833 - val_accuracy: 0.5802\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4161 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00081: val_accuracy improved from 0.61728 to 0.64198, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4161 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.7427 - val_accuracy: 0.6420 - val_top1_acc: 0.6420 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 82/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3845 - accuracy: 0.8280\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8291 - top1_acc: 0.8291 - top5_acc: 1.0000\n",
       "Epoch 00082: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3845 - accuracy: 0.8280 - val_loss: 0.8468 - val_accuracy: 0.5679\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4032 - accuracy: 0.8291 - top1_acc: 0.8291 - top5_acc: 1.0000 - val_loss: 0.7947 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 83/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8045\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4326 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000\n",
       "Epoch 00083: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4165 - accuracy: 0.8045 - val_loss: 0.7531 - val_accuracy: 0.6214\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4326 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 84/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4117 - accuracy: 0.8127\n",
-      "Epoch 00084: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4256 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000\n",
       "Epoch 00084: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4117 - accuracy: 0.8127 - val_loss: 0.8539 - val_accuracy: 0.5720\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4256 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000 - val_loss: 0.8390 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 85/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3926 - accuracy: 0.8147\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4141 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
       "Epoch 00085: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3926 - accuracy: 0.8147 - val_loss: 0.7820 - val_accuracy: 0.6091\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4141 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8190 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 86/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4118 - accuracy: 0.8096\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4214 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00086: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
       "Epoch 00086: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4118 - accuracy: 0.8096 - val_loss: 0.8113 - val_accuracy: 0.6091\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4214 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8395 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 87/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4029 - accuracy: 0.8270\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4311 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000\n",
       "Epoch 00087: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4029 - accuracy: 0.8270 - val_loss: 0.7245 - val_accuracy: 0.6091\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4311 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000 - val_loss: 0.8246 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 88/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3753 - accuracy: 0.8454\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4257 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
       "Epoch 00088: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3753 - accuracy: 0.8454 - val_loss: 0.7965 - val_accuracy: 0.6132\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4257 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.8820 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 89/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4251 - accuracy: 0.8004\n",
-      "Epoch 00089: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
       "Epoch 00089: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4251 - accuracy: 0.8004 - val_loss: 0.7809 - val_accuracy: 0.6337\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3992 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8756 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 90/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4183 - accuracy: 0.8096\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4209 - accuracy: 0.8025 - top1_acc: 0.8025 - top5_acc: 1.0000\n",
       "Epoch 00090: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4183 - accuracy: 0.8096 - val_loss: 0.8011 - val_accuracy: 0.6173\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4209 - accuracy: 0.8025 - top1_acc: 0.8025 - top5_acc: 1.0000 - val_loss: 0.8621 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 91/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3801 - accuracy: 0.8362\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4325 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00091: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
       "Epoch 00091: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.3801 - accuracy: 0.8362 - val_loss: 0.7615 - val_accuracy: 0.6132\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4325 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7532 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 92/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4152 - accuracy: 0.8025\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4083 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
       "Epoch 00092: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 17s 2s/step - loss: 0.4152 - accuracy: 0.8025 - val_loss: 0.7753 - val_accuracy: 0.5802\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4083 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7819 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 93/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4167 - accuracy: 0.8076\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4089 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
       "Epoch 00093: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.4167 - accuracy: 0.8076 - val_loss: 0.7718 - val_accuracy: 0.6008\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4089 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8260 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
       "Learning rate:  1e-07\n",
       "Epoch 94/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4073 - accuracy: 0.8240\n",
-      "Epoch 00094: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4044 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00094: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4044 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8332 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 95/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4271 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
+      "Epoch 00095: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4271 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8011 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 96/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4049 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00096: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00096: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4049 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8113 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 97/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4142 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00097: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4142 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8392 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 98/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3947 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00098: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3947 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.8612 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 99/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4289 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
+      "Epoch 00099: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4289 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.8127 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 100/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3977 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00100: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3977 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8109 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 101/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4250 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00101: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00101: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4250 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7745 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 102/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4169 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00102: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4169 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7935 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 103/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4098 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000\n",
+      "Epoch 00103: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4098 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000 - val_loss: 0.8652 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 104/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4068 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
+      "Epoch 00104: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4068 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8326 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 105/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00105: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4032 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8283 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 106/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4017 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00106: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00106: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4017 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8378 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 107/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3853 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000\n",
+      "Epoch 00107: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3853 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000 - val_loss: 0.7966 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 108/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4340 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000\n",
+      "Epoch 00108: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4340 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000 - val_loss: 0.8355 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 109/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3952 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
+      "Epoch 00109: val_accuracy did not improve from 0.64198\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3952 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.7637 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 110/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4181 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
+      "Epoch 00110: val_accuracy improved from 0.64198 to 0.65432, saving model to landslide_classifier_efficientnet.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4181 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7650 - val_accuracy: 0.6543 - val_top1_acc: 0.6543 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 111/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4153 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
+      "Epoch 00111: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4153 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8310 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 112/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00112: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3963 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8353 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 113/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4266 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00113: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4266 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8709 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 114/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3953 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00114: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.3953 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8202 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 115/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4040 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00115: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00115: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4040 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8301 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 116/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3972 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00116: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3972 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8246 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 117/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4072 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00117: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4072 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8309 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 118/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4291 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000\n",
+      "Epoch 00118: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4291 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000 - val_loss: 0.8027 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 119/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
+      "Epoch 00119: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4145 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.7976 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 120/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4201 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
+      "Epoch 00120: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00120: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4201 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8541 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 121/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3885 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000\n",
+      "Epoch 00121: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3885 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000 - val_loss: 0.8040 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 122/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4096 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00122: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4096 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8665 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 123/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00123: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8233 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 124/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4238 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
+      "Epoch 00124: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4238 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 125/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4124 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
+      "Epoch 00125: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00125: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4124 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.8165 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 126/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00126: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8247 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 127/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4161 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00127: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4161 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.8241 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 128/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.8342 - top1_acc: 0.8342 - top5_acc: 1.0000\n",
+      "Epoch 00128: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3992 - accuracy: 0.8342 - top1_acc: 0.8342 - top5_acc: 1.0000 - val_loss: 0.8063 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 129/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3994 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00129: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3994 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7940 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 130/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4076 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00130: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00130: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4076 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8176 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 131/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3999 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00131: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.3999 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8128 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 132/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4064 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00132: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4064 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.7827 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 133/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4264 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00133: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4264 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8162 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 134/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4045 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00134: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4045 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.8722 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 135/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00135: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00135: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4227 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.7998 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 136/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4397 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
+      "Epoch 00136: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4397 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.8207 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 137/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4199 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
+      "Epoch 00137: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4199 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.8432 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 138/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4279 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
+      "Epoch 00138: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4279 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7595 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 139/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4121 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
+      "Epoch 00139: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4121 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.8093 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 140/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4046 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
+      "Epoch 00140: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00140: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4046 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.8322 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 141/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
+      "Epoch 00141: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7741 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 142/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4293 - accuracy: 0.8035 - top1_acc: 0.8035 - top5_acc: 1.0000\n",
+      "Epoch 00142: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4293 - accuracy: 0.8035 - top1_acc: 0.8035 - top5_acc: 1.0000 - val_loss: 0.8359 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 143/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4171 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
+      "Epoch 00143: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4171 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8525 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 144/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4122 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00144: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4122 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8431 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 145/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
+      "Epoch 00145: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00145: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4032 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8754 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 146/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3939 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000\n",
+      "Epoch 00146: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3939 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000 - val_loss: 0.7797 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 147/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4003 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
+      "Epoch 00147: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4003 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8439 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 148/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4031 - accuracy: 0.8362 - top1_acc: 0.8362 - top5_acc: 1.0000\n",
+      "Epoch 00148: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4031 - accuracy: 0.8362 - top1_acc: 0.8362 - top5_acc: 1.0000 - val_loss: 0.8480 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 149/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3908 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
+      "Epoch 00149: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3908 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8352 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 150/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4011 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
+      "Epoch 00150: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00150: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4011 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.8256 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 151/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
+      "Epoch 00151: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8496 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 152/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
+      "Epoch 00152: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3963 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8126 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 153/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4223 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000\n",
+      "Epoch 00153: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4223 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000 - val_loss: 0.8070 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 154/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
+      "Epoch 00154: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.8222 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 155/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3918 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
+      "Epoch 00155: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00155: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3918 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8130 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 156/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4041 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
+      "Epoch 00156: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.4041 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8405 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 157/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4024 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
+      "Epoch 00157: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4024 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8030 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 158/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4081 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
+      "Epoch 00158: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4081 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.7719 - val_accuracy: 0.6255 - val_top1_acc: 0.6255 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 159/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4029 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
+      "Epoch 00159: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4029 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7738 - val_accuracy: 0.6255 - val_top1_acc: 0.6255 - val_top5_acc: 1.0000\n",
+      "Learning rate:  1e-07\n",
+      "Epoch 160/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3936 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
+      "Epoch 00160: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "Restoring model weights from the end of the best epoch.\n",
       "\n",
-      "Epoch 00094: val_accuracy did not improve from 0.64198\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4073 - accuracy: 0.8240 - val_loss: 0.7892 - val_accuracy: 0.5802\n",
-      "Epoch 00094: early stopping\n",
-      "CPU times: user 44min 47s, sys: 4min 4s, total: 48min 52s\n",
-      "Wall time: 34min 2s\n"
+      "Epoch 00160: val_accuracy did not improve from 0.65432\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.3936 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8669 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
+      "Epoch 00160: early stopping\n",
+      "CPU times: user 1h 14min 8s, sys: 6min 51s, total: 1h 20min 59s\n",
+      "Wall time: 55min 25s\n"
      ]
     }
    ],
@@ -1601,7 +1966,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1613,7 +1978,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1657,15 +2022,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "predict with test number:  110\n",
-      "./img_data/resized/Negative/3838389_sentinel_2_rgb.tif\n",
-      "0.66556025   :   (1, 'Positive')\n",
-      "0.33443972   :   (0, 'Negative')\n"
+      "predict with test number:  6\n",
+      "./img_data/resized/Negative/3837558_landsat_8_rgb.tif\n",
+      "0.6771707   :   (0, 'Negative')\n",
+      "0.32282928   :   (1, 'Positive')\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1712,8 +2077,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.66556025   :   (1, 'Positive')\n",
-      "0.33443972   :   (0, 'Negative')\n"
+      "0.6771707   :   (0, 'Negative')\n",
+      "0.32282928   :   (1, 'Positive')\n"
      ]
     },
     {
@@ -1766,12 +2131,12 @@
       "Classification Report\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "    Negative       0.60      0.61      0.61       122\n",
-      "    Positive       0.61      0.60      0.60       121\n",
+      "    Negative       0.57      0.58      0.57       122\n",
+      "    Positive       0.57      0.55      0.56       121\n",
       "\n",
-      "    accuracy                           0.60       243\n",
-      "   macro avg       0.60      0.60      0.60       243\n",
-      "weighted avg       0.60      0.60      0.60       243\n",
+      "    accuracy                           0.57       243\n",
+      "   macro avg       0.57      0.57      0.57       243\n",
+      "weighted avg       0.57      0.57      0.57       243\n",
       "\n"
      ]
     }
@@ -1808,7 +2173,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1bc155c090>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f966c6f5690>"
       ]
      },
      "execution_count": 13,
@@ -1817,7 +2182,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 5760x5760 with 2 Axes>"
       ]
@@ -1847,10 +2212,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "functools.partial(<function top_k_categorical_accuracy at 0x7fac570aa200>, k=1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(top1_acc)\n",
+    "plt.title('Top accuracy')\n",
+    "plt.plot(params.epoch, params.history['top1_acc'], label='Top-1 accuracy')\n",
+    "plt.plot(params.epoch, params.history['top5_acc'], label='Top-5 accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
diff --git a/train-mobilenetv2.ipynb b/train-mobilenetv2.ipynb
index 9ae787b..87b6a1c 100644
--- a/train-mobilenetv2.ipynb
+++ b/train-mobilenetv2.ipynb
@@ -35,8 +35,8 @@
      "output_type": "stream",
      "text": [
       "py 3.7.7\n",
-      "tf 2.2.0\n",
-      "keras 2.3.0-tf\n",
+      "tf 2.3.0\n",
+      "keras 2.4.0\n",
       "mem 128555.12109375\n",
       "cpu 40\n"
      ]
@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "['Fri Sep  4 06:59:06 2020       ',\n",
+       "['Sun Sep  6 03:44:54 2020       ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
        " '| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |',\n",
        " '|-------------------------------+----------------------+----------------------+',\n",
@@ -52,28 +52,28 @@
        " '| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |',\n",
        " '|===============================+======================+======================|',\n",
        " '|   0  Quadro RTX 4000     Off  | 00000000:1D:00.0 Off |                  N/A |',\n",
-       " '| 30%   32C    P8     9W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   50C    P8    15W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
-       " '| 30%   31C    P8     6W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   43C    P8    11W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
-       " '| 30%   30C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   40C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
-       " '| 30%   31C    P8    10W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   45C    P8    18W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
-       " '| 25%   30C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 24%   34C    P5    12W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
-       " '| 20%   20C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   28C    P8     9W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
-       " '| 20%   28C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   32C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
-       " '| 20%   27C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   31C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '                                                                               ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
@@ -264,7 +264,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 976 images belonging to 2 classes.\n",
+      "Found 977 images belonging to 2 classes.\n",
       "Found 243 images belonging to 2 classes.\n"
      ]
     }
@@ -316,13 +316,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "./img_data/resized/Negative/3844923_landsat_8_rgb.tif\n"
+      "./img_data/resized/Positive/3838780_landsat_8_rgb.tif\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f740d463b10>"
+       "<matplotlib.image.AxesImage at 0x7fb6c4e57890>"
       ]
      },
      "execution_count": 4,
@@ -331,7 +331,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -474,7 +474,7 @@
       "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
       "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
       "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
-      "Model: \"model\"\n",
+      "Model: \"functional_1\"\n",
       "__________________________________________________________________________________________________\n",
       "Layer (type)                    Output Shape         Param #     Connected to                     \n",
       "==================================================================================================\n",
@@ -844,7 +844,7 @@
     "\n",
     "    model.compile(optimizer=optimizer,\n",
     "                  loss=loss,\n",
-    "                  metrics=[\"accuracy\"])\n",
+    "                  metrics=[\"accuracy\", \"top_k_categorical_accuracy\", tf.keras.metrics.MeanIoU(num_classes=2)])\n",
     "\n",
     "    model.summary()\n",
     "    # for i, layer in enumerate(model.layers):\n",
@@ -872,1249 +872,738 @@
       "Please use Model.fit, which supports generators.\n",
       "Learning rate:  1e-04\n",
       "Epoch 1/250\n",
+      "WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use `tf.data.Iterator.get_next_as_optional()` instead.\n",
       "INFO:tensorflow:batch_all_reduce: 164 all-reduces with algorithm = nccl, num_packs = 1\n",
       "INFO:tensorflow:batch_all_reduce: 164 all-reduces with algorithm = nccl, num_packs = 1\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.8840 - accuracy: 0.4969\n",
-      "Epoch 00001: val_accuracy improved from -inf to 0.56379, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 29s 4s/step - loss: 0.8840 - accuracy: 0.4969 - val_loss: 0.6823 - val_accuracy: 0.5638 - lr: 1.0000e-04\n",
+      "1/7 [===>..........................] - ETA: 0s - loss: 0.9579 - accuracy: 0.5200 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
+      "Instructions for updating:\n",
+      "use `tf.profiler.experimental.stop` instead.\n",
+      "2/7 [=======>......................] - ETA: 2s - loss: 0.9027 - accuracy: 0.5067 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2517s vs `on_train_batch_end` time: 0.5751s). Check your callbacks.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.9008 - accuracy: 0.5087 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00001: val_accuracy improved from -inf to 0.53086, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 25s 4s/step - loss: 0.9008 - accuracy: 0.5087 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7172 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 2/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.7176 - accuracy: 0.6117\n",
-      "Epoch 00002: val_accuracy improved from 0.56379 to 0.57613, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.7176 - accuracy: 0.6117 - val_loss: 0.6878 - val_accuracy: 0.5761 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.7596 - accuracy: 0.6018 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00002: val_accuracy improved from 0.53086 to 0.55556, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.7596 - accuracy: 0.6018 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7029 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 3/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.6583 - accuracy: 0.6506\n",
-      "Epoch 00003: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.6583 - accuracy: 0.6506 - val_loss: 0.7125 - val_accuracy: 0.5391 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.6723 - accuracy: 0.6325 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00003: val_accuracy did not improve from 0.55556\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.6723 - accuracy: 0.6325 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7010 - val_accuracy: 0.5185 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 4/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5778 - accuracy: 0.7111\n",
-      "Epoch 00004: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5778 - accuracy: 0.7111 - val_loss: 0.7584 - val_accuracy: 0.5062 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.6027 - accuracy: 0.6909 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00004: val_accuracy did not improve from 0.55556\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.6027 - accuracy: 0.6909 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7228 - val_accuracy: 0.5144 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 5/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5703 - accuracy: 0.7029\n",
-      "Epoch 00005: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5703 - accuracy: 0.7029 - val_loss: 0.7618 - val_accuracy: 0.5185 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5557 - accuracy: 0.7083 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00005: val_accuracy improved from 0.55556 to 0.56379, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.5557 - accuracy: 0.7083 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7036 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 6/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.5017 - accuracy: 0.7623\n",
-      "Epoch 00006: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.5017 - accuracy: 0.7623 - val_loss: 0.7213 - val_accuracy: 0.5144 - lr: 1.0000e-04\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.5393 - accuracy: 0.7329 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00006: val_accuracy did not improve from 0.56379\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.5393 - accuracy: 0.7329 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7225 - val_accuracy: 0.4979 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-04\n",
       "Epoch 7/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4817 - accuracy: 0.7633\n",
-      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
-      "\n",
-      "Epoch 00007: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4817 - accuracy: 0.7633 - val_loss: 0.7202 - val_accuracy: 0.5473 - lr: 1.0000e-04\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4988 - accuracy: 0.7574 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00007: val_accuracy improved from 0.56379 to 0.58025, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4988 - accuracy: 0.7574 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7127 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 8/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4416 - accuracy: 0.7848\n",
-      "Epoch 00008: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4416 - accuracy: 0.7848 - val_loss: 0.7294 - val_accuracy: 0.5226 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4578 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00008: val_accuracy did not improve from 0.58025\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.4578 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7574 - val_accuracy: 0.5021 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 9/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4365 - accuracy: 0.7982\n",
-      "Epoch 00009: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4365 - accuracy: 0.7982 - val_loss: 0.7206 - val_accuracy: 0.5514 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4561 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00009: val_accuracy improved from 0.58025 to 0.60082, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.4561 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7078 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 10/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4622 - accuracy: 0.7828\n",
-      "Epoch 00010: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4622 - accuracy: 0.7828 - val_loss: 0.7285 - val_accuracy: 0.5432 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4322 - accuracy: 0.8004 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00010: val_accuracy did not improve from 0.60082\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.4322 - accuracy: 0.8004 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7696 - val_accuracy: 0.5350 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 11/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4217 - accuracy: 0.8125\n",
-      "Epoch 00011: val_accuracy did not improve from 0.57613\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4217 - accuracy: 0.8125 - val_loss: 0.7168 - val_accuracy: 0.5556 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3810 - accuracy: 0.8229 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00011: val_accuracy did not improve from 0.60082\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3810 - accuracy: 0.8229 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7463 - val_accuracy: 0.5391 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 12/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4217 - accuracy: 0.8135\n",
-      "Epoch 00012: val_accuracy improved from 0.57613 to 0.60082, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.4217 - accuracy: 0.8135 - val_loss: 0.7080 - val_accuracy: 0.6008 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3898 - accuracy: 0.8332 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00012: val_accuracy did not improve from 0.60082\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3898 - accuracy: 0.8332 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7329 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 13/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.4051 - accuracy: 0.8125\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3554 - accuracy: 0.8567 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00013: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.4051 - accuracy: 0.8125 - val_loss: 0.6941 - val_accuracy: 0.6008 - lr: 3.1623e-05\n",
-      "Learning rate:  3.1622774e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3554 - accuracy: 0.8567 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7269 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  1e-04\n",
       "Epoch 14/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3902 - accuracy: 0.8299\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3270 - accuracy: 0.8608 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
+      "\n",
       "Epoch 00014: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3902 - accuracy: 0.8299 - val_loss: 0.7317 - val_accuracy: 0.5844 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3270 - accuracy: 0.8608 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7890 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 15/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3942 - accuracy: 0.8289\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3290 - accuracy: 0.8557 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00015: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3942 - accuracy: 0.8289 - val_loss: 0.7435 - val_accuracy: 0.5679 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3290 - accuracy: 0.8557 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7621 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 16/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3917 - accuracy: 0.8371\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2933 - accuracy: 0.8782 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00016: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3917 - accuracy: 0.8371 - val_loss: 0.7178 - val_accuracy: 0.5761 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2933 - accuracy: 0.8782 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7800 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-05\n",
       "Epoch 17/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3561 - accuracy: 0.8689\n",
-      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3225 - accuracy: 0.8741 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00017: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3561 - accuracy: 0.8689 - val_loss: 0.7266 - val_accuracy: 0.5226 - lr: 3.1623e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.3225 - accuracy: 0.8741 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7712 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 18/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3558 - accuracy: 0.8473\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2935 - accuracy: 0.8762 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00018: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3558 - accuracy: 0.8473 - val_loss: 0.7349 - val_accuracy: 0.5679 - lr: 1.0000e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2935 - accuracy: 0.8762 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7659 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 19/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8617\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2720 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
+      "\n",
       "Epoch 00019: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3350 - accuracy: 0.8617 - val_loss: 0.7516 - val_accuracy: 0.5720 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2720 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7624 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 20/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3553 - accuracy: 0.8432\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2723 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00020: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3553 - accuracy: 0.8432 - val_loss: 0.7550 - val_accuracy: 0.5556 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2723 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7938 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 21/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3665 - accuracy: 0.8443\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2831 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00021: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3665 - accuracy: 0.8443 - val_loss: 0.7539 - val_accuracy: 0.5597 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2831 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8037 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-06\n",
       "Epoch 22/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3450 - accuracy: 0.8504\n",
-      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2909 - accuracy: 0.8772 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00022: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3450 - accuracy: 0.8504 - val_loss: 0.7437 - val_accuracy: 0.6008 - lr: 1.0000e-05\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2909 - accuracy: 0.8772 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7662 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 23/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8576\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2655 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00023: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3418 - accuracy: 0.8576 - val_loss: 0.7462 - val_accuracy: 0.5844 - lr: 3.1623e-06\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2655 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7987 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 24/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3498 - accuracy: 0.8668\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2466 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
+      "\n",
       "Epoch 00024: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3498 - accuracy: 0.8668 - val_loss: 0.7556 - val_accuracy: 0.5885 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2466 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7649 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-06\n",
       "Epoch 25/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3420 - accuracy: 0.8555\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2553 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00025: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3420 - accuracy: 0.8555 - val_loss: 0.7625 - val_accuracy: 0.5926 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2553 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8101 - val_accuracy: 0.5473 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-06\n",
       "Epoch 26/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3466 - accuracy: 0.8596\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2549 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00026: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3466 - accuracy: 0.8596 - val_loss: 0.7788 - val_accuracy: 0.5597 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2549 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8154 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622774e-06\n",
       "Epoch 27/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3507 - accuracy: 0.8586\n",
-      "Epoch 00027: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2751 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00027: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3507 - accuracy: 0.8586 - val_loss: 0.7723 - val_accuracy: 0.5844 - lr: 3.1623e-06\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2751 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8256 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 28/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8596\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2848 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00028: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3350 - accuracy: 0.8596 - val_loss: 0.7818 - val_accuracy: 0.5844 - lr: 1.0000e-06\n",
-      "Learning rate:  9.999999e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2848 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7880 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622774e-06\n",
       "Epoch 29/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3689 - accuracy: 0.8402\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00029: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
+      "\n",
       "Epoch 00029: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3689 - accuracy: 0.8402 - val_loss: 0.8125 - val_accuracy: 0.5597 - lr: 1.0000e-06\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2691 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8155 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-07\n",
       "Epoch 30/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8576\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00030: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3417 - accuracy: 0.8576 - val_loss: 0.8187 - val_accuracy: 0.5514 - lr: 1.0000e-06\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2677 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8631 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-07\n",
       "Epoch 31/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3539 - accuracy: 0.8555\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2532 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00031: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3539 - accuracy: 0.8555 - val_loss: 0.7862 - val_accuracy: 0.5885 - lr: 1.0000e-06\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2532 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8359 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  9.999999e-07\n",
       "Epoch 32/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3225 - accuracy: 0.8760\n",
-      "Epoch 00032: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
-      "\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2851 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00032: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3225 - accuracy: 0.8760 - val_loss: 0.7727 - val_accuracy: 0.5679 - lr: 1.0000e-06\n",
-      "Learning rate:  3.1622773e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2851 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8274 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 33/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3303 - accuracy: 0.8648\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2681 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00033: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3303 - accuracy: 0.8648 - val_loss: 0.8077 - val_accuracy: 0.5638 - lr: 3.1623e-07\n",
-      "Learning rate:  3.1622773e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2681 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8248 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  9.999999e-07\n",
       "Epoch 34/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3472 - accuracy: 0.8350\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2566 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00034: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
+      "\n",
       "Epoch 00034: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3472 - accuracy: 0.8350 - val_loss: 0.7864 - val_accuracy: 0.5844 - lr: 3.1623e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2566 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8946 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622773e-07\n",
       "Epoch 35/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3492 - accuracy: 0.8566\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2848 - accuracy: 0.8792 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00035: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3492 - accuracy: 0.8566 - val_loss: 0.7581 - val_accuracy: 0.5885 - lr: 3.1623e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2848 - accuracy: 0.8792 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8544 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622773e-07\n",
       "Epoch 36/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8494\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2602 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00036: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3585 - accuracy: 0.8494 - val_loss: 0.8202 - val_accuracy: 0.5556 - lr: 3.1623e-07\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2602 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9250 - val_accuracy: 0.5267 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  3.1622773e-07\n",
       "Epoch 37/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3504 - accuracy: 0.8535\n",
-      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00037: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3504 - accuracy: 0.8535 - val_loss: 0.7828 - val_accuracy: 0.5761 - lr: 3.1623e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2454 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00037: val_accuracy improved from 0.60082 to 0.61317, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2454 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8472 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 38/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8566\n",
-      "Epoch 00038: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3443 - accuracy: 0.8566 - val_loss: 0.8223 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2681 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00038: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2681 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8657 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 39/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3502 - accuracy: 0.8545\n",
-      "Epoch 00039: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3502 - accuracy: 0.8545 - val_loss: 0.8064 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2700 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00039: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2700 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8675 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 40/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3374 - accuracy: 0.8566\n",
-      "Epoch 00040: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3374 - accuracy: 0.8566 - val_loss: 0.8015 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2498 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00040: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2498 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9167 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 41/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8535\n",
-      "Epoch 00041: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3417 - accuracy: 0.8535 - val_loss: 0.8394 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2834 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00041: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 20s 3s/step - loss: 0.2834 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8890 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Learning rate:  3.1622773e-07\n",
       "Epoch 42/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8658\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2431 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00042: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
-      "Epoch 00042: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3356 - accuracy: 0.8658 - val_loss: 0.7922 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "Epoch 00042: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2431 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8516 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 43/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8596\n",
-      "Epoch 00043: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8596 - val_loss: 0.8201 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2744 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00043: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2744 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9049 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 44/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3276 - accuracy: 0.8648\n",
-      "Epoch 00044: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3276 - accuracy: 0.8648 - val_loss: 0.8857 - val_accuracy: 0.5226 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2485 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00044: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2485 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9200 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 45/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8463\n",
-      "Epoch 00045: val_accuracy did not improve from 0.60082\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3509 - accuracy: 0.8463 - val_loss: 0.8262 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2621 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00045: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2621 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8810 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 46/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3441 - accuracy: 0.8637\n",
-      "Epoch 00046: val_accuracy improved from 0.60082 to 0.62551, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3441 - accuracy: 0.8637 - val_loss: 0.7952 - val_accuracy: 0.6255 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2686 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00046: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2686 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9243 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 47/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3377 - accuracy: 0.8566\n",
-      "Epoch 00047: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3377 - accuracy: 0.8566 - val_loss: 0.8026 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00047: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00047: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2483 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8165 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 48/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3397 - accuracy: 0.8689\n",
-      "Epoch 00048: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3397 - accuracy: 0.8689 - val_loss: 0.7874 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2606 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00048: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2606 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8416 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 49/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3257 - accuracy: 0.8719\n",
-      "Epoch 00049: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3257 - accuracy: 0.8719 - val_loss: 0.8117 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2521 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00049: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2521 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8861 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 50/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3692 - accuracy: 0.8371\n",
-      "Epoch 00050: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3692 - accuracy: 0.8371 - val_loss: 0.8433 - val_accuracy: 0.5432 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2644 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00050: val_accuracy did not improve from 0.61317\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2644 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8572 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 51/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3274 - accuracy: 0.8699\n",
-      "Epoch 00051: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00051: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3274 - accuracy: 0.8699 - val_loss: 0.8842 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2575 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00051: val_accuracy improved from 0.61317 to 0.62551, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2575 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9291 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 52/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3373 - accuracy: 0.8596\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2404 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00052: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3373 - accuracy: 0.8596 - val_loss: 0.8531 - val_accuracy: 0.5309 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2404 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8885 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 53/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.8525\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2611 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00053: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3467 - accuracy: 0.8525 - val_loss: 0.8459 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2611 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8284 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 54/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8443\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2821 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00054: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3509 - accuracy: 0.8443 - val_loss: 0.8005 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2821 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8625 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 55/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3343 - accuracy: 0.8689\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00055: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3343 - accuracy: 0.8689 - val_loss: 0.8281 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2465 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8588 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 56/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3468 - accuracy: 0.8637\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2387 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00056: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
       "Epoch 00056: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3468 - accuracy: 0.8637 - val_loss: 0.8438 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2387 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8780 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 57/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3403 - accuracy: 0.8627\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00057: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3403 - accuracy: 0.8627 - val_loss: 0.8118 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2561 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8342 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 58/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3415 - accuracy: 0.8494\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2373 - accuracy: 0.9181 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00058: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3415 - accuracy: 0.8494 - val_loss: 0.8626 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2373 - accuracy: 0.9181 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8639 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 59/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.8576\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2502 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00059: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 16s 2s/step - loss: 0.3380 - accuracy: 0.8576 - val_loss: 0.8285 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2502 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9016 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 60/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3292 - accuracy: 0.8709\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2665 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00060: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3292 - accuracy: 0.8709 - val_loss: 0.8302 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2665 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8504 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 61/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3455 - accuracy: 0.8586\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2498 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00061: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
       "Epoch 00061: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3455 - accuracy: 0.8586 - val_loss: 0.8335 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2498 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8339 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 62/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3670 - accuracy: 0.8381\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2538 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00062: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3670 - accuracy: 0.8381 - val_loss: 0.8174 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2538 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8285 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 63/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.8678\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00063: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 15s 2s/step - loss: 0.3401 - accuracy: 0.8678 - val_loss: 0.8149 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2561 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8794 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 64/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3288 - accuracy: 0.8648\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2461 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00064: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3288 - accuracy: 0.8648 - val_loss: 0.8413 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2461 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8995 - val_accuracy: 0.5473 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 65/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8699\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2630 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00065: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8699 - val_loss: 0.8298 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2630 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9266 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 66/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3437 - accuracy: 0.8566\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2652 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00066: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "\n",
       "Epoch 00066: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3437 - accuracy: 0.8566 - val_loss: 0.8331 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2652 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9469 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 67/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3523 - accuracy: 0.8453\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2535 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
       "Epoch 00067: val_accuracy did not improve from 0.62551\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3523 - accuracy: 0.8453 - val_loss: 0.8204 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2535 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8645 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 68/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3448 - accuracy: 0.8607\n",
-      "Epoch 00068: val_accuracy improved from 0.62551 to 0.62963, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3448 - accuracy: 0.8607 - val_loss: 0.8085 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2710 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00068: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2710 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8442 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 69/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8535\n",
-      "Epoch 00069: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3509 - accuracy: 0.8535 - val_loss: 0.8605 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2524 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00069: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2524 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8830 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 70/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8648\n",
-      "Epoch 00070: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3356 - accuracy: 0.8648 - val_loss: 0.8139 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2496 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00070: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2496 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8736 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 71/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8678\n",
-      "Epoch 00071: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8678 - val_loss: 0.8081 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2547 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00071: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00071: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2547 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9161 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 72/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3324 - accuracy: 0.8607\n",
-      "Epoch 00072: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3324 - accuracy: 0.8607 - val_loss: 0.7934 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2770 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00072: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.2770 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8405 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 73/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3378 - accuracy: 0.8637\n",
-      "Epoch 00073: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00073: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3378 - accuracy: 0.8637 - val_loss: 0.7900 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2588 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00073: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2588 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8699 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 74/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3243 - accuracy: 0.8740\n",
-      "Epoch 00074: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3243 - accuracy: 0.8740 - val_loss: 0.8318 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2712 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00074: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2712 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9141 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 75/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3292 - accuracy: 0.8627\n",
-      "Epoch 00075: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3292 - accuracy: 0.8627 - val_loss: 0.8205 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2478 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00075: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2478 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9195 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 76/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8791\n",
-      "Epoch 00076: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8791 - val_loss: 0.8387 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2534 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00076: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00076: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2534 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8758 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 77/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3494 - accuracy: 0.8586\n",
-      "Epoch 00077: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3494 - accuracy: 0.8586 - val_loss: 0.8618 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2490 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00077: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2490 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8574 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 78/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3365 - accuracy: 0.8545\n",
-      "Epoch 00078: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00078: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3365 - accuracy: 0.8545 - val_loss: 0.7830 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2579 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00078: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2579 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8573 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 79/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3340 - accuracy: 0.8617\n",
-      "Epoch 00079: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3340 - accuracy: 0.8617 - val_loss: 0.8720 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2362 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00079: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2362 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8933 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 80/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.2953 - accuracy: 0.8996\n",
-      "Epoch 00080: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.2953 - accuracy: 0.8996 - val_loss: 0.8033 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2506 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00080: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2506 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8688 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 81/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3548 - accuracy: 0.8576\n",
-      "Epoch 00081: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3548 - accuracy: 0.8576 - val_loss: 0.8233 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2462 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00081: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00081: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2462 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9496 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 82/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3332 - accuracy: 0.8535\n",
-      "Epoch 00082: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3332 - accuracy: 0.8535 - val_loss: 0.8475 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2467 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00082: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2467 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9060 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 83/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3520 - accuracy: 0.8566\n",
-      "Epoch 00083: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00083: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3520 - accuracy: 0.8566 - val_loss: 0.7635 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2609 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00083: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2609 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8878 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 84/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3538 - accuracy: 0.8422\n",
-      "Epoch 00084: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3538 - accuracy: 0.8422 - val_loss: 0.8541 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2333 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00084: val_accuracy did not improve from 0.62551\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2333 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8473 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 85/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3264 - accuracy: 0.8576\n",
-      "Epoch 00085: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3264 - accuracy: 0.8576 - val_loss: 0.8340 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2428 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00085: val_accuracy improved from 0.62551 to 0.67490, saving model to landslide_classifier.h5\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.2428 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7651 - val_accuracy: 0.6749 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 86/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3362 - accuracy: 0.8566\n",
-      "Epoch 00086: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3362 - accuracy: 0.8566 - val_loss: 0.8032 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2787 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00086: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2787 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8720 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 87/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3799 - accuracy: 0.8258\n",
-      "Epoch 00087: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3799 - accuracy: 0.8258 - val_loss: 0.8162 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2600 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00087: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2600 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9121 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 88/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8412\n",
-      "Epoch 00088: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00088: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3360 - accuracy: 0.8412 - val_loss: 0.8562 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2557 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00088: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2557 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8430 - val_accuracy: 0.6584 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 89/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3454 - accuracy: 0.8566\n",
-      "Epoch 00089: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3454 - accuracy: 0.8566 - val_loss: 0.8215 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00089: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2465 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9231 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 90/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3404 - accuracy: 0.8607\n",
-      "Epoch 00090: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3404 - accuracy: 0.8607 - val_loss: 0.7802 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2845 - accuracy: 0.8813 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00090: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00090: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2845 - accuracy: 0.8813 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8547 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 91/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3394 - accuracy: 0.8545\n",
-      "Epoch 00091: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3394 - accuracy: 0.8545 - val_loss: 0.8864 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2529 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00091: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2529 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8629 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 92/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3339 - accuracy: 0.8627\n",
-      "Epoch 00092: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3339 - accuracy: 0.8627 - val_loss: 0.7640 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2735 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00092: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2735 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8381 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 93/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3519 - accuracy: 0.8514\n",
-      "Epoch 00093: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00093: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3519 - accuracy: 0.8514 - val_loss: 0.8168 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2427 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00093: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2427 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8898 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 94/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3547 - accuracy: 0.8555\n",
-      "Epoch 00094: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3547 - accuracy: 0.8555 - val_loss: 0.8271 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2581 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00094: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2581 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8540 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 95/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8555\n",
-      "Epoch 00095: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3443 - accuracy: 0.8555 - val_loss: 0.8949 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2364 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00095: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00095: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2364 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8577 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 96/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3225 - accuracy: 0.8678\n",
-      "Epoch 00096: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3225 - accuracy: 0.8678 - val_loss: 0.8717 - val_accuracy: 0.5391 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2671 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00096: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2671 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8913 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 97/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3499 - accuracy: 0.8504\n",
-      "Epoch 00097: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3499 - accuracy: 0.8504 - val_loss: 0.7670 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2411 - accuracy: 0.9130 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00097: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2411 - accuracy: 0.9130 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8336 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 98/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3553 - accuracy: 0.8453\n",
-      "Epoch 00098: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00098: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3553 - accuracy: 0.8453 - val_loss: 0.8293 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2513 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00098: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2513 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8305 - val_accuracy: 0.6379 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 99/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3542 - accuracy: 0.8525\n",
-      "Epoch 00099: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3542 - accuracy: 0.8525 - val_loss: 0.7910 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2472 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00099: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2472 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8513 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 100/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3505 - accuracy: 0.8494\n",
-      "Epoch 00100: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3505 - accuracy: 0.8494 - val_loss: 0.8450 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2588 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00100: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00100: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2588 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8374 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 101/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8617\n",
-      "Epoch 00101: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8617 - val_loss: 0.8090 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00101: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2677 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8531 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 102/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3257 - accuracy: 0.8627\n",
-      "Epoch 00102: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3257 - accuracy: 0.8627 - val_loss: 0.8293 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2635 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00102: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2635 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8723 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 103/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3474 - accuracy: 0.8627\n",
-      "Epoch 00103: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00103: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3474 - accuracy: 0.8627 - val_loss: 0.8259 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2688 - accuracy: 0.8936 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00103: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 19s 3s/step - loss: 0.2688 - accuracy: 0.8936 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8420 - val_accuracy: 0.6543 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 104/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3433 - accuracy: 0.8535\n",
-      "Epoch 00104: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3433 - accuracy: 0.8535 - val_loss: 0.7913 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2489 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00104: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2489 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9124 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 105/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3361 - accuracy: 0.8525\n",
-      "Epoch 00105: val_accuracy did not improve from 0.62963\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3361 - accuracy: 0.8525 - val_loss: 0.8182 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2649 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00105: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00105: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2649 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9089 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 106/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3295 - accuracy: 0.8607\n",
-      "Epoch 00106: val_accuracy improved from 0.62963 to 0.64609, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3295 - accuracy: 0.8607 - val_loss: 0.7872 - val_accuracy: 0.6461 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2654 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00106: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2654 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8723 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 107/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3345 - accuracy: 0.8514\n",
-      "Epoch 00107: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3345 - accuracy: 0.8514 - val_loss: 0.7980 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2536 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00107: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2536 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9203 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 108/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3518 - accuracy: 0.8484\n",
-      "Epoch 00108: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3518 - accuracy: 0.8484 - val_loss: 0.8269 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2511 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00108: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2511 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8540 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 109/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3631 - accuracy: 0.8463\n",
-      "Epoch 00109: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3631 - accuracy: 0.8463 - val_loss: 0.7967 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2493 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00109: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2493 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8497 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 110/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3256 - accuracy: 0.8811\n",
-      "Epoch 00110: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3256 - accuracy: 0.8811 - val_loss: 0.7855 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2351 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00110: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00110: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2351 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8849 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 111/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3424 - accuracy: 0.8596\n",
-      "Epoch 00111: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00111: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3424 - accuracy: 0.8596 - val_loss: 0.8009 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2592 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00111: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2592 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8466 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 112/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3269 - accuracy: 0.8545\n",
-      "Epoch 00112: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3269 - accuracy: 0.8545 - val_loss: 0.8064 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2705 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00112: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2705 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9399 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 113/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3537 - accuracy: 0.8473\n",
-      "Epoch 00113: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3537 - accuracy: 0.8473 - val_loss: 0.8147 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2710 - accuracy: 0.8864 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00113: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2710 - accuracy: 0.8864 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8628 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 114/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3551 - accuracy: 0.8453\n",
-      "Epoch 00114: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3551 - accuracy: 0.8453 - val_loss: 0.8217 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2526 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00114: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2526 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9157 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 115/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8740\n",
-      "Epoch 00115: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3360 - accuracy: 0.8740 - val_loss: 0.8076 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2657 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00115: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00115: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2657 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8460 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 116/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.8494\n",
-      "Epoch 00116: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00116: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3467 - accuracy: 0.8494 - val_loss: 0.8377 - val_accuracy: 0.5514 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2513 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00116: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2513 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9235 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 117/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3400 - accuracy: 0.8576\n",
-      "Epoch 00117: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3400 - accuracy: 0.8576 - val_loss: 0.7742 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2696 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00117: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2696 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8443 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 118/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3250 - accuracy: 0.8689\n",
-      "Epoch 00118: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3250 - accuracy: 0.8689 - val_loss: 0.8126 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2493 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00118: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2493 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8471 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 119/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3344 - accuracy: 0.8658\n",
-      "Epoch 00119: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3344 - accuracy: 0.8658 - val_loss: 0.8570 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2434 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00119: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2434 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8549 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 120/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3520 - accuracy: 0.8525\n",
-      "Epoch 00120: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3520 - accuracy: 0.8525 - val_loss: 0.8003 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2296 - accuracy: 0.9222 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00120: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00120: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2296 - accuracy: 0.9222 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8200 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 121/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8596\n",
-      "Epoch 00121: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00121: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8596 - val_loss: 0.8445 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2687 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00121: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2687 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9070 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 122/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3434 - accuracy: 0.8514\n",
-      "Epoch 00122: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3434 - accuracy: 0.8514 - val_loss: 0.8889 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2371 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00122: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2371 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8041 - val_accuracy: 0.6379 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 123/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3447 - accuracy: 0.8627\n",
-      "Epoch 00123: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3447 - accuracy: 0.8627 - val_loss: 0.8424 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2795 - accuracy: 0.8802 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00123: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2795 - accuracy: 0.8802 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8686 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 124/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3465 - accuracy: 0.8494\n",
-      "Epoch 00124: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3465 - accuracy: 0.8494 - val_loss: 0.7666 - val_accuracy: 0.6255 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2578 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00124: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2578 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8294 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 125/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3563 - accuracy: 0.8535\n",
-      "Epoch 00125: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3563 - accuracy: 0.8535 - val_loss: 0.7835 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00125: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00125: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2691 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8685 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 126/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3456 - accuracy: 0.8596\n",
-      "Epoch 00126: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00126: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3456 - accuracy: 0.8596 - val_loss: 0.8848 - val_accuracy: 0.5432 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2678 - accuracy: 0.9069 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00126: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2678 - accuracy: 0.9069 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9145 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 127/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3229 - accuracy: 0.8678\n",
-      "Epoch 00127: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3229 - accuracy: 0.8678 - val_loss: 0.7758 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2786 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00127: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2786 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8757 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 128/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3519 - accuracy: 0.8555\n",
-      "Epoch 00128: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3519 - accuracy: 0.8555 - val_loss: 0.8406 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2633 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00128: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2633 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8155 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 129/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8637\n",
-      "Epoch 00129: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3350 - accuracy: 0.8637 - val_loss: 0.8846 - val_accuracy: 0.5103 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2695 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00129: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2695 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8042 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 130/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3508 - accuracy: 0.8494\n",
-      "Epoch 00130: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3508 - accuracy: 0.8494 - val_loss: 0.7418 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.9161 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00130: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "\n",
+      "Epoch 00130: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2287 - accuracy: 0.9161 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8985 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 131/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3476 - accuracy: 0.8443\n",
-      "Epoch 00131: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00131: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3476 - accuracy: 0.8443 - val_loss: 0.8043 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2657 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00131: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 17s 2s/step - loss: 0.2657 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9389 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 132/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8586\n",
-      "Epoch 00132: val_accuracy did not improve from 0.64609\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3375 - accuracy: 0.8586 - val_loss: 0.7864 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2565 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00132: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2565 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8627 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 133/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3460 - accuracy: 0.8637\n",
-      "Epoch 00133: val_accuracy improved from 0.64609 to 0.65021, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3460 - accuracy: 0.8637 - val_loss: 0.7554 - val_accuracy: 0.6502 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2487 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00133: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 18s 3s/step - loss: 0.2487 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8624 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 134/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3703 - accuracy: 0.8289\n",
-      "Epoch 00134: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3703 - accuracy: 0.8289 - val_loss: 0.8519 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2576 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00134: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 16s 2s/step - loss: 0.2576 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8436 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
       "Learning rate:  1e-07\n",
       "Epoch 135/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3421 - accuracy: 0.8545\n",
-      "Epoch 00135: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3421 - accuracy: 0.8545 - val_loss: 0.8661 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 136/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3481 - accuracy: 0.8607\n",
-      "Epoch 00136: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3481 - accuracy: 0.8607 - val_loss: 0.7809 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 137/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3487 - accuracy: 0.8566\n",
-      "Epoch 00137: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3487 - accuracy: 0.8566 - val_loss: 0.7846 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 138/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3199 - accuracy: 0.8719\n",
-      "Epoch 00138: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00138: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3199 - accuracy: 0.8719 - val_loss: 0.8131 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 139/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3505 - accuracy: 0.8525\n",
-      "Epoch 00139: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3505 - accuracy: 0.8525 - val_loss: 0.7995 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 140/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3507 - accuracy: 0.8463\n",
-      "Epoch 00140: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3507 - accuracy: 0.8463 - val_loss: 0.8360 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 141/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8668\n",
-      "Epoch 00141: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3314 - accuracy: 0.8668 - val_loss: 0.8132 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 142/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3645 - accuracy: 0.8463\n",
-      "Epoch 00142: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3645 - accuracy: 0.8463 - val_loss: 0.7943 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 143/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3629 - accuracy: 0.8361\n",
-      "Epoch 00143: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00143: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3629 - accuracy: 0.8361 - val_loss: 0.8071 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 144/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3274 - accuracy: 0.8719\n",
-      "Epoch 00144: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3274 - accuracy: 0.8719 - val_loss: 0.7615 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 145/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3324 - accuracy: 0.8689\n",
-      "Epoch 00145: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3324 - accuracy: 0.8689 - val_loss: 0.8242 - val_accuracy: 0.5720 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 146/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.8617\n",
-      "Epoch 00146: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3401 - accuracy: 0.8617 - val_loss: 0.8306 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 147/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3069 - accuracy: 0.8760\n",
-      "Epoch 00147: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3069 - accuracy: 0.8760 - val_loss: 0.8724 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 148/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3335 - accuracy: 0.8514\n",
-      "Epoch 00148: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00148: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3335 - accuracy: 0.8514 - val_loss: 0.8213 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 149/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8576\n",
-      "Epoch 00149: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3585 - accuracy: 0.8576 - val_loss: 0.8113 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 150/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3130 - accuracy: 0.8842\n",
-      "Epoch 00150: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3130 - accuracy: 0.8842 - val_loss: 0.7828 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 151/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3313 - accuracy: 0.8699\n",
-      "Epoch 00151: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3313 - accuracy: 0.8699 - val_loss: 0.7748 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 152/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3463 - accuracy: 0.8576\n",
-      "Epoch 00152: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3463 - accuracy: 0.8576 - val_loss: 0.8466 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 153/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3499 - accuracy: 0.8514\n",
-      "Epoch 00153: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00153: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3499 - accuracy: 0.8514 - val_loss: 0.7810 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 154/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3410 - accuracy: 0.8658\n",
-      "Epoch 00154: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3410 - accuracy: 0.8658 - val_loss: 0.8127 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 155/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3354 - accuracy: 0.8596\n",
-      "Epoch 00155: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3354 - accuracy: 0.8596 - val_loss: 0.8085 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 156/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3388 - accuracy: 0.8576\n",
-      "Epoch 00156: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3388 - accuracy: 0.8576 - val_loss: 0.8126 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 157/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8617\n",
-      "Epoch 00157: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3356 - accuracy: 0.8617 - val_loss: 0.7967 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 158/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8525\n",
-      "Epoch 00158: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00158: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3418 - accuracy: 0.8525 - val_loss: 0.8095 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 159/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3320 - accuracy: 0.8596\n",
-      "Epoch 00159: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3320 - accuracy: 0.8596 - val_loss: 0.8208 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 160/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3282 - accuracy: 0.8627\n",
-      "Epoch 00160: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3282 - accuracy: 0.8627 - val_loss: 0.7915 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 161/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3433 - accuracy: 0.8586\n",
-      "Epoch 00161: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3433 - accuracy: 0.8586 - val_loss: 0.8482 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 162/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3383 - accuracy: 0.8566\n",
-      "Epoch 00162: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3383 - accuracy: 0.8566 - val_loss: 0.8515 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 163/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3337 - accuracy: 0.8586\n",
-      "Epoch 00163: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00163: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3337 - accuracy: 0.8586 - val_loss: 0.8165 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 164/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3628 - accuracy: 0.8381\n",
-      "Epoch 00164: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3628 - accuracy: 0.8381 - val_loss: 0.8404 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 165/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3454 - accuracy: 0.8514\n",
-      "Epoch 00165: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3454 - accuracy: 0.8514 - val_loss: 0.7863 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 166/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3599 - accuracy: 0.8432\n",
-      "Epoch 00166: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3599 - accuracy: 0.8432 - val_loss: 0.8026 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 167/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3413 - accuracy: 0.8617\n",
-      "Epoch 00167: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3413 - accuracy: 0.8617 - val_loss: 0.8487 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 168/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3302 - accuracy: 0.8576\n",
-      "Epoch 00168: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00168: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3302 - accuracy: 0.8576 - val_loss: 0.8030 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 169/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3368 - accuracy: 0.8617\n",
-      "Epoch 00169: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3368 - accuracy: 0.8617 - val_loss: 0.7739 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 170/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8566\n",
-      "Epoch 00170: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3427 - accuracy: 0.8566 - val_loss: 0.8133 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 171/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3275 - accuracy: 0.8648\n",
-      "Epoch 00171: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3275 - accuracy: 0.8648 - val_loss: 0.8054 - val_accuracy: 0.6255 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 172/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3114 - accuracy: 0.8801\n",
-      "Epoch 00172: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3114 - accuracy: 0.8801 - val_loss: 0.7767 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 173/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8730\n",
-      "Epoch 00173: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00173: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3417 - accuracy: 0.8730 - val_loss: 0.8136 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 174/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3456 - accuracy: 0.8473\n",
-      "Epoch 00174: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3456 - accuracy: 0.8473 - val_loss: 0.8065 - val_accuracy: 0.5473 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 175/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3391 - accuracy: 0.8617\n",
-      "Epoch 00175: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3391 - accuracy: 0.8617 - val_loss: 0.7652 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 176/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3233 - accuracy: 0.8760\n",
-      "Epoch 00176: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3233 - accuracy: 0.8760 - val_loss: 0.8337 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 177/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3381 - accuracy: 0.8545\n",
-      "Epoch 00177: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3381 - accuracy: 0.8545 - val_loss: 0.8460 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 178/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3451 - accuracy: 0.8555\n",
-      "Epoch 00178: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00178: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3451 - accuracy: 0.8555 - val_loss: 0.7691 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 179/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3523 - accuracy: 0.8607\n",
-      "Epoch 00179: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3523 - accuracy: 0.8607 - val_loss: 0.8522 - val_accuracy: 0.5514 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 180/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3392 - accuracy: 0.8689\n",
-      "Epoch 00180: val_accuracy did not improve from 0.65021\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3392 - accuracy: 0.8689 - val_loss: 0.8080 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 181/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3372 - accuracy: 0.8494\n",
-      "Epoch 00181: val_accuracy improved from 0.65021 to 0.65432, saving model to landslide_classifier.h5\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3372 - accuracy: 0.8494 - val_loss: 0.7812 - val_accuracy: 0.6543 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 182/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3379 - accuracy: 0.8566\n",
-      "Epoch 00182: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3379 - accuracy: 0.8566 - val_loss: 0.8265 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 183/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3379 - accuracy: 0.8719\n",
-      "Epoch 00183: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3379 - accuracy: 0.8719 - val_loss: 0.8376 - val_accuracy: 0.6049 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 184/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3142 - accuracy: 0.8914\n",
-      "Epoch 00184: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3142 - accuracy: 0.8914 - val_loss: 0.7535 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 185/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3440 - accuracy: 0.8484\n",
-      "Epoch 00185: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3440 - accuracy: 0.8484 - val_loss: 0.8232 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 186/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3387 - accuracy: 0.8596\n",
-      "Epoch 00186: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00186: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3387 - accuracy: 0.8596 - val_loss: 0.8657 - val_accuracy: 0.5638 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 187/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3224 - accuracy: 0.8730\n",
-      "Epoch 00187: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3224 - accuracy: 0.8730 - val_loss: 0.8465 - val_accuracy: 0.6296 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 188/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3504 - accuracy: 0.8525\n",
-      "Epoch 00188: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3504 - accuracy: 0.8525 - val_loss: 0.8408 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 189/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3392 - accuracy: 0.8494\n",
-      "Epoch 00189: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3392 - accuracy: 0.8494 - val_loss: 0.8138 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 190/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3628 - accuracy: 0.8494\n",
-      "Epoch 00190: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3628 - accuracy: 0.8494 - val_loss: 0.7445 - val_accuracy: 0.6214 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 191/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3643 - accuracy: 0.8381\n",
-      "Epoch 00191: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00191: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3643 - accuracy: 0.8381 - val_loss: 0.8351 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 192/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3472 - accuracy: 0.8432\n",
-      "Epoch 00192: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3472 - accuracy: 0.8432 - val_loss: 0.8194 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 193/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3203 - accuracy: 0.8555\n",
-      "Epoch 00193: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3203 - accuracy: 0.8555 - val_loss: 0.7819 - val_accuracy: 0.6214 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 194/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3186 - accuracy: 0.8689\n",
-      "Epoch 00194: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3186 - accuracy: 0.8689 - val_loss: 0.8079 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 195/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3369 - accuracy: 0.8596\n",
-      "Epoch 00195: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3369 - accuracy: 0.8596 - val_loss: 0.8143 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 196/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3404 - accuracy: 0.8617\n",
-      "Epoch 00196: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00196: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3404 - accuracy: 0.8617 - val_loss: 0.8363 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 197/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8637\n",
-      "Epoch 00197: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3314 - accuracy: 0.8637 - val_loss: 0.8147 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 198/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3301 - accuracy: 0.8658\n",
-      "Epoch 00198: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3301 - accuracy: 0.8658 - val_loss: 0.7487 - val_accuracy: 0.6420 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 199/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3347 - accuracy: 0.8648\n",
-      "Epoch 00199: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3347 - accuracy: 0.8648 - val_loss: 0.8443 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 200/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3373 - accuracy: 0.8545\n",
-      "Epoch 00200: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3373 - accuracy: 0.8545 - val_loss: 0.7283 - val_accuracy: 0.6337 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 201/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3538 - accuracy: 0.8463\n",
-      "Epoch 00201: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00201: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3538 - accuracy: 0.8463 - val_loss: 0.7399 - val_accuracy: 0.6543 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 202/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3360 - accuracy: 0.8617\n",
-      "Epoch 00202: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3360 - accuracy: 0.8617 - val_loss: 0.7823 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 203/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8637\n",
-      "Epoch 00203: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3314 - accuracy: 0.8637 - val_loss: 0.8322 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 204/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3562 - accuracy: 0.8484\n",
-      "Epoch 00204: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3562 - accuracy: 0.8484 - val_loss: 0.8452 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 205/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3645 - accuracy: 0.8350\n",
-      "Epoch 00205: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3645 - accuracy: 0.8350 - val_loss: 0.8371 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 206/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3402 - accuracy: 0.8699\n",
-      "Epoch 00206: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00206: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3402 - accuracy: 0.8699 - val_loss: 0.7699 - val_accuracy: 0.6337 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 207/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3450 - accuracy: 0.8494\n",
-      "Epoch 00207: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3450 - accuracy: 0.8494 - val_loss: 0.8715 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 208/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3338 - accuracy: 0.8637\n",
-      "Epoch 00208: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3338 - accuracy: 0.8637 - val_loss: 0.8463 - val_accuracy: 0.5679 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 209/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3543 - accuracy: 0.8484\n",
-      "Epoch 00209: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3543 - accuracy: 0.8484 - val_loss: 0.7413 - val_accuracy: 0.6420 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 210/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3538 - accuracy: 0.8494\n",
-      "Epoch 00210: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3538 - accuracy: 0.8494 - val_loss: 0.7978 - val_accuracy: 0.6091 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 211/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3495 - accuracy: 0.8514\n",
-      "Epoch 00211: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00211: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3495 - accuracy: 0.8514 - val_loss: 0.8447 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 212/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3260 - accuracy: 0.8699\n",
-      "Epoch 00212: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3260 - accuracy: 0.8699 - val_loss: 0.7830 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 213/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3152 - accuracy: 0.8791\n",
-      "Epoch 00213: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3152 - accuracy: 0.8791 - val_loss: 0.8196 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 214/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3361 - accuracy: 0.8658\n",
-      "Epoch 00214: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3361 - accuracy: 0.8658 - val_loss: 0.7904 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 215/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3276 - accuracy: 0.8709\n",
-      "Epoch 00215: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3276 - accuracy: 0.8709 - val_loss: 0.8031 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 216/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3286 - accuracy: 0.8750\n",
-      "Epoch 00216: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00216: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3286 - accuracy: 0.8750 - val_loss: 0.7734 - val_accuracy: 0.6132 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 217/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3250 - accuracy: 0.8689\n",
-      "Epoch 00217: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3250 - accuracy: 0.8689 - val_loss: 0.8654 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 218/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3349 - accuracy: 0.8535\n",
-      "Epoch 00218: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3349 - accuracy: 0.8535 - val_loss: 0.8674 - val_accuracy: 0.5844 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 219/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3589 - accuracy: 0.8443\n",
-      "Epoch 00219: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3589 - accuracy: 0.8443 - val_loss: 0.8791 - val_accuracy: 0.5885 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 220/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3187 - accuracy: 0.8709\n",
-      "Epoch 00220: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3187 - accuracy: 0.8709 - val_loss: 0.7750 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 221/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3134 - accuracy: 0.8811\n",
-      "Epoch 00221: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00221: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3134 - accuracy: 0.8811 - val_loss: 0.8630 - val_accuracy: 0.5802 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 222/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3344 - accuracy: 0.8607\n",
-      "Epoch 00222: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3344 - accuracy: 0.8607 - val_loss: 0.7966 - val_accuracy: 0.6214 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 223/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3545 - accuracy: 0.8443\n",
-      "Epoch 00223: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3545 - accuracy: 0.8443 - val_loss: 0.7761 - val_accuracy: 0.5967 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 224/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3256 - accuracy: 0.8566\n",
-      "Epoch 00224: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3256 - accuracy: 0.8566 - val_loss: 0.8158 - val_accuracy: 0.6008 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 225/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3730 - accuracy: 0.8350\n",
-      "Epoch 00225: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3730 - accuracy: 0.8350 - val_loss: 0.7728 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 226/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3567 - accuracy: 0.8535\n",
-      "Epoch 00226: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
-      "\n",
-      "Epoch 00226: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3567 - accuracy: 0.8535 - val_loss: 0.8453 - val_accuracy: 0.5556 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 227/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3599 - accuracy: 0.8463\n",
-      "Epoch 00227: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3599 - accuracy: 0.8463 - val_loss: 0.8074 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 228/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8371\n",
-      "Epoch 00228: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3585 - accuracy: 0.8371 - val_loss: 0.8320 - val_accuracy: 0.5926 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 229/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3459 - accuracy: 0.8545\n",
-      "Epoch 00229: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3459 - accuracy: 0.8545 - val_loss: 0.8538 - val_accuracy: 0.5761 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 230/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3548 - accuracy: 0.8340\n",
-      "Epoch 00230: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 18s 3s/step - loss: 0.3548 - accuracy: 0.8340 - val_loss: 0.7723 - val_accuracy: 0.6173 - lr: 1.0000e-07\n",
-      "Learning rate:  1e-07\n",
-      "Epoch 231/250\n",
-      "7/7 [==============================] - ETA: 0s - loss: 0.3195 - accuracy: 0.8770\n",
-      "Epoch 00231: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.2692 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
+      "Epoch 00135: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
       "Restoring model weights from the end of the best epoch.\n",
       "\n",
-      "Epoch 00231: val_accuracy did not improve from 0.65432\n",
-      "7/7 [==============================] - 19s 3s/step - loss: 0.3195 - accuracy: 0.8770 - val_loss: 0.8239 - val_accuracy: 0.5597 - lr: 1.0000e-07\n",
-      "Epoch 00231: early stopping\n",
-      "CPU times: user 2h 2min 9s, sys: 9min 10s, total: 2h 11min 20s\n",
-      "Wall time: 1h 11min 7s\n"
+      "Epoch 00135: val_accuracy did not improve from 0.67490\n",
+      "7/7 [==============================] - 20s 3s/step - loss: 0.2692 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8920 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
+      "Epoch 00135: early stopping\n",
+      "CPU times: user 57min 39s, sys: 5min 37s, total: 1h 3min 17s\n",
+      "Wall time: 46min 24s\n"
      ]
     }
    ],
@@ -2151,12 +1640,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2168,7 +1657,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2184,6 +1673,9 @@
     "plt.title('Training and test accuracy')\n",
     "plt.plot(params.epoch, params.history['accuracy'], label='Training accuracy')\n",
     "plt.plot(params.epoch, params.history['val_accuracy'], label='Test accuracy')\n",
+    "plt.plot(params.epoch, params.history['top_k_categorical_accuracy'], label='Top-5 accuracy')\n",
+    "plt.plot(params.epoch, params.history['mean_io_u'], label='Mean IoU') #tf.keras.metrics.Mean IoU\n",
+    "\n",
     "plt.legend()\n",
     "plt.show()\n",
     "\n",
@@ -2205,22 +1697,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "predict with test number:  67\n",
-      "./img_data/resized/Negative/3838203_landsat_8_rgb.tif\n",
-      "0.9843275   :   (0, 'Negative')\n",
-      "0.015672525   :   (1, 'Positive')\n"
+      "predict with test number:  23\n",
+      "./img_data/resized/Negative/3837676_sentinel_2_rgb.tif\n",
+      "0.8859837   :   (1, 'Positive')\n",
+      "0.11401627   :   (0, 'Negative')\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2260,15 +1752,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.9843275   :   (0, 'Negative')\n",
-      "0.015672525   :   (1, 'Positive')\n"
+      "0.8859837   :   (1, 'Positive')\n",
+      "0.11401627   :   (0, 'Negative')\n"
      ]
     },
     {
@@ -2308,25 +1800,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From <ipython-input-12-6c336b480794>:2: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use Model.predict, which supports generators.\n",
       "Classification Report\n",
       "              precision    recall  f1-score   support\n",
       "\n",
-      "    Negative       0.63      0.58      0.61       122\n",
-      "    Positive       0.61      0.66      0.63       121\n",
+      "    Negative       0.60      0.60      0.60       122\n",
+      "    Positive       0.60      0.60      0.60       121\n",
       "\n",
-      "    accuracy                           0.62       243\n",
-      "   macro avg       0.62      0.62      0.62       243\n",
-      "weighted avg       0.62      0.62      0.62       243\n",
+      "    accuracy                           0.60       243\n",
+      "   macro avg       0.60      0.60      0.60       243\n",
+      "weighted avg       0.60      0.60      0.60       243\n",
       "\n"
      ]
     }
@@ -2350,7 +1839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2363,16 +1852,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ebc088d10>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1a6f23e50>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 5760x5760 with 2 Axes>"
       ]