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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training + Testing Landslide Prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Created: <b>Trong-An Bui</b> <br>\n",
+    "Organization: <b>ViP Lab - National Chi Nan University, Taiwan</b> <br/>\n",
+    "Email: trongan93@gmail.com <br/>\n",
+    "Project: <b>AI Cube Satellite</b> <br/>\n",
+    "Advisor: <b>Prof. Pei-Jun Lee</b>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "py 3.7.7\n",
+      "tf 2.3.0\n",
+      "keras 2.4.0\n",
+      "mem 128555.12109375\n",
+      "cpu 40\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['Sat Sep  5 07:43:37 2020       ',\n",
+       " '+-----------------------------------------------------------------------------+',\n",
+       " '| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |',\n",
+       " '|-------------------------------+----------------------+----------------------+',\n",
+       " '| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |',\n",
+       " '| 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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
+       " '| 30%   27C    P8     5W / 125W |   4776MiB /  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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
+       " '| 30%   28C    P8     9W / 125W |   4752MiB /  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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
+       " '| 20%   19C    P8     8W / 250W |   4581MiB / 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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
+       " '| 20%   24C    P8     8W / 250W |   4557MiB / 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",
+       " '+-----------------------------------------------------------------------------+']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
+    "#os.environ[\"AUTOGRAPH_VERBOSITY\"] = \"10\"\n",
+    "#os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\"\n",
+    "\n",
+    "from platform import python_version\n",
+    "import warnings\n",
+    "import time\n",
+    "import datetime as dt\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import multiprocessing as mp\n",
+    "import shutil\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import backend as K\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "# from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions\n",
+    "from tensorflow.keras.applications.efficientnet import EfficientNetB0, preprocess_input, decode_predictions\n",
+    "from tensorflow.keras.models import *\n",
+    "from tensorflow.keras.layers import *\n",
+    "from tensorflow.keras.optimizers import *\n",
+    "from tensorflow.keras.utils import *\n",
+    "from tensorflow.keras.callbacks import *\n",
+    "from tensorflow.keras.initializers import *\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sn\n",
+    "\n",
+    "from PIL import Image\n",
+    "import xml.etree.ElementTree as ET\n",
+    "import psutil\n",
+    "import random\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "%matplotlib inline\n",
+    "\n",
+    "print(\"py\", python_version())\n",
+    "print(\"tf\", tf.__version__)\n",
+    "print(\"keras\", tf.keras.__version__)\n",
+    "mem = psutil.virtual_memory()\n",
+    "print(\"mem\", mem.total/1024/1024)\n",
+    "cpu = mp.cpu_count()\n",
+    "print(\"cpu\", cpu)\n",
+    "\n",
+    "%system nvidia-smi\n",
+    "#%system rocm-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epochs = 250\n",
+    "batch_size = 150\n",
+    "testsplit = .2\n",
+    "targetx = 224\n",
+    "targety = 224\n",
+    "learning_rate = 0.0001\n",
+    "classes = 2\n",
+    "seed = random.randint(1, 1000)\n",
+    "\n",
+    "data_dir = \"../LandslideDataset/\"\n",
+    "resized_dir = \"./img_data/resized/\"\n",
+    "test_dir = \"./img_data/test/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Crop images using provided annotations"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "%system rm -rf $resized_dir\n",
+    "%system mkdir $resized_dir\n",
+    "\n",
+    "\n",
+    "def crop_and_save_img(img_path, new_dir_path, original_ext_filter = 0):\n",
+    "#     print('trongan93:', new_dir_path)\n",
+    "    image = Image.open(img_path)\n",
+    "    image = image.resize((targetx,targety))\n",
+    "    image.save(new_dir_path)\n",
+    "\n",
+    "def resize_images(ext = 0):\n",
+    "    imgPaths = sorted(list(paths.list_images(data_dir)))\n",
+    "#     print('images len: ', len(imgPaths))\n",
+    "    \n",
+    "    labels_dir = os.listdir(data_dir)\n",
+    "    for label_dir in labels_dir:\n",
+    "        cropper_label_dir = resized_dir+label_dir\n",
+    "        if not os.path.exists(cropper_label_dir):\n",
+    "            os.makedirs(cropper_label_dir)\n",
+    "            print(\"sucessfull make dir for label: \", cropper_label_dir)\n",
+    "    \n",
+    "    \n",
+    "    total_images = 0\n",
+    "\n",
+    "    for imgPath in imgPaths:\n",
+    "#         print(imgPath)\n",
+    "        img_name_info = imgPath.split(os.path.sep)\n",
+    "        imgLabel = img_name_info[2]\n",
+    "#         print('img label: ', imgLabel)\n",
+    "        imgName = img_name_info[3]\n",
+    "#         print('img name: ', imgName)\n",
+    "        file_ext = os.path.splitext(imgName)[1]\n",
+    "#         print('img ext: ', file_ext)\n",
+    "        \n",
+    "        new_path = resized_dir + imgLabel + '/' + imgName\n",
+    "#         print(new_path)\n",
+    "        if ext == 0:\n",
+    "            crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
+    "        elif ext == 1:\n",
+    "            # TIF file\n",
+    "            if file_ext == '.TIF':\n",
+    "                crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
+    "            else:\n",
+    "                break\n",
+    "        elif ext == 2:\n",
+    "            # TIF file\n",
+    "            if file_ext == '.RAW':\n",
+    "                crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
+    "            else:\n",
+    "                break\n",
+    "                \n",
+    "        total_images += 1\n",
+    "        \n",
+    "#         annotations_dir_list = os.listdir(annotations_dir + breed)\n",
+    "#         img_list = [data_dir + breed + '/' + i for i in dir_list]\n",
+    "#         os.makedirs(cropped_dir + breed)\n",
+    "\n",
+    "#         for file in img_list:\n",
+    "#             annotation_path = annotations_dir + breed + '/' + os.path.basename(file[:-4])\n",
+    "#             newpath = cropped_dir + breed + '/' + os.path.basename(file)\n",
+    "#             save_cropped_img(file, annotation_path, newpath)\n",
+    "#             total_images += 1\n",
+    "    \n",
+    "    print(\"total images resized\", total_images)\n",
+    "\n",
+    "resize_images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keras image data readers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 977 images belonging to 2 classes.\n",
+      "Found 243 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "datagen = ImageDataGenerator(\n",
+    "        shear_range=0.1,\n",
+    "        zoom_range=0.1,\n",
+    "        brightness_range=[0.9,1.1],\n",
+    "        horizontal_flip=True,\n",
+    "        validation_split=testsplit,\n",
+    "        preprocessing_function=preprocess_input\n",
+    ")\n",
+    "\n",
+    "train_generator = datagen.flow_from_directory(\n",
+    "        resized_dir,\n",
+    "        target_size=(targetx, targety),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True,\n",
+    "        seed=seed,\n",
+    "        subset=\"training\"\n",
+    ")\n",
+    "\n",
+    "test_generator = datagen.flow_from_directory(\n",
+    "        resized_dir,\n",
+    "        target_size=(targetx, targety),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=False,\n",
+    "        seed=seed,\n",
+    "        subset=\"validation\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./img_data/resized/Negative/3842558_landsat_8_rgb.tif\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4c43f1a3d0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "img = train_generator.filepaths[np.random.random_integers(low=0, high=train_generator.samples)]\n",
+    "print(img)\n",
+    "img = mpimg.imread(img)\n",
+    "plt.imshow(img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GPU configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3', '/job:localhost/replica:0/task:0/device:GPU:4', '/job:localhost/replica:0/task:0/device:GPU:5', '/job:localhost/replica:0/task:0/device:GPU:6', '/job:localhost/replica:0/task:0/device:GPU:7')\n",
+      "Number of devices: 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a MirroredStrategy.\n",
+    "strategy = tf.distribute.MirroredStrategy()\n",
+    "print('Number of devices: {}'.format(strategy.num_replicas_in_sync))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keras callbacks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
+      "WARNING:tensorflow:`batch_size` is no longer needed in the `TensorBoard` Callback and will be ignored in TensorFlow 2.0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "checkpoint = ModelCheckpoint('landslide_classifier_efficientnet.h5',\n",
+    "                             monitor='val_accuracy',\n",
+    "                             save_best_only=True,\n",
+    "                             verbose=1,\n",
+    "                             mode='auto',\n",
+    "                             save_weights_only=False,\n",
+    "                             period=1)\n",
+    "\n",
+    "#https://github.com/keras-team/keras/issues/3358\n",
+    "tensorboard = TensorBoard(log_dir=\"./logs-\"+dt.datetime.now().strftime(\"%m%d%Y%H%M%S\"),\n",
+    "                            histogram_freq=0,\n",
+    "                            batch_size=batch_size,\n",
+    "                            write_graph=False,\n",
+    "                            update_freq='epoch')\n",
+    "\n",
+    "def epoch_end(epoch, logs):\n",
+    "    message = \"End of epoch \"+str(epoch)+\". Learning rate: \"+str(K.eval(model.optimizer.lr))\n",
+    "    os.system('echo '+message)\n",
+    "\n",
+    "def epoch_begin(epoch, logs):\n",
+    "    print(\"Learning rate: \", K.eval(model.optimizer.lr))\n",
+    "    \n",
+    "def train_begin(logs):\n",
+    "    os.system(\"echo Beginning training\")\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor='val_accuracy',\n",
+    "                          min_delta=.0001,\n",
+    "                          patience=50,\n",
+    "                          verbose=1,\n",
+    "                          mode='auto',\n",
+    "                          baseline=None,\n",
+    "                          restore_best_weights=True)\n",
+    "\n",
+    "reducelr = ReduceLROnPlateau(monitor='val_accuracy',\n",
+    "                             factor=np.sqrt(.1),\n",
+    "                             patience=5,\n",
+    "                             verbose=1,\n",
+    "                             mode='auto',\n",
+    "                             min_delta=.0001,\n",
+    "                             cooldown=0,\n",
+    "                             min_lr=0.0000001)\n",
+    "\n",
+    "lambdacb = LambdaCallback(on_epoch_begin=epoch_begin,\n",
+    "                          on_epoch_end=epoch_end,\n",
+    "                          on_batch_begin=None,\n",
+    "                          on_batch_end=None,\n",
+    "                          on_train_begin=train_begin,\n",
+    "                          on_train_end=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define new top layers and compile model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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"
+     ]
+    }
+   ],
+   "source": [
+    "with strategy.scope():\n",
+    "#     base_model = MobileNetV2(include_top=False, weights='imagenet', input_shape=(targetx, targety, 3))\n",
+    "    base_model = EfficientNetB0(include_top=False, weights='imagenet', input_shape=(targetx, targety, 3))\n",
+    "\n",
+    "    x = base_model.output\n",
+    "    x = GlobalAveragePooling2D()(x)\n",
+    "    # x = Dropout(rate = .2)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Dense(1280, activation='relu',  kernel_initializer=glorot_uniform(seed), bias_initializer='zeros')(x)\n",
+    "    # x = Dropout(rate = .2)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    predictions = Dense(classes, activation='softmax', kernel_initializer='random_uniform', bias_initializer='zeros')(x)\n",
+    "\n",
+    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
+    "\n",
+    "    optimizer = Adam(lr=learning_rate)\n",
+    "    # optimizer = RMSprop(lr=learning_rate)\n",
+    "\n",
+    "    loss = \"categorical_crossentropy\"\n",
+    "    # loss = \"kullback_leibler_divergence\"\n",
+    "\n",
+    "    for layer in model.layers:\n",
+    "        layer.trainable = True\n",
+    "    # for layer in model.layers[-2:]:\n",
+    "    #     layer.trainable = True\n",
+    "\n",
+    "    model.compile(optimizer=optimizer,\n",
+    "                  loss=loss,\n",
+    "                  metrics=[\"accuracy\"])\n",
+    "\n",
+    "    model.summary()\n",
+    "    # for i, layer in enumerate(model.layers):\n",
+    "    #     print(i, layer.name, layer.trainable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "\n",
+    "params = model.fit_generator(generator=train_generator, \n",
+    "                                steps_per_epoch=len(train_generator), \n",
+    "                                validation_data=test_generator, \n",
+    "                                validation_steps=len(test_generator),\n",
+    "                                epochs=epochs,\n",
+    "                                callbacks=[reducelr, earlystop, lambdacb, tensorboard, checkpoint])"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "%%time\n",
+    "params = model.fit(generator=train_generator, \n",
+    "                                steps_per_epoch=len(train_generator), \n",
+    "                                validation_data=test_generator, \n",
+    "                                validation_steps=len(test_generator),\n",
+    "                                epochs=epochs,\n",
+    "                                callbacks=[reducelr, earlystop, lambdacb, tensorboard, checkpoint])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and test loss/accuracy graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plt.subplot(2, 1, 1)\n",
+    "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.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# plt.subplot(2, 1, 2)\n",
+    "plt.title('Training and test loss')\n",
+    "plt.plot(params.epoch, params.history['loss'], label='Training loss')\n",
+    "plt.plot(params.epoch, params.history['val_loss'], label='Test loss')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Randomly test an image from the test set\n",
+    "\n",
+    "# model.load_weights('dog_breed_classifier.h5')\n",
+    "\n",
+    "imageno=np.random.random_integers(low=0, high=test_generator.samples)\n",
+    "print('predict with test number: ', imageno)\n",
+    "\n",
+    "name = test_generator.filepaths[imageno]\n",
+    "print(name)\n",
+    "plt.imshow(mpimg.imread(name))\n",
+    "\n",
+    "img = Image.open(test_generator.filepaths[imageno]).resize((targetx, targety))\n",
+    "probabilities = model.predict(preprocess_input(np.expand_dims(img, axis=0)))\n",
+    "breed_list = tuple(zip(test_generator.class_indices.values(), test_generator.class_indices.keys()))\n",
+    "\n",
+    "for i in probabilities[0].argsort()[-5:][::-1]: \n",
+    "    print(probabilities[0][i], \"  :  \" , breed_list[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample for paper test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# path for landslide \n",
+    "#./data/reized/WithLandslide/25_94_Large_cropped_6.TIF\n",
+    "# ./data/reized/WithLandslide/44_-123_Large_cropped_4.TIF\n",
+    "# ./imgtest/27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20130915_20170502_01_T1.TIF\n",
+    "# ./imgtest/27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20140918_20170419_01_T1.TIF\n",
+    "img_test = Image.open(test_dir + \"27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20140918_20170419_01_T1.TIF\").resize((targetx, targety))\n",
+    "plt.imshow(img_test,origin='lower')\n",
+    "probabilities = model.predict(preprocess_input(np.expand_dims(img, axis=0)))\n",
+    "breed_list = tuple(zip(test_generator.class_indices.values(), test_generator.class_indices.keys()))\n",
+    "\n",
+    "for i in probabilities[0].argsort()[-5:][::-1]: \n",
+    "    print(probabilities[0][i], \"  :  \" , breed_list[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classification report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_generator.reset()\n",
+    "predictions = model.predict_generator(test_generator, steps=len(test_generator))\n",
+    "y = np.argmax(predictions, axis=1)\n",
+    "\n",
+    "print('Classification Report')\n",
+    "cr = classification_report(y_true=test_generator.classes, y_pred=y, target_names=test_generator.class_indices)\n",
+    "print(cr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print('Confusion Matrix')\n",
+    "cm = confusion_matrix(test_generator.classes, y)\n",
+    "df = pd.DataFrame(cm, columns=test_generator.class_indices)\n",
+    "plt.figure(figsize=(80,80))\n",
+    "sn.heatmap(df, annot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reference: \n",
+    "## Transfer learning with Keras and MobileNet V2\n",
+    "#### https://www.kaggle.com/devang/transfer-learning-with-keras-and-mobilenet-v2/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "viplab-gpu",
+   "language": "python",
+   "name": "viplab-gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb b/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb
index 2341484..95bbcb4 100644
--- a/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb
+++ b/.ipynb_checkpoints/train-mobilenetv2-checkpoint.ipynb
@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "['Fri Sep  4 02:44:42 2020       ',\n",
+       "['Fri Sep  4 06:59:06 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%   35C    P8     8W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   32C    P8     9W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
-       " '| 30%   33C    P8     6W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   31C    P8     6W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
-       " '| 30%   31C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   30C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
-       " '| 30%   33C    P8     9W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   31C    P8    10W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
-       " '| 25%   32C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 25%   30C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
-       " '| 20%   24C    P8     9W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   20C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
-       " '| 20%   32C    P8     9W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   28C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
-       " '| 20%   29C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   27C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '                                                                               ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
     "\n",
     "data_dir = \"../LandslideDataset/\"\n",
     "resized_dir = \"./img_data/resized/\"\n",
-    "test_dir = \"./img_data/test\""
+    "test_dir = \"./img_data/test/\""
    ]
   },
   {
@@ -264,8 +264,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 2015 images belonging to 2 classes.\n",
-      "Found 502 images belonging to 2 classes.\n"
+      "Found 976 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/Positive/3843713_landsat_7_rgb.tif\n"
+      "./img_data/resized/Negative/3844923_landsat_8_rgb.tif\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f91b4bdd890>"
+       "<matplotlib.image.AxesImage at 0x7f740d463b10>"
       ]
      },
      "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>"
       ]
@@ -356,15 +356,6 @@
     "### GPU configuration"
    ]
   },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "physical_devices = tf.config.experimental.list_physical_devices('GPU') \n",
-    "for physical_device in physical_devices: \n",
-    "    tf.config.experimental.set_memory_growth(physical_device, True)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -434,7 +425,7 @@
     "\n",
     "earlystop = EarlyStopping(monitor='val_accuracy',\n",
     "                          min_delta=.0001,\n",
-    "                          patience=20,\n",
+    "                          patience=50,\n",
     "                          verbose=1,\n",
     "                          mode='auto',\n",
     "                          baseline=None,\n",
@@ -860,44 +851,6 @@
     "    #     print(i, layer.name, layer.trainable)"
    ]
   },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "with strategy.scope():\n",
-    "    base_model = MobileNetV2(include_top=False, weights='imagenet', input_shape=(targetx, targety, 3))\n",
-    "\n",
-    "    x = base_model.output\n",
-    "    x = GlobalAveragePooling2D()(x)\n",
-    "    # x = Dropout(rate = .2)(x)\n",
-    "    x = BatchNormalization()(x)\n",
-    "    x = Dense(1280, activation='relu',  kernel_initializer=glorot_uniform(seed), bias_initializer='zeros')(x)\n",
-    "    # x = Dropout(rate = .2)(x)\n",
-    "    x = BatchNormalization()(x)\n",
-    "    predictions = Dense(classes, activation='softmax', kernel_initializer='random_uniform', bias_initializer='zeros')(x)\n",
-    "\n",
-    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
-    "\n",
-    "    optimizer = Adam(lr=learning_rate)\n",
-    "    # optimizer = RMSprop(lr=learning_rate)\n",
-    "\n",
-    "    loss = \"categorical_crossentropy\"\n",
-    "    # loss = \"kullback_leibler_divergence\"\n",
-    "\n",
-    "    for layer in model.layers:\n",
-    "        layer.trainable = True\n",
-    "    # for layer in model.layers[-2:]:\n",
-    "    #     layer.trainable = True\n",
-    "\n",
-    "    model.compile(optimizer=optimizer,\n",
-    "                  loss=loss,\n",
-    "                  metrics=[\"accuracy\"])\n",
-    "\n",
-    "    model.summary()\n",
-    "    # for i, layer in enumerate(model.layers):\n",
-    "    #     print(i, layer.name, layer.trainable)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -907,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -921,113 +874,1247 @@
       "Epoch 1/250\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",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.5027 - loss: 0.8569\n",
-      "Epoch 00001: val_accuracy improved from -inf to 0.50398, saving model to landslide_classifier.h5\n",
-      "14/14 [==============================] - 49s 3s/step - accuracy: 0.5027 - loss: 0.8569 - val_accuracy: 0.5040 - val_loss: 0.7772 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 2/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.5638 - loss: 0.7553\n",
-      "Epoch 00002: val_accuracy improved from 0.50398 to 0.53785, saving model to landslide_classifier.h5\n",
-      "14/14 [==============================] - 38s 3s/step - accuracy: 0.5638 - loss: 0.7553 - val_accuracy: 0.5378 - val_loss: 0.7356 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 3/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6149 - loss: 0.6962\n",
-      "Epoch 00003: val_accuracy did not improve from 0.53785\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6149 - loss: 0.6962 - val_accuracy: 0.5359 - val_loss: 0.7441 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 4/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6566 - loss: 0.6314\n",
-      "Epoch 00004: val_accuracy improved from 0.53785 to 0.55578, saving model to landslide_classifier.h5\n",
-      "14/14 [==============================] - 38s 3s/step - accuracy: 0.6566 - loss: 0.6314 - val_accuracy: 0.5558 - val_loss: 0.7152 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 5/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6526 - loss: 0.6359\n",
-      "Epoch 00005: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6526 - loss: 0.6359 - val_accuracy: 0.5398 - val_loss: 0.7142 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 6/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6774 - loss: 0.6009\n",
-      "Epoch 00006: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6774 - loss: 0.6009 - val_accuracy: 0.5339 - val_loss: 0.7319 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 7/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6804 - loss: 0.5988\n",
-      "Epoch 00007: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6804 - loss: 0.5988 - val_accuracy: 0.5378 - val_loss: 0.7520 - lr: 1.0000e-04\n",
-      "Learning rate:  1e-04\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",
       "Epoch 8/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7017 - loss: 0.5779\n",
-      "Epoch 00008: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7017 - loss: 0.5779 - val_accuracy: 0.5120 - val_loss: 0.7925 - lr: 1.0000e-04\n",
-      "Learning rate:  1e-04\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",
       "Epoch 9/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7112 - loss: 0.5569\n",
-      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
-      "\n",
-      "Epoch 00009: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7112 - loss: 0.5569 - val_accuracy: 0.5100 - val_loss: 0.8326 - lr: 1.0000e-04\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",
       "Epoch 10/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7270 - loss: 0.5282\n",
-      "Epoch 00010: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7270 - loss: 0.5282 - val_accuracy: 0.5159 - val_loss: 0.8136 - lr: 3.1623e-05\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",
       "Epoch 11/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7236 - loss: 0.5335\n",
-      "Epoch 00011: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7236 - loss: 0.5335 - val_accuracy: 0.5020 - val_loss: 0.8296 - lr: 3.1623e-05\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",
       "Epoch 12/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7280 - loss: 0.5409\n",
-      "Epoch 00012: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7280 - loss: 0.5409 - val_accuracy: 0.5299 - val_loss: 0.8433 - lr: 3.1623e-05\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",
       "Epoch 13/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7504 - loss: 0.5148\n",
-      "Epoch 00013: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7504 - loss: 0.5148 - val_accuracy: 0.5319 - val_loss: 0.8690 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4051 - accuracy: 0.8125\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",
       "Epoch 14/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7449 - loss: 0.5103\n",
-      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
-      "\n",
-      "Epoch 00014: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7449 - loss: 0.5103 - val_accuracy: 0.5359 - val_loss: 0.9017 - lr: 3.1623e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3902 - accuracy: 0.8299\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",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 15/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7672 - loss: 0.4831\n",
-      "Epoch 00015: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7672 - loss: 0.4831 - val_accuracy: 0.5279 - val_loss: 0.9036 - lr: 1.0000e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3942 - accuracy: 0.8289\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",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 16/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7538 - loss: 0.4952\n",
-      "Epoch 00016: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7538 - loss: 0.4952 - val_accuracy: 0.5120 - val_loss: 0.9364 - lr: 1.0000e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3917 - accuracy: 0.8371\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",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 17/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7623 - loss: 0.4870\n",
-      "Epoch 00017: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7623 - loss: 0.4870 - val_accuracy: 0.5378 - val_loss: 0.9247 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3561 - accuracy: 0.8689\n",
+      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
+      "\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",
       "Epoch 18/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7350 - loss: 0.5125\n",
-      "Epoch 00018: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7350 - loss: 0.5125 - val_accuracy: 0.5418 - val_loss: 0.9386 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3558 - accuracy: 0.8473\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",
       "Epoch 19/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7717 - loss: 0.4804\n",
-      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8617\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",
+      "Learning rate:  9.999999e-06\n",
+      "Epoch 20/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3553 - accuracy: 0.8432\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",
+      "Learning rate:  9.999999e-06\n",
+      "Epoch 21/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3665 - accuracy: 0.8443\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",
+      "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",
-      "Epoch 00019: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7717 - loss: 0.4804 - val_accuracy: 0.5339 - val_loss: 0.9490 - lr: 1.0000e-05\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",
-      "Epoch 20/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7772 - loss: 0.4741\n",
-      "Epoch 00020: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7772 - loss: 0.4741 - val_accuracy: 0.5219 - val_loss: 0.9684 - lr: 3.1623e-06\n",
+      "Epoch 23/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8576\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",
-      "Epoch 21/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7628 - loss: 0.4934"
+      "Epoch 24/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3498 - accuracy: 0.8668\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",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 25/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3420 - accuracy: 0.8555\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",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 26/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3466 - accuracy: 0.8596\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",
+      "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",
+      "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",
+      "Epoch 28/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8596\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",
+      "Epoch 29/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3689 - accuracy: 0.8402\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",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 30/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8576\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",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 31/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3539 - accuracy: 0.8555\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",
+      "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",
+      "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",
+      "Epoch 33/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3303 - accuracy: 0.8648\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",
+      "Epoch 34/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3472 - accuracy: 0.8350\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",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 35/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3492 - accuracy: 0.8566\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",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 36/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8494\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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "Epoch 42/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8658\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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "Learning rate:  1e-07\n",
+      "Epoch 52/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3373 - accuracy: 0.8596\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 53/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.8525\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 54/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8443\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 55/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3343 - accuracy: 0.8689\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 56/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3468 - accuracy: 0.8637\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 57/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3403 - accuracy: 0.8627\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 58/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3415 - accuracy: 0.8494\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 59/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.8576\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 60/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3292 - accuracy: 0.8709\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 61/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3455 - accuracy: 0.8586\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 62/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3670 - accuracy: 0.8381\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 63/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.8678\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 64/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3288 - accuracy: 0.8648\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 65/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8699\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 66/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3437 - accuracy: 0.8566\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 67/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3523 - accuracy: 0.8453\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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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"
      ]
     }
    ],
@@ -1064,9 +2151,34 @@
   },
   {
    "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",
@@ -1093,9 +2205,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "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"
+     ]
+    },
+    {
+     "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",
@@ -1125,9 +2260,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9843275   :   (0, 'Negative')\n",
+      "0.015672525   :   (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": [
     "# path for landslide \n",
     "#./data/reized/WithLandslide/25_94_Large_cropped_6.TIF\n",
@@ -1152,9 +2308,29 @@
   },
   {
    "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       0.63      0.58      0.61       122\n",
+      "    Positive       0.61      0.66      0.63       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",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "test_generator.reset()\n",
     "predictions = model.predict_generator(test_generator, steps=len(test_generator))\n",
@@ -1174,9 +2350,39 @@
   },
   {
    "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 0x7f6ebc088d10>"
+      ]
+     },
+     "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",
diff --git a/train-efficientNet.ipynb b/train-efficientNet.ipynb
new file mode 100644
index 0000000..924ce98
--- /dev/null
+++ b/train-efficientNet.ipynb
@@ -0,0 +1,1884 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Training + Testing Landslide Prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Created: <b>Trong-An Bui</b> <br>\n",
+    "Organization: <b>ViP Lab - National Chi Nan University, Taiwan</b> <br/>\n",
+    "Email: trongan93@gmail.com <br/>\n",
+    "Project: <b>AI Cube Satellite</b> <br/>\n",
+    "Advisor: <b>Prof. Pei-Jun Lee</b>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "py 3.7.7\n",
+      "tf 2.3.0\n",
+      "keras 2.4.0\n",
+      "mem 128555.12109375\n",
+      "cpu 40\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['Sat Sep  5 08:09:42 2020       ',\n",
+       " '+-----------------------------------------------------------------------------+',\n",
+       " '| NVIDIA-SMI 440.100      Driver Version: 440.100      CUDA Version: 10.2     |',\n",
+       " '|-------------------------------+----------------------+----------------------+',\n",
+       " '| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |',\n",
+       " '| 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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
+       " '| 30%   51C    P0    37W / 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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
+       " '| 31%   55C    P0    44W / 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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
+       " '| 18%   22C    P0    60W / 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",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
+       " '| 15%   26C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '+-------------------------------+----------------------+----------------------+',\n",
+       " '                                                                               ',\n",
+       " '+-----------------------------------------------------------------------------+',\n",
+       " '| Processes:                                                       GPU Memory |',\n",
+       " '|  GPU       PID   Type   Process name                             Usage      |',\n",
+       " '|=============================================================================|',\n",
+       " '|  No running processes found                                                 |',\n",
+       " '+-----------------------------------------------------------------------------+']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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",
+    "\n",
+    "from platform import python_version\n",
+    "import warnings\n",
+    "import time\n",
+    "import datetime as dt\n",
+    "from sklearn.metrics import classification_report, confusion_matrix\n",
+    "import multiprocessing as mp\n",
+    "import shutil\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
+    "\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import backend as K\n",
+    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+    "# from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input, decode_predictions\n",
+    "from tensorflow.keras.applications.efficientnet import EfficientNetB0, preprocess_input, decode_predictions\n",
+    "from tensorflow.keras.models import *\n",
+    "from tensorflow.keras.layers import *\n",
+    "from tensorflow.keras.optimizers import *\n",
+    "from tensorflow.keras.utils import *\n",
+    "from tensorflow.keras.callbacks import *\n",
+    "from tensorflow.keras.initializers import *\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sn\n",
+    "\n",
+    "from PIL import Image\n",
+    "import xml.etree.ElementTree as ET\n",
+    "import psutil\n",
+    "import random\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "%matplotlib inline\n",
+    "\n",
+    "print(\"py\", python_version())\n",
+    "print(\"tf\", tf.__version__)\n",
+    "print(\"keras\", tf.keras.__version__)\n",
+    "mem = psutil.virtual_memory()\n",
+    "print(\"mem\", mem.total/1024/1024)\n",
+    "cpu = mp.cpu_count()\n",
+    "print(\"cpu\", cpu)\n",
+    "\n",
+    "%system nvidia-smi\n",
+    "#%system rocm-smi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epochs = 250\n",
+    "batch_size = 150\n",
+    "testsplit = .2\n",
+    "targetx = 224\n",
+    "targety = 224\n",
+    "learning_rate = 0.0001\n",
+    "classes = 2\n",
+    "seed = random.randint(1, 1000)\n",
+    "\n",
+    "data_dir = \"../LandslideDataset/\"\n",
+    "resized_dir = \"./img_data/resized/\"\n",
+    "test_dir = \"./img_data/test/\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Crop images using provided annotations"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "%system rm -rf $resized_dir\n",
+    "%system mkdir $resized_dir\n",
+    "\n",
+    "\n",
+    "def crop_and_save_img(img_path, new_dir_path, original_ext_filter = 0):\n",
+    "#     print('trongan93:', new_dir_path)\n",
+    "    image = Image.open(img_path)\n",
+    "    image = image.resize((targetx,targety))\n",
+    "    image.save(new_dir_path)\n",
+    "\n",
+    "def resize_images(ext = 0):\n",
+    "    imgPaths = sorted(list(paths.list_images(data_dir)))\n",
+    "#     print('images len: ', len(imgPaths))\n",
+    "    \n",
+    "    labels_dir = os.listdir(data_dir)\n",
+    "    for label_dir in labels_dir:\n",
+    "        cropper_label_dir = resized_dir+label_dir\n",
+    "        if not os.path.exists(cropper_label_dir):\n",
+    "            os.makedirs(cropper_label_dir)\n",
+    "            print(\"sucessfull make dir for label: \", cropper_label_dir)\n",
+    "    \n",
+    "    \n",
+    "    total_images = 0\n",
+    "\n",
+    "    for imgPath in imgPaths:\n",
+    "#         print(imgPath)\n",
+    "        img_name_info = imgPath.split(os.path.sep)\n",
+    "        imgLabel = img_name_info[2]\n",
+    "#         print('img label: ', imgLabel)\n",
+    "        imgName = img_name_info[3]\n",
+    "#         print('img name: ', imgName)\n",
+    "        file_ext = os.path.splitext(imgName)[1]\n",
+    "#         print('img ext: ', file_ext)\n",
+    "        \n",
+    "        new_path = resized_dir + imgLabel + '/' + imgName\n",
+    "#         print(new_path)\n",
+    "        if ext == 0:\n",
+    "            crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
+    "        elif ext == 1:\n",
+    "            # TIF file\n",
+    "            if file_ext == '.TIF':\n",
+    "                crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
+    "            else:\n",
+    "                break\n",
+    "        elif ext == 2:\n",
+    "            # TIF file\n",
+    "            if file_ext == '.RAW':\n",
+    "                crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
+    "            else:\n",
+    "                break\n",
+    "                \n",
+    "        total_images += 1\n",
+    "        \n",
+    "#         annotations_dir_list = os.listdir(annotations_dir + breed)\n",
+    "#         img_list = [data_dir + breed + '/' + i for i in dir_list]\n",
+    "#         os.makedirs(cropped_dir + breed)\n",
+    "\n",
+    "#         for file in img_list:\n",
+    "#             annotation_path = annotations_dir + breed + '/' + os.path.basename(file[:-4])\n",
+    "#             newpath = cropped_dir + breed + '/' + os.path.basename(file)\n",
+    "#             save_cropped_img(file, annotation_path, newpath)\n",
+    "#             total_images += 1\n",
+    "    \n",
+    "    print(\"total images resized\", total_images)\n",
+    "\n",
+    "resize_images()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keras image data readers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 977 images belonging to 2 classes.\n",
+      "Found 243 images belonging to 2 classes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "datagen = ImageDataGenerator(\n",
+    "        shear_range=0.1,\n",
+    "        zoom_range=0.1,\n",
+    "        brightness_range=[0.9,1.1],\n",
+    "        horizontal_flip=True,\n",
+    "        validation_split=testsplit,\n",
+    "        preprocessing_function=preprocess_input\n",
+    ")\n",
+    "\n",
+    "train_generator = datagen.flow_from_directory(\n",
+    "        resized_dir,\n",
+    "        target_size=(targetx, targety),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=True,\n",
+    "        seed=seed,\n",
+    "        subset=\"training\"\n",
+    ")\n",
+    "\n",
+    "test_generator = datagen.flow_from_directory(\n",
+    "        resized_dir,\n",
+    "        target_size=(targetx, targety),\n",
+    "        batch_size=batch_size,\n",
+    "        class_mode='categorical',\n",
+    "        shuffle=False,\n",
+    "        seed=seed,\n",
+    "        subset=\"validation\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./img_data/resized/Positive/3845306_landsat_8_rgb.tif\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f325bf66550>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "img = train_generator.filepaths[np.random.random_integers(low=0, high=train_generator.samples)]\n",
+    "print(img)\n",
+    "img = mpimg.imread(img)\n",
+    "plt.imshow(img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### GPU configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3', '/job:localhost/replica:0/task:0/device:GPU:4', '/job:localhost/replica:0/task:0/device:GPU:5', '/job:localhost/replica:0/task:0/device:GPU:6', '/job:localhost/replica:0/task:0/device:GPU:7')\n",
+      "Number of devices: 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a MirroredStrategy.\n",
+    "strategy = tf.distribute.MirroredStrategy()\n",
+    "print('Number of devices: {}'.format(strategy.num_replicas_in_sync))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keras callbacks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
+      "WARNING:tensorflow:`batch_size` is no longer needed in the `TensorBoard` Callback and will be ignored in TensorFlow 2.0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "checkpoint = ModelCheckpoint('landslide_classifier_efficientnet.h5',\n",
+    "                             monitor='val_accuracy',\n",
+    "                             save_best_only=True,\n",
+    "                             verbose=1,\n",
+    "                             mode='auto',\n",
+    "                             save_weights_only=False,\n",
+    "                             period=1)\n",
+    "\n",
+    "#https://github.com/keras-team/keras/issues/3358\n",
+    "tensorboard = TensorBoard(log_dir=\"./logs-\"+dt.datetime.now().strftime(\"%m%d%Y%H%M%S\"),\n",
+    "                            histogram_freq=0,\n",
+    "                            batch_size=batch_size,\n",
+    "                            write_graph=False,\n",
+    "                            update_freq='epoch')\n",
+    "\n",
+    "def epoch_end(epoch, logs):\n",
+    "    message = \"End of epoch \"+str(epoch)+\". Learning rate: \"+str(K.eval(model.optimizer.lr))\n",
+    "    os.system('echo '+message)\n",
+    "\n",
+    "def epoch_begin(epoch, logs):\n",
+    "    print(\"Learning rate: \", K.eval(model.optimizer.lr))\n",
+    "    \n",
+    "def train_begin(logs):\n",
+    "    os.system(\"echo Beginning training\")\n",
+    "\n",
+    "earlystop = EarlyStopping(monitor='val_accuracy',\n",
+    "                          min_delta=.0001,\n",
+    "                          patience=50,\n",
+    "                          verbose=1,\n",
+    "                          mode='auto',\n",
+    "                          baseline=None,\n",
+    "                          restore_best_weights=True)\n",
+    "\n",
+    "reducelr = ReduceLROnPlateau(monitor='val_accuracy',\n",
+    "                             factor=np.sqrt(.1),\n",
+    "                             patience=5,\n",
+    "                             verbose=1,\n",
+    "                             mode='auto',\n",
+    "                             min_delta=.0001,\n",
+    "                             cooldown=0,\n",
+    "                             min_lr=0.0000001)\n",
+    "\n",
+    "lambdacb = LambdaCallback(on_epoch_begin=epoch_begin,\n",
+    "                          on_epoch_end=epoch_end,\n",
+    "                          on_batch_begin=None,\n",
+    "                          on_batch_end=None,\n",
+    "                          on_train_begin=train_begin,\n",
+    "                          on_train_end=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define new top layers and compile model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    }
+   ],
+   "source": [
+    "with strategy.scope():\n",
+    "#     base_model = MobileNetV2(include_top=False, weights='imagenet', input_shape=(targetx, targety, 3))\n",
+    "    base_model = EfficientNetB0(include_top=False, weights='imagenet', input_shape=(targetx, targety, 3))\n",
+    "\n",
+    "    x = base_model.output\n",
+    "    x = GlobalAveragePooling2D()(x)\n",
+    "    # x = Dropout(rate = .2)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Dense(1280, activation='relu',  kernel_initializer=glorot_uniform(seed), bias_initializer='zeros')(x)\n",
+    "    # x = Dropout(rate = .2)(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    predictions = Dense(classes, activation='softmax', kernel_initializer='random_uniform', bias_initializer='zeros')(x)\n",
+    "\n",
+    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
+    "\n",
+    "    optimizer = Adam(lr=learning_rate)\n",
+    "    # optimizer = RMSprop(lr=learning_rate)\n",
+    "\n",
+    "    loss = \"categorical_crossentropy\"\n",
+    "    # loss = \"kullback_leibler_divergence\"\n",
+    "\n",
+    "    for layer in model.layers:\n",
+    "        layer.trainable = True\n",
+    "    # for layer in model.layers[-2:]:\n",
+    "    #     layer.trainable = True\n",
+    "\n",
+    "    model.compile(optimizer=optimizer,\n",
+    "                  loss=loss,\n",
+    "                  metrics=[\"accuracy\"])\n",
+    "\n",
+    "    model.summary()\n",
+    "    # for i, layer in enumerate(model.layers):\n",
+    "    #     print(i, layer.name, layer.trainable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fit model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "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: 0.9618 - 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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "\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 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",
+      "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",
+      "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",
+      "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",
+      "\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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "Learning rate:  1e-07\n",
+      "Epoch 82/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3845 - accuracy: 0.8280\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 83/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8045\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",
+      "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",
+      "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",
+      "Learning rate:  1e-07\n",
+      "Epoch 85/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3926 - accuracy: 0.8147\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 86/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4118 - accuracy: 0.8096\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 87/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4029 - accuracy: 0.8270\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 88/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3753 - accuracy: 0.8454\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",
+      "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",
+      "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",
+      "Learning rate:  1e-07\n",
+      "Epoch 90/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4183 - accuracy: 0.8096\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 91/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3801 - accuracy: 0.8362\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 92/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4152 - accuracy: 0.8025\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 93/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4167 - accuracy: 0.8076\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",
+      "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",
+      "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"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "params = model.fit_generator(generator=train_generator, \n",
+    "                                steps_per_epoch=len(train_generator), \n",
+    "                                validation_data=test_generator, \n",
+    "                                validation_steps=len(test_generator),\n",
+    "                                epochs=epochs,\n",
+    "                                callbacks=[reducelr, earlystop, lambdacb, tensorboard, checkpoint])"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "%%time\n",
+    "params = model.fit(generator=train_generator, \n",
+    "                                steps_per_epoch=len(train_generator), \n",
+    "                                validation_data=test_generator, \n",
+    "                                validation_steps=len(test_generator),\n",
+    "                                epochs=epochs,\n",
+    "                                callbacks=[reducelr, earlystop, lambdacb, tensorboard, checkpoint])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and test loss/accuracy graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "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": {
+      "image/png": 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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",
+    "plt.plot(params.epoch, params.history['accuracy'], label='Training accuracy')\n",
+    "plt.plot(params.epoch, params.history['val_accuracy'], label='Test accuracy')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# plt.subplot(2, 1, 2)\n",
+    "plt.title('Training and test loss')\n",
+    "plt.plot(params.epoch, params.history['loss'], label='Training loss')\n",
+    "plt.plot(params.epoch, params.history['val_loss'], label='Test loss')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+     ]
+    },
+    {
+     "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",
+    "# model.load_weights('dog_breed_classifier.h5')\n",
+    "\n",
+    "imageno=np.random.random_integers(low=0, high=test_generator.samples)\n",
+    "print('predict with test number: ', imageno)\n",
+    "\n",
+    "name = test_generator.filepaths[imageno]\n",
+    "print(name)\n",
+    "plt.imshow(mpimg.imread(name))\n",
+    "\n",
+    "img = Image.open(test_generator.filepaths[imageno]).resize((targetx, targety))\n",
+    "probabilities = model.predict(preprocess_input(np.expand_dims(img, axis=0)))\n",
+    "breed_list = tuple(zip(test_generator.class_indices.values(), test_generator.class_indices.keys()))\n",
+    "\n",
+    "for i in probabilities[0].argsort()[-5:][::-1]: \n",
+    "    print(probabilities[0][i], \"  :  \" , breed_list[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample for paper test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.66556025   :   (1, 'Positive')\n",
+      "0.33443972   :   (0, 'Negative')\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": [
+    "# path for landslide \n",
+    "#./data/reized/WithLandslide/25_94_Large_cropped_6.TIF\n",
+    "# ./data/reized/WithLandslide/44_-123_Large_cropped_4.TIF\n",
+    "# ./imgtest/27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20130915_20170502_01_T1.TIF\n",
+    "# ./imgtest/27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20140918_20170419_01_T1.TIF\n",
+    "img_test = Image.open(test_dir + \"27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20140918_20170419_01_T1.TIF\").resize((targetx, targety))\n",
+    "plt.imshow(img_test,origin='lower')\n",
+    "probabilities = model.predict(preprocess_input(np.expand_dims(img, axis=0)))\n",
+    "breed_list = tuple(zip(test_generator.class_indices.values(), test_generator.class_indices.keys()))\n",
+    "\n",
+    "for i in probabilities[0].argsort()[-5:][::-1]: \n",
+    "    print(probabilities[0][i], \"  :  \" , breed_list[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Classification report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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       0.60      0.61      0.61       122\n",
+      "    Positive       0.61      0.60      0.60       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",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_generator.reset()\n",
+    "predictions = model.predict_generator(test_generator, steps=len(test_generator))\n",
+    "y = np.argmax(predictions, axis=1)\n",
+    "\n",
+    "print('Classification Report')\n",
+    "cr = classification_report(y_true=test_generator.classes, y_pred=y, target_names=test_generator.class_indices)\n",
+    "print(cr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Confusion matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1bc155c090>"
+      ]
+     },
+     "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",
+    "df = pd.DataFrame(cm, columns=test_generator.class_indices)\n",
+    "plt.figure(figsize=(80,80))\n",
+    "sn.heatmap(df, annot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reference: \n",
+    "## Transfer learning with Keras and MobileNet V2\n",
+    "#### https://www.kaggle.com/devang/transfer-learning-with-keras-and-mobilenet-v2/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "viplab-gpu",
+   "language": "python",
+   "name": "viplab-gpu"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/train-mobilenetv2.ipynb b/train-mobilenetv2.ipynb
index 142dc51..9ae787b 100644
--- a/train-mobilenetv2.ipynb
+++ b/train-mobilenetv2.ipynb
@@ -44,7 +44,7 @@
     {
      "data": {
       "text/plain": [
-       "['Fri Sep  4 02:44:42 2020       ',\n",
+       "['Fri Sep  4 06:59:06 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%   35C    P8     8W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   32C    P8     9W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
-       " '| 30%   33C    P8     6W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   31C    P8     6W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
-       " '| 30%   31C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   30C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
-       " '| 30%   33C    P8     9W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
+       " '| 30%   31C    P8    10W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
-       " '| 25%   32C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 25%   30C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
-       " '| 20%   24C    P8     9W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   20C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
-       " '| 20%   32C    P8     9W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   28C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
-       " '| 20%   29C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
+       " '| 20%   27C    P2    50W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
        " '+-------------------------------+----------------------+----------------------+',\n",
        " '                                                                               ',\n",
        " '+-----------------------------------------------------------------------------+',\n",
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,8 +264,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 2015 images belonging to 2 classes.\n",
-      "Found 502 images belonging to 2 classes.\n"
+      "Found 976 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/Positive/3843713_landsat_7_rgb.tif\n"
+      "./img_data/resized/Negative/3844923_landsat_8_rgb.tif\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f91b4bdd890>"
+       "<matplotlib.image.AxesImage at 0x7f740d463b10>"
       ]
      },
      "execution_count": 4,
@@ -331,7 +331,7 @@
     },
     {
      "data": {
-      "image/png": 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3LcenJ7jD+Tixm2Y2k5NFOR92nG03qCo3rt/k+sEeXUq4w+OzM3Ym9NfuROklCccZ+o5vPLrH2Zd/B8bNJbXkJpydzbifXt6mC82FGVyg699tG6eRcfownfqb/NIv/w6qH+T9uX6LOz/0F3i1zoxAEkc8IXrI0ZW0MEhC13Zc2VvTtg2oM+wmihdcISGsROlVICuqCSW49aFM2FzYzDOTGxVnO1WmuUbNnZTD3HLQKae7HSdjocnCuu2QlJlnx31CMY72e8bJqG6UeeZ8WzjfDsylBsvVNmQRsiQQwS1YsJwEkYwGyUGtxvFmoDqcjyN9u0JzJpFAFNWGlBxUOC8jZVdQCUr0k+yz4QQWc3OqOUOdocYFGM3IC9ebkwYQViqJoKy8GnWhA1sx3AxsoVtKpRZnlSFrlBRt12EIZ8PIW++8i6SOpmlZraDalrHCdpzIOXOwWtH3LdM483gemefCOmeaJlEkqBgrMyxAji3yFOdCABP4gNuS8lcjWEe9VJp4sUhuc4BS0+zc2u955fk7eJk4G43z3Y6DvSu8cvsZhlJ49/iYXZmZqyGWKaXgSYO+M2iScrhecfPoiDZn5lLYjCOqEUkqggv0OXOnbXj+zsvsbr6ANhqAI04qLb/6qw8Zx19CpJKSht5ChHvvGG53L0maC7FNqeVTWhm2CGg+9PL9u3zr/v/Itz7wotK2f4Wf/KkGc0M1s7fqGYd9rrz0OW4dXaH1ErX0OEVGJqEn2O96+iYxlMpYSlwvnFxi/VhxhmlimCZyk9jb67i66lE1ZJ7pO+jbzLUrV/C58uh8g5HIS3nrdeLxbubJVlHNHO7vsR0mduOImNE2ivgiaGMRN12IkFBEwaszV2O0oDJJHau2YaiVcbvDHzxkLBMPTx5TvYB6CK0ulE0fY58JJ6AS9FCdDHNjWyfGaaZJGXFlL2VSk3GrzKWiLsExS3j6UisJyBJIbxN6NYpocBNJ0ZRYdS2HB4ecbgfqNPLOw8e4Cptd5fHJGce7DbUapRhd1+BmnJxtKFapZogrc63klFj1PW0KgM5tutw81eImVRNm88AGgK5rAai1YhZSl7EajSoJvQR1Vm1Dyg0u8NLzL3C4t8/d+/e5sn/AF168xb2Hx3y9EUaPEqqosRkGskCdK5ng2HfbLfceKZIicp8NA4iQVXGPCJglcJeuyaSkjA6bYSIpJDW++NNXmOzfQJOybhv6poVG+OVfmlH/x+y1gVzvhoksztv3vsVuHFiIi0VdB7UUqn2vGAxjmv4Rv/xLH3q5X3Pw03+Jv3x0FTZnPN4MPJlA0xXK0VXqfmgSNMFummi6jqsHe+yGkbNxJqtS1EkCbVb22yhZi1U224HB4Ce/7wVMlW/de0ybGyQFZVmscj5MbIaRzVTBYa8X1l3DWGqUm64USYFdmQXI7aF38NgkOMGAmQsmUHGmWsilYlYwK5xbpdTKCiM1DcUMEaHJ6aOXarHPhBNIKuylFJo6AVeYLFDdVhITQs5L9JRIuMsSfZIsIg3i360YjSZMYJ0BbZCUEF/45qz0bUO1liYLfd/y4NEjmjbzTHeVh8cnTF4ZhgFPCXddbgJkScxWmb0yjVNwu64gSjVjqJXzuTDNlSyKqJAkzs8XmklUmKywnSdElLVmFMUcWhH2Vy1n08ywnei7joO+4crhPussPNns+Prb93jrwTFdylgbF6zV4LEv9LVixpOTE+4+OaFcgIPulGoLeKq0EuIYRWhSnMNcjawJVaF6ODAUCoXNXNhMM1XgtR+tPHvtr/LitSNOhh33Hj6ic+P1r/8+D4+PmUqhuAeWkeDR4/s8On60OMq4BrM58zzC94rFGLac/d//G//LB15sabtnuXHzGa4eOKKhTyFlnvn+H+RHjg6Yp4FxHKiLmi/1DU1uaFNit92xdWfnhXa15q/8+Z+gvdbz3/y3/4SuAztzvBSsGg/OtwxlImtiKolyvuVkMzCVgliUuMOCfeniQuv7sssL5skWhWSTlTYlxA2vI40qoxnTNAa4mBRI2OwUr/T6yWqhz4QTuNCo+yK8Drl41EQFZ6qVtib6lEkEN14tKLikieRGtYLgiwotLuRSRgFQ3NgMI13TcLheoWIMw5Z33j1mnEbu3HoGT8K778ZiLVYCMEpRplDBkjO54uLoFByxiwKKyLKwiyMuaBJyyqHcutTOLxp1CS66z0rfJrLkqLDFmeYAQ1dNi4pwst1wuh2ZEmyqc//xCeNcuNL1uEbItblQbaJJkSGNc2U7z5zWymx1WVhRTrUawpScE22TaJpMSjkUcR5ochKYSsXdUYFVl+mSsJuDAitz5cnZGcmF83HLk80pnSRefPk1XnpZmOaZXTXWOWTFDx7f5+3797AawFnbNTwshXfuvQnH9xeVZ9y77Rbm+Qz4tEqL99vENH6Tt+9+k7ff/7Iqq8fvcP/mLWS7YzMOlBrb8aVXX2M/J8ak7IaRtsms9+6w38Abj875sesrctOw3Zxi5hGhPTaiA5gzU/Dql+ftAtPsUAIgTuoLxrQED4ScoVFBUrBiqzbHfhBYp6DEd7uCmZFSBErzEDuZ/3+gHIgIGYHeq+MS3i+1F4KPoNKqVDChLrRSxeIkLQC6CGiy0DuQRSheKdUoJgy1kFKOzYkwS2IcC7nrIWXGYcf5OCGLcMU88AWW+t/dSJpBL5BriWhJNDcVD5FL22ZSSixdTuiSpVSL1K6VhDQdNw7XXN3bY54rm93Ebp6ZirPqOh6fb3jrwWPWXWI3zuxwVubktuPVZ28wF+fJ2QZE2VVjckcl9O9J4njMFqn1wgREY0ssvrbJHO6vaXIbwphhROeJlJQkSq2GJnA3emnYbxMqRh4rM8rx6RlPTs/jvhFybKtGk2GdnAxkMeZS6Q9v8ML6iOTBVrR9y7MuvHTnZdLmhN00s5sn9jvh3pvK4+MvU/2EnBtEYC6V05PHTNPwvVmQZuy++Tq/983XP/Kr7fkJb/RrkoT+o82ZVfev8oPf3/Po5H/nd3/jKm/de8z5ZsPUtnhaw1yoNWhth2j0sUXau4Dblw1FEvhBm3TJyJYGqiS0Cy5mLnQqdDiCcdBmctOwGyfMhDYrpQQrIK78EYrh+M7v9vX7dqzMM0+eZLr9hI2GLbRbIzBhoVl3j6102UASqVL0b0QUyaohjgBA0CZjU/Cl1RPFPKS/U6FJidVqj9VqzUzifJjZnJ4xiOHFaERRUVR8ieSLOmFpbjCL9L5YZZgmprkuCsTw5lnjhteFmhENdsA8VGFtk7l5cMhq1fLO8Qnn04h7yFDdjAdPnmCq3Lqyz6PzU6ZiPHfjKiA8f+2I0Z13Hz5mtGAikkYDlVWjuJOSstYGkZasQhJd5MnhCFarjiuHBzQaG203hbCka3JkNbXSNhnM2VuvefGZZ9hNE1+/9y7TMODmFLNQcaYUWZdVlOC8G4v0urgzLal0qwq1Mo9wY2/FKy++TNd1PDnf8u7jR2StPHsjMY1HbOeRbrVHlsTZduQb3/gSm7Pj93XYhdrz9PyEk5PH37O1+vjhOx/z6jH33u1IqYRoDacWw6/f4urtVzmYpuiAFUiSaZrbHB0FdZtTplZnsmhGapPSKaF2VV2yAWNe9BDmHqrEUlF1kifcK0hDn5Wmabh5uM/x+Ybd49PLtk33T8ZkPhNO4Hxzym/85ld59Qszu6liHilxzorfvMN1zZGCK+BC0+To9KuR/uRFLVgWJyEpMQPDWBnmgiHkFIDLMIw8KUbXteyte/aSstd0nJxsQYRnb1zlbByZxxIORhSVpWHJHV9UWNUMR9hNM6e7IerppEiTqdWwiwYkIhqjGfPKWKNpJBXh4dmGvN1w/+ycsRhH/R7rJlHLzG523j15xG7cstltKcU435zTpIZ5WGHAbpzYzJUrq5acErvdHMowgb7rOGx7cmpImvAQNFN9ZjNt2NXC+TByZaXkhaLs2469vo1zK86Nq4dMY+XFZ6/xb/4rf5nN6RP+u//j/2I7PCBpgLgisGoyKw3Jk4jQaEZ8YqwVR1klQbPSkigLFZyXkkRFaFXoRJinSt8bh/uHTFXZVhBPXO/2uXL4M1gtlFKpcwE3+rbh3oN3eOPN1xcnI2QxJoE3hi28/eYfseq+m/aQ4ycf8/LpI07eeoO80HMOiKxY7/0oL72qKNFGPJfKlFrWN57l1pLFJo0S81LsRTjvuVqoZN0xT3QinA4jZZjIwFG/5uhwzWQGj56gGFVAPusUoVnl8aN/zNml41qSo9SQX/kBdinTpsQ656DX2hXNtVvsaYqI03UhL3boU0ufEqUa26lgHsBXk4S5wDjO2DQz15nqRs6ZoytKl5Ur66u8du2As3HirXcecu/hE4pXDGOqFZZeg0jhIjMoHhFxafO5TLlLtUWs5JdOYyqFTZkpbjSSOD7fBsPhsNe0tOmijz9apZ9sJra7gb2uY9W21OpQZ+4/XMRKKnRZsFIXnXtlqjU2mCuVFc6aQqJ6oCRWz9lMJ1Qm1l3H7WtXWbcN22nGPIDDUp29ruEv/OgP8M6DLSI7Vvt7+Lhjr2vom4apFLxEeGuT0KXEXCzwGEkhmqEi7vSaok2XBeE243w3Umxp555nGiJ7CW69RjNPqeymypVVz/UrVxnmibE4qcxM80xKmZde+DzXb7zAZMKVTtjLlQnn8OyEuv97jOYRHIB5hvtv77D6xiV7AReY1Kege6iVujv7UGfHhnH3TzlbFJRy0QeyOmD/he9j2zSR2SVF2GN/7zY3rq9IKTLf4ktH7NLiXASeDAO7aqyahiu1klTpupaDdQ9e2ZXYB59knwknEDbxEWVjmalf/i2+8YEXBfoV/TMvcKPtadxpckLoabvbPHP7OocHgYa4xUW23KFLa6aL0yTDxRjGgccnMEwjt68dcfPaEW1OCJmzfsU7nFHqRPVoyJDqeI7l40SzhqguHY/Q5USz1NQS3iDEN+8bTlExZjfEBTOjS5muTTSieC3sqtPmTN85ZQKty3nkxNVVptbK+fkWkQXgSwFGzg6SFK+VyQwxYxi3FBvDcZKQtAIfGcpMSk7bdhxduUqflHePTzjbDSwJETf2D/i+52+R85bf/YMv8au/9bvsHh/jdWLdJc6nyNRUhFIKJU6XuUSjiwfJjiyyaAjMpFZjKtHwZW5YKagbmYh+tRrbsbLXwV6Tg06toRPwZYjGVJ3tVNDsrHLIftEGzULqEgdu/MyVq+x+7F/ivBjD0io+Tc7hwcBkN0JT4XGsu+0Zx4/uUefxA8vMzahL38l3e61/hBQ5e8STP/gV3lNXC3DA4eHz3H5mj7aJTLQCtVuxf+clnl3vh0bEjckKdc7Mh/vgkDWxv95jN81Mdab4JwOtnyEn8AnmH74FDrsNu2/+IR9M9hra9hb3373Cev2eFh1R2s+9xhcODkkLyl/VMF/R90LRkUmdddtwZf+AP/z6t3jw5JhhGCjzQK1BCV4I5GazpbxQJDutB99bi7HuWxpVxC5yGUfsPXVjNKZEeQGhaUiiqPt7PLqEYEdd6VK0v9YauMNeq8giRGrkYnJStMRqUnLNSDXOS2EoE8jAOIeuQiShqY8eiQXATLnBPMqK3VDYjSNNt+bqumG9d8D5dmYYB6a5cO/tt3hycs7ZZsdshU6FdZcvb0mFZRoQjEs35YJpLYrKOO7JPARUpSyOMjIm0UW3oJHZbCbjsE+scmKaKienpxQR1IVpDI2FV6NWBXOqV6rBVAKzca9MxRmrMdWLKUbCsy/3mPwIpTiKoQLDbsu1Gy+AFZIEU5VUmbYn3H/wFrtxt9wziTKsVnzYfPdbuz+w1h044eTk5KN9F23P6tE9HnYr2iXgVA969+pP/SyNzzzZDpxuBs7HkZMp0a0+eZLgZ98J/IltZprucv/e3Y/8Jp0fc9qv0EXFEuOePsfnX73CeiWsmszvDxsePHrA1x6dYbVSa2GmRidZVUQDkKmLNzCMNmX2U4MCkxp9jsglWNTVFgKmi5T/YiSUelBlGLhcMsERgjVYiVIqLFJnF6NY4fh8RybKixTEA7rw/uA0AiUpuyKMiw4h5lxUcMMowTULJO3IwLjbUaaRYSq0OfPaK6/w3LU1V45ucu3GLbTf49nb1zjYa/n13/ka70z0KugAACAASURBVP7+CaU4B6o0Ci7CWIO7Ng/gVQRWriRCy59ElizoYpgLzFbZjvVyBFldWl+ThoOd3JjMSGKIV6axMonQeKIVp2RhnCpzjXbhaoZXYTsLZSmpPLixhXJOiCxzI3Aww5YMre1X3L7zCn3Oywgvp08JrTtuP/siu6X9OKXMaPD6bsP4xh9g0xCtyss5Pn7swINPf6lPA7s3v8aHEQ8RoYwbVm3LOEcTmwNT/QLPvbr+xI/7/5ET+GSr9+/yUdfwmLNNT5ODUlMNzny8cpMXnrlDmga2pdKlBN6xWt3h4MhjM7lTashJixu1hFzXSl2iX/hzM8c0In/1SH+FUOslol1V5P216aJ/R6lWQ2rsRlIFDW0EZrRZEI0yoYpSMaQGXbW3yhRRdmaMc4kN4nbRk7LUv8s8u2UTqFyg0Ykrq8y62+fzzz/Ds7du88wzldw3CPD1Nx4gSUkEmKcKoBSMcSlDtlNMWlq3y5xAEZqUMBXK0vCSBFrlkkm50IlM1aB6SMQlxDJtUnKGwWpIs8XYeQC1BYeFDYnGcgcSpQQYliyotYsZh0mFbtHo78YQNC1+lyxGkoovoo7kxmq1B92a2eGgSXRNZqxwtcyU6zc4G3aMpUYvRhK+/IdQ5998j+cHSMKDd9/+hClS311zd77xxlc/5jfH7LbtJ/7dt+0EROQF4O8Bt4hr/Ivu/ndE5D8F/gPec4l/a5kt8Bmzxzz+OGapu8fp3a+TrAYFJgrSsV5/P7efc+YadKW50fZ77D37MkdEA4oSLEUsOotFyiIW8sJsFbOgAZvUBKtBRCpdNkVi2aQWEcaJVNZKtLHmFDqICpedlWYFMcelIefEGmhEQmQ0zwi6DNRczlFCBKSaSCkxj4ZboVbjjbfe4v6jJxyfP+H6tbcQVZpGGKbK733tq2ymEffCaDEDIsSazlgro0esT5qZFxrPCEeVUCaPOT9ZhHaJ+nXhyB1hXjQh7k7KixhMIgsbag3cITlTcWzRithyvRAupzTF8KlghFISmuU6IjEGLKnSZictYqhMZFY5CSZK9Wj02c6F7ZLCFTeSGyLK7S4z37xN3uyYSgxFcXV+4AcF95+hU2g9ugk9J+5+66uMuw3qIfW9cPwPHr3D4yePPs1FvthDTo8/+bff9nMHRORZ4Fl3/00ROQB+A/i3gH8bOHf3/+xP+lmr1Z6//OqfA0BT5q03v8rJk4ff1nF9Oiao7tH177EX7iBdT//MC1xXDYGHRmeeHN3k+p2XuJ3a2CQCuzryZLejVGO/XbHObaDHXtCUWbcN6hbTZO2yoTQ610phKoWr/Yr9JiTR1bgck1VqqNI0NZAzokrbdRQzNtsd4zyFO9KYYjNb4cr+Pq/eeY6r/YqHjx/y7pMnDNNMzjmGbaqwXvf0mvEyM1dntxtwjHkurERZNaFHH6uxXdJr8ZB6QzAkU3WqCM2irxCc5DEXEImZhbZkJikJqkGppiz0TWAMx9vCk+2MsWRFAEv7L5IQPIRMokwGY50RYlp1FmhEScoiMBNEE2Ix9/BCXOUWKs5KZBm+ZGKFONaDHOrRQqJvE9WM4+3I7Ea3pHT9Mtqs00RjxuwCWSnzRLLQUUzUaCYS4d0H93hy8uiSNSnA41K4+/BtePcDIzm/W/Yb7v7FD7/4bWcC7n4PYjalu5+JyB8Qo8b/ha3v17z2Az+xLOTMzWu3eHTyMLhOQmRxTuXeg7e/h9zv+80xO2f34eb3ccu0PWOzJH/BAjisD9i/+zrfSvlSulz9gO7KS9x6do+myXSNUiaLtmKJ7j6fg3sfq106AU2JPivi0WGGVZqcSQpWK+YZ0URimbm3TMrZTXMwIjmh0tHkhptHB8xWuPvgEQo8ePyEhxyz3W2ZagGEea64BOUpOJobxt2AV4vhnhLCI3GoVnHN1JRoVei7dQhbpinksG4oRqtKmwK/YKHEYhr0MtpLQoTV5ygdqjnFhCTO+Vw4HaOsEQSvAYKKRq+FAiqZnAlGpJRIkQQagQ6JSE/Mex6KLRlBDBKRpcVbNZpzaq3UajFoJPxUMBTueI2yRNzJi2rUlg3cp0SXZJl8HFmOeJQ0XduTVJlrIRNslmvm2W7Nc3deihZ1dwqwMePF01eZjx9SzAjZh3PvLefBu3+I+32+2/ZdwQRE5GXgx4FfBX4W+Jsi8u8Bvw78x+7+RyQjASqt+tVlH/XNm8+yunLEZDB7wdW5pnB061n85S+QUTpt6Mm8e9/42utfx/3Liy4/PtM+0nf6KZg7Pk8fVbnPE8enx5xc9NoC0NJ0b/DgrYY2azQVmWNdz9Uf/hl+PCvJfNETTEweC7y1ykGb6doGcacS0asRx1xjCIbFZt3VyjTPNDlwiVKNMpfoVKuVaezJOSjM3Thxut1RbUmJm8y66WjdSV5xK1iJ8iVBOAANZD9GbkHEymU469Li3SSlpoRj7OdEo0q1yG5gKXk0IeqIx1ivheZYQFSYapQf27GyGeuipAwFZ9K8bDvhsEt4rbjAulGmIoiWmI3i0cPRt4kkQqPCPBWqG2npLqkex6AOqDDjMd9QYJXC0fpcmbEY453SMtPCqR6TprokrJpEVqW60LiTcNB0yexcDEJokiI1yj9T0NyQCcGPSFyj7E577Rmmw+uLU4pze+m5xOPT20zlfGlKU7o28bWvNrz99q9Q6z2iOFqW5r/A+v+OnYCI7AP/E/AfufupiPxXwN8mAuDfBv5z4N//mL+7fO7Aen0Q62BpvGlTHw9hEDivM6NNiDjX9q/QHFwlu6CeWGnL9evKiy/dpOGHWKWYVlpQvvL6l3jz7uuU+n5CVihlihvzaZoHRfXBbynU7Y5x96H3auLR8UMeLJ2OfiGRlr/Ej36xZxSFWVhlJWvC0ppkjtcS6awrkhzUmKbCVEJ+2jc9xTNDnSi1IqrLrHuLKbe1gEO3ROG0REN1WImjNIzkZT7DglOokpOiJcZniWbG6hiVNjXMJpQ6UzFWnbPXZEoxnoyhBL2o07Mark7WRKMhj1WNrsVhqozFGUs0QlVzVhoDZkGYJGY0qC9SaYwOOMzGicczKQIijB68ZhnScSFMigwkjjlZQXPCJDHX5UEn4iiKqiJiuDjFnXEZduOEBmJXjLlGluOwROxlnp9FJpByg/q0rLwo7QpBZZbZ6XMKei8JDYlaJqZawnm7oCk6XZsk9K3j3TWG6ZBWAVWqwOdeS7z6+X+ZPgf4O5QZJHPv7td54xu/d/lQHYDd8OFUNuw7ehahiDTA3wf+obv/Fx/z+5eBv+/uP/RHfc7167f853/+F8CEtsn0CcZx4satm/Trli/d/RZPtiMHTaZFQmvtISZpJYZqtG70KVFV2Zmz3Q1M87igw5HXqWZef/1LfPONr2Bml+h4AbbzCLvNt30tvvt2yHpfFwXe8rw4VfS1L/JTN++wKlNMpNEQAe2vDpE0sVt6E1SV2Vj6+52uzfRtYq6VucSm1suUN+r2yDLgyhKlizaUMseYd4S2jflMNhXOi6BNwoiZ/kf7e+zGyjTukGwc7YXTerKZ2AyVuYa6UjWi8gVGoQ4sU6Pdo4W5aRKlwmY34WbsNQ2dwmTOQDSFiQsHfUcrwloKnTrHk/BwnJksxsplFa42De26D5HVMC9j4WJDYgsVKBpimgsWgZhGnAS208xUjS5HgKnmHHbR9/9oMzETrMxB09KnmJacxOhTi2mieEEtxoNN1ZjNqQKuGsNJRTGERqMBq9ZwX5MHbaHLo6JqLexqpZZKJxJA6kIuN6pc7RpMlZNpYp4r2Z39ruHKuqNY3L+/+/f+zncXE5AogP9r4A/e7wBE5NkFLwD4a8Dv/bGfRaSI4JR5ZldgNxXq8SmrXQNT4dbeIVfanmG3YSJStnZ5qIMoeCnMAqVG67E0mb5ryCkHN6yRcr38yp/j5p3Ps+o61k0DtXKqid99/ID6pV+9BNkI0SGnjwrwpwFSnrI9/5iXf+uf8k9zXjaPIyRSvs1Ld36Ml1+ciAf7KMWcyh79+jpHV4VVlzEz6hzTlxIx3n0yW57TsOihVChJsGqYhKO8GIIZohtjshD96GS0beAV4zhSaygxVd4bXoJxOd9hWsZqC9EzQAVbKFGcAOscMCG7sdckdlWYvV4OiN3PSkUppqzallQixR9LHFuGmEexTKL2vQOef/ELnJ8+ZLj/Dl5LONQazw8YJUa6XTzOKyheQ9wWSFBiiIdBn6BITLtG4sEnpUZQqtnI0qI5RouPtTLXQp+jBJoXGjN6K9LlmHmBoHxF6XITfQRzoRCNUtUc8xrPZkhCyi2lhgai03bpmg1wJWli1XXMPpBS4urhVZIuKLJ8crD/TsqBnwX+XeCfi8hvL6/9LeAXROTHCKf6BvAf/vEfdaGkkUvKZ3ZnOD/nfCu0rnRdLMx56dZDhEFCYx+DJRNNSlAMq/WSQx6nQGO7pllkp5lVn9lrmyUNNZ5rlGeee4nh+i1Odju2dcZSaOi/8c+3iP/2cpRxjLUWHt9/k+/pYM4LG3fsPvJEs3N+f/MmX33jg88OFLnJ9Rs/wEsvwqoNIdNmnJkPrvPM0U2ONDKDSjzJya0yFePUoo7OOJICjS8W49JsruBCTk4j8QwI3NgNAy562X6dNMUjy4hZBo06npWyfNbFk4AS0Oeo98dFUTgOM2uBgzbhIpwPAyLQq9ArTC50ObFOwlxi+IymKGtEhc6d1CTmCk1qaFIma6JtZGnhTTEajmgHRjwQe0Kfr4QzmN0Ya+AgXQovKUB1Qd3ZS4sYSxRlwUoWTqcswKq7MJgzujGZkKh0ujw7M8dxqCTavqdToWyHGFm37Fv3AGlNDSUGv8xmNCK0WYJuzonU9jGLc9pBFiYzjndbqhnF6tLg9fH2nbAD/w/v6Vzeb9+GJmBJ2VXQFF14615xJRpKamUYRiadkaT0wGQwlML5XBhrJacMXYwfTznRaINZ5WQaKLWQPPCZZklHUxKyZsynRVEXTy/qpMFyeO6cnO/74T2S/MWYFOyxAGyeeXD1q9R5jMGUEn0DZR65+9Y32H5sCP80rVLqlvIRqOMu995+h3fvx41aBItw7RY3r9/iKKWI3jgpvczLL73K7RtgNoOnWFw4wzQu7arE470kRoGnBZ0394hcNZSSKWfcomvzAhRUgU4SRmVb5pjSA/Sa8Ap1GRtfidp9EvCxMi/NWZu5MiWB3DDPxkETU3S2c8CTvUCfcjyzYp4RopavmzPe/OaXGWs0HZk5JGPGKB4bRIlz8eJLG3bCgMnt8vtlYTFqddKCZyALi5MSvQYTEA8FiczJgdkgnowV7iGJMlllGI2+SazbNmYspBRt60lxTVgty3h9R3JkD7U6u1JjCGmbmYkHq/S54erePsVndmXkILds5sKj3Q6r8UgzaT55q38mFIMC5BSjQ4tHrbhSWZpi4maIRZdZToGOe415g5t5YlsLlIlN1ZjoI8qetHQ5mlyi9bTiptGJZSA1EGIkHn6BGEKl1RDzVPOQrkqkg5MYYgT6mxs+/7kfpkuZtkmL01G8zrxw5xW200BKDVmcmcpXTk948I0/hNOP6zf9NM0wmz46oPPBXd55+DYfJJve4OE7f8i1q/GUnKAIBa7d4toP/ASfs8qsSpeXrkwtMfWZWORJhCzOlqC76pKSyIL4m8e0p8Zj4EgRYbU8rsyB2UNWXGsAZ+cLIr/fJY5yw65UtlPleJiRpfdeTTHeW8TiFbUY+06tmBu7aWQsBcMv5+1liadQFTwab/B4sOnCGojIkuUt56AR+RtgWp5P4ctzL9qklwNXrZYFfxK6nNkVYyyFNiVajXIGF3ZlYijhGNtkzNUZzAIoTLp4i3CexS+mZUeQTA3UyWJObRI8KU3X0e+tODuL52C0mhi1sqsVagy6/TAe/f9S9ya/smXZed9v7eaciLjN61+2rKwqVkexOpIiCxIpQxQligYMGJp4ZnhgwP+CNfZIY8/MkW0YNizBEGRDtgQ1pEWBpEiKRZFVZJHVZ5+vv01EnHP23mt5sHbcrMqmZLgyU89nUC/r3vduE3HO3nut9X2/7wevp2IRUDOmpXjtY4WUBkJI1KWizUh4YyXijbAmysW0sFtmFlUs+DmtqoIJizkXbxUCWftRz4ypLSxqSBRaLRBmYogcp0iInmHgWvdIqYVmkdPVwFIasvgsaQj+wOcUe36eh3UKhkrmuRde8u60xCvS0a1S2L/4KWzaM5XGUj0j8WvW2H31tz76hmSfXvxwlfiA+w8e8eAh8IOfefmbDK98k++2Rus7vxBZj3+Hv/UfBZoWFvWFNwdhaQWzipBJQX0Gj3lAjBjZhOPkjbUxBsbe/NqrsEyLV4b4zH5MwmrIjEMkVUHVmGqlADoVUnSWgQWYip/U5iaeTajeG5oLDEn9fRJXKx737D/B/35tRmt0k5CyND0Iu0kx+s+DN6MhUKoRkvc+msHSmqPNA2ySl1ISIRpYM+Y+LlV1MxcSGYMSFGqtkCL7aWEv/r3murztJel29AOBOMZAHFPXPgSGnGnBuPfkjO3unHlePG6tFudqRmHM3kd4v+upWATA/fZzcdlpCMq8LASL0F8sgNoWaqssqlwuExPtij2auoWX6nHMTVz9tUne3pmWxq5UVDx7YDEjaCOa21EjgZT9hLFU46InIV1LmVVMnlhrvqDQ3W6GsTcjFJ91h9B7BmZYqKABUeG6RG7ffh4LsN3PlNbIKfJiCvDsC8zTxKyFRQRrla/+buby/B/Bj1y/P4xL322MKwvzm6/wTomKyP/IP/g/XK9+lSA0DKSf+xX+2o0bYIfs3eqSW3nbJLVKzvY/+BpibySuh8hqTMyLsluUk8E5D0l8apRPAhf7wra4i3JS2BZPqn7YGscpMjX3TFTzhUQseGMs9NDRkD1ZKgjHApc1su2TogDd2ejI+sMJNQh9J7arv9eSn17MvCdA75k44CZ252TXPahS1OXMWRs5gHQbtuIJVq0aldLt70buPMgqbhNvDSwIMfnmEouPSQVjO02c755Qa2c3pMwa92BE88VC81NOG0agtMq+LV4TLovLLeuChsQQesfbHBU2qVI4vMq+c2iDhDBKRLKPydY5YSi1KBaFQSImftPQ3OE2psgqdQqb+nFwr5ViymAB1EEbIsYQE5v16NhxhM04UtvMo4s9i7kufUzRa+QKMsAoQPOILE9MdjJOErg+Zoabd1hKAfE5ddHG3V+F9eq/YiozZ5cTJ0cbVkPid3/nN/j+9/7MybM/YDs11Q8HivEjLrPHPDl/j0/8xv/GP+7+CZ+brzjafJEv/KXP8/zzjdYaSaBaQAnEEJkUYuwJv0EIqsxFqNUhKYcjdk6ZHIwxNo6TsFIX/OxLpdTGHm8Qm3qCUMzRI+z6z+OTMkXEVZmlGusQseS5kKaOAw8IFsBoFHOEVyT4expAch9x9jLA8W0HrqPfL/Ps/ZOGP2RTKSxVuTZkNjGy7SOZZrD0CccoPulyQbg3v7MFgjZmdb9IK77xJSImUJt6zW+R2goxR47XK6YpMHYZ+r56bsT7XU/HIgBvu+lEaES2BabSyMlYxENAI5FIIwUozUdK5r5gwPMAXDvgs9UUhSgDW1uYl8rxamSVnQqMKGdTYR28rsQ8UqyaXAlKVutAHBK7ZeGyzlxfJ47XR0zzHlXjaLNisMx+atSyeCfZGos25toYW+LamP1N3M9AD8YESi0sS/QueRBO1mtCgMu5wdjYDCtsEIZ75xQqVY1P//RX+Okv/BVOR2/8PJr2lGrcf/3bvPLtf9dtxX5nNmBfF5g/Ijjn4aoLP8zLmFmW3+Jf/fa/5qCq9mtkc/RlPveZz/H888a4Gihm1KLMYSCEiIbMxRKYqjEmaHNhO1ev69PIZkxs5wKqWE4Ug9THbus8EEWY1I1XJi6l3mpD5q41ILijMfr672EfIMFYpGc72MHe7J38infppa+5pp0eFSOIMRXDR7duE0/dBLVthYlGapEQncO4tOonCPc6M4z9FStGwa3hsXsdRlFSL0WwyrhKpCG781LFEfTJ52uX+4lS/XXa5BVq/MiA2qdiEVD1BsqYk3PiQnCiiwiL+lF/Ja4aa2ZeR6mAOQXYzXuBoY+CtBlDdO2ASWUdIuREyIEhJJYyU8zY5MyQIijOFWyNfalEAYI/oLMY52WhNE8b2u+21FoJAtvtBVv61AF3n4UgZAI1CGMeuXZ8RIqBuczM0w5daodrNIoa6+greh4yOQoPLs84GhJH6zVxiDzOl2AJrT39qDviqim18+NuvvCTPP/xz3FzveJ6t8q+Ycrvv/Z92tf+zZV1GGC3g/3uDN6VAvhhX/aOUmNiu/1d/u1Xf5d/+9Uf/pvhp77Cl24+w/UUKOKjuSHc4vhkhUj2wNAQUPEFW2LkKLhT0vsACqqEGBhT4tnbtwixce/+Q5ZqnJfmuHZtna/ofcB2SIXqJY5HTPqIcLLGOrnW5DAFSPh7X3G+BAozoX/e5cUhiA8dQySn7GyFgHMYY2DaO7Q1RGHf4a2lJ2yNOfaIdS9DVj0TwkiE5NHpau43iQg3VhlEeLjbucN0SARp7q7Up/wkEEJgSJkQjDxkxhhZSiDH2im5rlSrVbvDy5Vmat5FEgvkIXO8WjFKYFcnSnUFIQRCgKMQnTIz79jkyGq94eTGdWxZePWtR+yKs//AiOLW0CdTgVqo2khAq439fodJIgXYbmdmGmqefLwKDoDPYoRgHK9GNpsNm3GkaePJY+NsvmDBY6Rur0aev3uL890F55c7zIztPCMi3E2Z41Umh4jNhahKHiLSew5Lc3ZB6Dt/EmN0VxHFjNsS+Duf/Cz1k5/jYufZdZKEt94IvPr9b1DLd8gRcs7klLh3/3UePrz/ESYFvf+lf/Zv+Oq7Pvo5Pv2pF7h2zRhS4E2FmIRydINbd17kVhcxaSkM0sDqlUbhfHfux/Xm6ki1RrZAE5cDx94wJHhWxBACJoEiDva02kEwitOm/ciKWiOINwIP+DMVpSxdnhzFFyMLrJOnEWl/0L1CD1hIlLYQoUfnFbQ1Nin2ma47KYt5GTua+KJTW+cuHPoPjSF6+RNxCXiKkZ0aZemNxve5nopFIIbA9eM1l/uJGIXTzUCwgYvtRK3ezCvNXVYq1tVPh4aAr8rBPF03R/GdQp3I2kSxylU3eIhud93tJuJqS9LM3HyENGQhizv/ZjHq0qB6vdowdq152nF0xNi2NrZ1JoXI6TCSQ7xiCASMZVl4cn7BPM4ghwhrY9saczMw5XJpzHOlLguXs4+zTtdHWGvs98qda8dcnDf2u9pvYB+dqh107t4Uid63oglgSo6JYRxZY2x3ex5dblHg9u2Rl178LCl9kaP1mpOjY47Xa773vT/nj77+VR5fPHExT/Dj8e7sMffffIUfR17+wVzf4Jvf+sa7P3ztNs+89BmerY0qXurVFjjafJGf+lSixcDl4nTiEANj9Bq+VncdqvhJ0kR6c3ggZzcpZTMmqxTxhrXiQqPDwhtiYpUHalXmOqMWWItH3u1q47IuXqLgoioTWNQj9/Ztebtjf8Db0/MFEQqwV2MUZ1SINuZW2VVhakYU48aqR7YjrMYVc1X2ZfIH3mCpXsqoBX5ES+DpWATACTLr1UjsHdhNDLQxc6ZeX7kf3RtvOXlsV9Hm0c89x2+7LLTYSN1r3lDM3OlWxfn8pbgUs5ny4K1HSBr9xYouJIq9RRybkMUf9mj4xy1wWRsZH4ltrVDMtQ2xVk+CkT5EC/6g7uYFVW+GWXUNt8ukhbk2Hp2fs7/ccbL2wJKoxlxm3jp7xGa15mi1IsZw1YGvdjDBHGbvPjN2Hr2SY/bwk+BTkM244marXM4Tc/URamsQg1FKYZp2oIXnnvsYeX3CG2ePmbozT8yYLy/ZPXidjF2ZZC5a42vThH3j99+1v1iXM39k19kD3vrjB++YXggpPmJ75lbf1puoYXPEzZ/5Cp/PI+eXE1RlFf3Ir+pe0FKVue4dWpqF2aoLhMTFZBlY2QEFLqSmnJXiR/PezBtioKgR1HX940EqLEILvhlcqpeUG4msenJUDoKQuFy8iSgJhhiJBimPmAiPdxNVKylHiuoVWk2KsC0Vs8CYBtSUsYfIlphY9CkHjQreB9hkQfFGTxI4TQEduiXTYN9gnnwEOKTMkBMavQMqSRhyBPUdXDp7z+P6fGWVENEcqPPsjRzMO/PqKHEXqxgSjT52JiOgwmZzxColdtsdS1VyjjxzfMRSGg/3e+ZWGSUQkwAHwm7AtDHPjVkaqHP6NzGg0WEXKQRW48B6vaLiQMulFl55fIkBx3lDNGfwDTHSxCildiSXJ9Qc5uqzKatoFISpVupu74+jBI42KzYWWGWPbnMHX3THHEJrjWVZ0J6NZwbWKqthzd2f/BJHyX3yIUbOlsqw28P12yQcFTZGD1X9nT+YmeZ/ydsRMD2l+aOwdl9dRm1/zne++44Pp8Qbj97irRCp1cs4ETi9dosXP/5TrI+u+wYgzitQnChEcBXgUQgcdybibEKpRqW+bU3WhjYfTxdzKtXpMLAOgjb3GkRgtyg07WRrF1qtpW9CqwGT6LmPXeswhMA6CCll6qiYRrL4z7YvDlDZLjOlQUqpJ2Ibcy2dzdBo9pT3BNSU0pzv7+grIaTo5/CpMS2FhjGpj4NaVQTn3UtX/Tnjz3cgxUm0Obowo3X6nEtCfSwoEtg3Y6kTKTgwY28uOIpdLXg4ookFd2YhrHJgUZ/xWvObW8z7CKH7wmP0N1eb+yCaKqUVh2x0TUMSF6Jc36xJJ0doK0yzaxBEjHPgUgvb3RnJIpnI0XpgSNGZhiEymuO3MWcHPNlPLHUBhKbm0erD4AvsMLKdC5fT7DZXCaSSGZY9Q+9WV22IBEqpCMJKEjkI2mYuLZIRxsElwy+myP65jxFQjkPk2pgQAqenSmu3QStp9C79/SeX/NHXfp9HD9961xlBdSCAPQAAIABJREFUtX10pUatlDdf5Y13fPjRwzd547XvEGO6aqDCNa7d/Bk+/pmbHB3h8z8zEuYIMhGC2hW5aFafx+fccwTEgakx+OJ9UQuLGWKRpXpDcoW4RsCEFr0xWfFG5SpnQmcT+L3k43FRAxUWnLGo5hqHuRpzg6DFGQ10tooY6+S9pfe7nopFwMCR0ebmn20z2hIYh0xplfPJ03Dn3jmtqkgtREnO3MMVYlq0s/N8rGIhUvDRTa2NWpQhSH/hAnNtVC0e+iFeI3paTDwoNz3LL7hqMYvXBQnzE4c2tB2al06pieYCIbsaE/lcTHCLaGkwJBjTwMnREXdu3mTI8OjhQ0QbciVgyixUaoGlFJZakAr+kxircSBUwQrQ7IoqvJRGPHSRzdjPfmrYLgvn+4l9WTCUKIF1SqxTIh3wWOPAzdWa0JR9ccXlmIJHXBuINldaxsyq042GkFhHwWqlqnB6Ipje6HW2cbLOyHjKLx7/KjEI++rlzBgT8/aCb/zFH/H6vVeulIICFBQt5SMzaGmr7HcX7/joIy4vXuPN11wY1k/6hDxy+kt/m19eHzMGLzMXg6Ulh6vi/aNVTEjwh1hbzwuYCxYas5YeJupMyYayKCyOeKACOSeSBEo/sSyLOmnIFMxnDzEGVtmRZsTI6Thwa7NiWRr7aY9EyOLTskXff6F9KhYBkcDx+ogn20ue7CcQz2MbgodtahCCOEWmmdtVVZwyG7TrqPEeQDMjWmDoU4GYhMZArYVSd648S6nnvPus2IIQQkKoBDskGzsFxnHYLuKZq1BbYxVC1xuAkNjagqpSzU8L6NvJydabl2KO6K7qtbXhYyxXgLkfIcXIZN5XOEbIrNlT2YfATMHMHXO7xRVmXvGKC41MiCrEru2P4my7++dP0BDdGu14Uj8G4xyAIeBNs5CQ5kS9IUUuizvhDBitUUvrN2wiJ+H06Mg5fMGo89xPUZXYIqkff91sVDwnYVhzNGZSUebaWIlwcmvFX/vFXwXTHvPmIp8/KDP3/vC34OzR1cKAwOUlND3jKgSiXx/WScLsPUJC5j37f/oP+PtXy/zh+hX+xi8/w2ZjTjkWz1tgXLPJI602Lmt1klPw2HqLAhaxCvum/STmzd0y174wuo7k0IUIkjoAxk/LQ0qss2sGPv7cs9w5XvGt197k9WlPa9b1Ar6xvd/1VCwCtSy89vr3mauyxIHFGrs+4KkYRzEz9olJCpAkuYqsj7NSiv44pI66wrvlpRoxwXo1umqwRYguMmrVH8IDdSbawkrEZ8XBiOr19hCTC5hMoRZGOfDkvMbaVQ/CyAIlGCkYwcRr9No6ZcbIwfsLOUp3LDqhd5OE0oz7F3se7iZo6vNhM46IDOuBTV2YYmY1pE4jbpwvWzcGBfFaHRdOqaQuggmoNqZWKDZfzSxEXM9ezShRseBcvxAC1pSZcjVelShebmhjDH6eyR2qCj5had3KvZ8r1iqbLCQCrSrNKnNMVBW3/4ovRhH38PsS5kfmFDwlKErgr8aI/NKvESXSDGJnRvzeHwiPnvxTtJ3Tf3VAePLkAdO8f1epYZ1c/IFfXcf/ww3Qf8a/+Jfv8Xd/8vN8+eOf42atnNWF1owcVsST51hdU6bSaNpVVP00OYiHrIj5ghFScAirdO6G+fs3VWMq/pAPZlzstjzZXvDq2Tm1tqvHvhhkef9T1Y9FFvqgLhEnHpycXOfTP/VlpqV1np5ByByNn+S55wJLVQLGOvlxN8bAehwZV6dIHKmtstvv0FYYsj9sY4xIypS6oKWizW3DAPtW2S0zJsoYfKYcCexR5gazesjGyejM9hy8VgMf2TSMh3PlyVI8Mz4NrHPyKGhxueZunpFA18cL6zGT00g68A9EuNztKfNMadUZegLJIpsxICE4GUaNISeaNh7vZh7sZpcKi592Qn/A1zFylBJZHLQ1HX7P6sz/VYiI+u95tMqM4k1RjeHtmHcCtRSSCClFZnWGw6obWubmno5WlTxmYspc7PZYnViH1HsukaqLJxOrUTQzDgOqjUzzBqj5lGQQo4lj4aTP2wOwSokUo9fgrbEaIsOw9hgzgVWn8fzJ1/+AN++/ggK1+kRHgXl7xpMnD37I8O7iH3XRwH+gS+Qa12/8PC99orgZKfjiGo+POXrmBe6QCKYEVRAfW6bB04tb6/kTwbpa0BvZSRJ3r605WwqXW38fDmlKCmQx/vv/5b/9YMlCH8Z1cfGEP/y933zHRwfycMb5Jw8pNTCmQDg55eaLH+eTcYOEDEREFw8BFV9RxxS5dXpKQzheXWNpyvdffYtSKkMMrFKidHyUBV8bmmo/ykMwf3CPsrBUV90NMblq0dy7sMowmvPmVH2RCiG6mUSUVfKmpOKMvjGPHI8rQoxMZeL8csJqZR3c77DtIiCGw0PR0KoEx94CPg4MQWniE+sk7kcfxUNEHKNtzgPs9NvW3XfrGDkaoseWpQSt24aDnxRaLQ4jDV7fZhEmAhfLni2RRKM2Y50jY4xMxfX2RdV3sOA9BJFILQ7pMHOdhrYDeNP5/bVrPOgNOVfG9RvXXKhDiH4cjpGpBR/XRSFHn0jsL3d87KXP8NlPf54KTPPCpL771YsHvPHat6A3ag1jp42XL87hzZev7jARaBVKrcD7y2s/qMvsjMeP/jmP35l7cXyN05c+zYu4EtbXLuH6zbvceeZFUsxdZgyESM6ZKt5QDzG4ZsEiNSwIdjjoEu2dBdQPXx8EaPR7wAUuV69m9pdF5CbwvwIfx+lC/9m/jzj8/tdCWb7KN9+pE1kfcfrmK7yWV2DRjRd2zOnpJ7jz7BHzWBniwJACEjPXrp9yIwa+z32fgxukwX3fbXF4RAj9hfeFFsH6LtwYejx604rhD4nPhOFoGLhcfNQY+rizqpN3ckw00x7ikUghsJSF0PpgSQSNkYl6lW5czTmAqspo/vC3XmPO0pisIsm7v7k3hE5zJuHecZNwNbc2gzFGTobMrvroNEQ/UrZWHaDVYRlWG6GPBrXnCMxLxXKmKexbYRMDQwqsopdF20XZ7y8J+GRDEabm/YFSlVUOPVrNUKv+ukvyMNNm5BBZDQlJgVhccKvaNfmtw0Zb8Z6FNkLySYZ1lyfi06VpWbxsa9VdhMDNa7d4/vYzkCNVvWe0bZXj8wvkzotXuPMoxuNHxutvvonq21lVIkIpM8syfzQTjMszzr/+B/zpOz58fHKDGzfvklLuNnAQucWzdz/DnWcTIShmkUfnHtJCcjKSqo8dA6H3g977+qBOAr9sZj8I4vu7wL8ws78nIn+3////+gP6Xn7tt5x/95v8sJEtM47f4uR7IzH1QIsgyHrDCz/3i/zs8RHbi8fsS2G2QIrX2GwiRH9ok9CJu0ZS6zZPYS7GeuyZeYdMgK4rzyFxRIAEpRRvIlZvVjrFN5LNIZVRAlYbRRshJmKO7NvC3NTHo80Q0y4HNjAn6+xbpWKkWNmLNykPqUIiviOuE9QqzApEDw85IKoEp/rmQ8NOKmOMVwKYanTHXlckdZuqmrKtzbHqvf+ih5OOQo1eHqXgWQClVq/9vbzt1l3rIE33vk+Te0JidFVlHjKbzcr7GSw0NcZ1pjRlt11chtsaUhcEGDXTWu93qDFX1y4EMTJC67FvdHlt0YbOhW1tfvIw+Pi4IX3q864H6L/V7bvK3ec+htmnSeIAT4mJew9e5/XXv0cphcDbOQmlLmy3Fx/J4nB58ZjLi3fuoSNvvvEdTr+bnEgk3mDX1YqbX/h5PtFc67JOgUDC5Ph9v/6HVQ78p8Bf7//9PwC/yQe9CLznVZjnN5nfxeCDN+6/wbdiQjtOyxCC/BW+/Pk1NXhzL0Z11vzmlJM0MpqiFnhcKotETofY+a4+xqkYQ3B14jpl15wfJL046kxx7p0cdsPm7i4XjRQuFs8tCArJvASJAa5lH1OeN2PbjEWMTM/KQ/pCJIjBUhq7fnws6vNsi5GgcjU9CUE4SslDOtUw8TFh6doI6V3oublCbpPS1YNcmpK7ln7RhlXPHLB0yCRIhBBpye3cOXrEeAqBIar/GxyxJUH6HNuNNnOtbHt46txltCsJjvBKPXK9dZmuxB7U4c3fRZVZfZriO56QszCqA2vnUhHz5nJrdvUAY4bV4r0G9RFdHIxbd9ekeOyvb5/SpJPrbG6/QDQX7GxSJObA47MHfOdbf0qpHUyLn0oeaINH9z7Mm7xfM/v9q+zfAzlx79F9XsMFQ+Fqofvr7/uVfuzGoIh8F3iMt0r/OzP7dRF5YmbX++cFeHz4/z/w765yB4Cf+7F+iP/P14pxDFf4K/AVlU/8NJ+5cYuNuiS4mnvdn711m1vXbhC6DHXGEIk9SaeP5dRZe7GDL1U9qLOZOmAS7SMv4fFUuKgzJoLURrJITIkxRk5XGVXl8TxzUVwnkQGxrsDrIpFNTp6yI8FRZzgdaepBPB4c2tN7uhKw4f73dXKH2646gTfG6Aw7jNW4JoqxnRfmWgkSKDRX0JmRTFivR1biAFdXWrqAS5tbqYckHCVXyF0WvyFXeeB0PXqNv1T3wMfAkL35aj3/sWrzQNcO+jQ8EORolSkI2grNfEfGGt2rQ8yJpSnbafZgEaGTn3wWId2VaOY0oKqHfoj0RCU/RVkzaNabosImZa6lwMmQkCjM2tD9jqo+3juMMX+/Vvh3//qqM++pxXDvPpTyKofq3PwW+Ag9GRtg96E1Bn/JzF4TkbvAPxORH6rezcwO3f93fPzXgV8Hnw64WuvwWaHWg2ziw7ym9zw18I0/5OvBd5vDDx4k8NzzH+PjP/EpqB784TXwhnH1LLduyJX0WM2QPvc3AHWkdO5gE0SYyoK2ymlwffdkRgyZcUjEkDFgGAcyBnX2h1wC0qk3B3dkDjD0m7lWJ95KDAzJTxVDFIIpU1V2S0WCwznok4AQYZWTL2zqjMXjoyNSymynicW8m74JvuDEFJlrY6quYLtxtKE14XLZEkPwzn2rLEsjhUiz6J4DVRpGS0ZMifU4MOZEaQMi3tvg0NNQmJrRrF1hycT8mL+dZ2bt8e9mLijSytTc1z9qpVUvU06PN0zzwlIKu6VQ1DUgwQJm/u+161GyOBJusXYltJpbc/mvBcRaf8/VR3oSGNYnZPWfeSWCmfJLKRF/6T/2sBRVpqakJPzJ14z97rcxFtTEswdMefTwHtuDUMneBpUcXIkf3PX+1vEfexEws9f6n/dE5B8CvwC8dcgf6MGlP/J8NIxrPvPZL3CUs4soEL79ncb55dfB3IF3AFKU4oacD/Uy5Z3fQmm88vK3eeXlb//AR4UQbnDj+ud48UWvyboMiHTjDrduP8d13jbV1BYZhoFhdLHSGBJHyQU7lzGS0sjxZsWsyrLzHdIAUWEVg4eDioAprQt/ahMkuwklmH+zUirEQBYhGQx++mYW3h7JYVR8Jj0GPwo5W896MzVQlkJt5aqcWEUXpuzFJyQxO8pK1Zua+72j30yrS6NDYGmeIix4qbRUZTtPBNrV6Qnc2TcMA8tSKEthCMKCXqG7Y3A5+G5ZqFp7c8ynRb7ju2hKugMyxcTNG6c8PDtnbjMqXr419XzEQ4K0RO/djDkxGKhmjtYjizbub7fUpSANbzwHD2jRLssWs84NdINRQbixSohmWqkUUXJwuOmXvwBif80x5uo/CwavvvxtHjy57yIielQZytmTB5ydPfohBoNbiJerKdEHdf1Yi4CIHAGhB5IeAb8K/DfA/w78F8Df63/+ox/1dVabI372L/8StzbHpOTNqus3Z16/t8F0T0qRnBLWGm+8+SrnZw9d3GJe/6oq8zz50fAjvQzVRzx89Ns8fOe458Ydbt99kdsiV8eJKDd45vbzPHd3pGilzAv7FKnDQFwfc5ITN45P2NfCw+mCWmaWubhLsnljb5UjOwksza3TtRotuUUatX7EbdD8hipd9RjF+f6x69lNBQuR1pWYwbRDVoVpWmihoWasg1tXVbrBqk85VtoowOV+cs98EJbFqUpijVX2CPDd4iNX6bFyOfjOOU2lg1UUMbhzesLJ0ZqL3Y7dtGdAiKpIZ0d4sCqMIRLFAR7SF4YkgdWQWapzfyJKbY1SioegqOspUgePmJrLxEOPXpPQtQmB1ZA4HkcIHdyiBhhD14cs3lJB8H8vvaEs9BNY8OnGrC6iSjE456A2FGNXGnvzqUySxLMvfoqbL/wkS61EETaDI9Quzu7x6NEDalOflojwwIzLt15Gtuf97vNrtwXVc370IPD9rx/3JPAM8A+76SIB/7OZ/RMR+X3g74vIfwl8H48rf98rICxz4YwdOQdKVW49szBc+xTZhHHMpBAIaty5+xPM0yVD8kQdbZVp2vP6W6/z6NE9fDP0OmwbhHJxBuWjXhyAx/d58Pj+O7KLTnnt2h1u38g+bmzqEtKjE2584rO8JIHdxTlFlfP9hHCT9fFAFDfvbFJizEKjOiCjQ1PGThNarGLdQuykHK+naxQGCZgY+6ZEojcwq6LmiKtI90iE6OlD3VnpnCQ3FmnHrwmOMavNKFZZDZmbp0dsxoEn2z1qhdMxOyW3GavoDITQpzUgLGpOazJljK5yPN/uebKd0A4G7dw3srjYxWJg1am96l+G5QoKEmjZJbjBhF0pvPXonGtHa0LIaPUFwQfnrmcIIoxDYjN6/6XUSkiBYfBR7vXVhqX4SNbho42YHGojGFjwhxSoweXf2/3ccwYck9/wxqovPOaTIGmE4N6LEAyp7hkJPQcixsSdZz7GM8/+BMuizMUX6DsByrMvkpe9W8LVsxK/911hu/0aZtu3U54xzi5gnh/y7yurf6xFwMy+A3zpPT7+EPiV/7dfJ/T629NkKvuiWHPZYxA/vlG82fbc3eeIaSBHHxNpqyylcOuZF3lydsZ+dhLQmAKPVpmLl78NF084xEiLGY8eCdP8Gh+FMOSHr3OenJ3z5OwdH84D60f3eINDSeEPsMgX+cxnIk3VEWXBjU7Lzbsc58xokEJklYdO7/XQlNbc0prEewaKi2eKOd8+ARjU1sjmDDwFr2+BcRzctqpd/LGUK3ruUgrSsdxWvexYDWuuH224lC21FCQY6zGwnxyTlf0sz5ASQ85XTr0cPXZsPQ6UVjl7fElDGFdrDGOZFwaEFKwHcbiOIfTRKCGQJHnSVC2o4L0DXFt/uZu6Kc0YQyJGn59Lt3ynEDherTjejMyl8vByS0XYrFds8sCYB1pr3FsuKdVVrOvkE5nWWg8XddhIjXA6JN/1aWyycLEvzItLs33nN5CDmC34PYwbqoSDbU06ebu4iKkvyFGEZ0Ig332OlEcgeNgLxskx7KZTzKYrSvGYjO+9Ytx78KeoniMivP7G997zrnwqFINBhBi9O12bMqBOTAlG1eqa8hgZVwMQKVpQAstSmFthPQ689PwL3Ll5l9cfnrEtC5tV5u5mRT2+QSyz12z9WPjaq5EnF3+GtW23fUKIgcePH/Dk8Ucx3nnHVRb2b7zMa+/6xCX7vS8K0j0RJgIf/wwvhMwKH9GlHIkhcf3GHcbNCbVPL3JODMGPoHNtFGudn+d9C7XmUdni8WCrEDiJgRQzIfQoMgGacx09odhfQ7GDc9FIXe03zzORxnrwtN1SPRU4hOCAi9QfRDpuuym1mufv4QKl0+MTVpsj5mWilepWbnFquEspmo/7ohBDcLZ/TzjyV8h35gRcLIXtsnAyJI5WCVVf9h244sd86/V8Sglw9+lms+HW8RGX2x0Pzi8cbBpcgp77KSsGoWpXcoog0U8HsypLqzQNnNfKftEOvHVxlQWcOt0Bo1chL8GDc2L0puRSDowqGHJkFaODckphbg3r84cGnFyDk+t3wVwtOqbMmAKnJ5WzyxMWnQnwdC8CIETJpJyoWq581ClGV9MVPyrFNFJL9Tc9uFNvKo0YKyv1uO3WKqVVdovP8K9tTj16LCVvqLXK8VrYLV+mtdLfAqEFeOve6zy6/zoxOAoqAEhgtzvnjVe/8x/gdXnI4/fSWe4u2Uno+4b/T4yJW7ee4eTkRh/jwSpmhnyXa7ef4/RE0aVHjLvUCMVPDmCs8kCOucduN0JzCpGJNwynWojmXPvQ8x5a9B2uLguPa/VRYoAc3s4cHAIMIqRxcClwrSAeBKK10prSdPFgkhCgVeb9zhWA5rW7tg7sFNdaNDy/TySQUFrw1CAx//1MnK9QW/MyUvxekpSQ6r2O2gVaqbmEOQV/DY5XK4acGcfMbgrOuWjOixiSC8ZychHZVIWmfTEKkVoLl1NlVuXcoFlF1RcLCebfR7zZm6O/BqUcAmF9SjREoapQmkerB19xIbj70/CJSEBoIj0oqW9kcNWPqDWQBuP69VMshK4see/rKVkEIIVMkohpYVsKcykcy8pDP/qKvZsndvNCxM1DOQcGi+yrsn1yybQsXVnnlsO5NTYcpKUuUW21MORKkpFdc2prU2Gphdu3X+D69Ts4d97HZ2KB/bTjxvU7vmCYsUoOlfja7pL2F3/80b9Y+y3vBRLfbi+IMdFvFacGpRtcv/EMp8fGUr2rHkWQ517i7rVbrMzn+6frRI7Zo8uXxvFq4PbpEWrGg8s9j7dbVsENRSYJtHTXn5cItTfcqrgb8LBDLqoMpkTzEV5thtHon/bSQMzjvTdrqgrTbuvlkAUMvVrUDsSB1G9qZy66IDb0v6LihOrS/GHPAqjR1AM8o7geoPXmIHh+ohqshszRaiSG6NHstbIs1ev7GN1cFdxQXc2zLJIEcozEGFhq8VNAx9YnEU7GxGzG1NxGPERhFQJDcj3DgoN2Yx+T5uChrtZ9GGIeaY7hOpMoV05ZPSx65n01Cf4aLLUH+DQ/Qo2rBP9/WARiCp3W2jq/30hLpQVFomfBXez27GvheHBhiQSvia0Z+7agGGNOrPuKGbrl1UGkDWs+VnPVgrMJXVqrrHPiaDUwzQ6AbMF5gAHjKF3n+OgYLBDVOBoiKUVulhm986yLfWKkmvHgUeXV791j2n8LeFuEYKYfOmLrcBL6oWuZmPb3uBd+QJgiQnj0Fg/XR2R8FxpTIshn+MQnnuP4OuSoiI2crDZIDFzOM0OrhCDse4SZAbO6Y8896364LeBcvRwoBZZm6Lz44tP3N8QtzIdZ38nRhuPNiicXOzdiifRYusN8zgm9/YDilKa+OIi5bkLC2yrKECJIdclxgNhHy9Z/hpRcVGVBPKuw8xM9qTowL5WL7cw0F/9NTZmLcbxJaHM4SsFYB9d/uPDHrdCI/xxjjN4nMOHJtPdmbGea7ZeF2rzUGVL4ARqQ6yycrdCt373rH4OPirtJx3sjLuT2icVhVIoL3MSsZ3R4HP37XU/FIuAEnNl371J6Qyswl0aJxlqS21zxN6mh7Jb56oVPwR2BISePc25KaZWlNuYwEz1k2zMK8LpaJZBSD7QUb4blmEgiDptsBUOvegY5jAQTrDZX9aXIx2KCj30ai5EF42JZWF+DZ+6+QCsfR2jk5BS/x4/u8/2Xv8l2fwlI3wXptfT+Q1SOGWb1Xc5ZfXzvB0qNg0rrHo+fHDOM/YbLiZwy9hOf4JmXfoqXtjsutsqkFbWAhGuMYyWnCBLRqpg2puJ49M0QuWhOxDm87v6keaJT7EfjhrmYp1bmZfEcCXMYbOk3ejAhJCF0CTIkzIMBfOnp3Xg4yIp9EmGqVxkNQ8gQBAuBZh5YGsXHrqJcyZZjiC5n3k8stR2+GyqwzgGVSFiKN0Y7gNZUMSKraGz7vxnxrEVTL8Fi9H5EaUrpGWYpByS427KZEDT0WPj+UuEbZJbux5BubhMnDosEz95sijR+QCbsOZmxU7Om6f0nZE/NIhDMqK2hxS2pKSZHK3VbaCvF7ashY+IPp6iz1WOvtaq62qvJ4Y2vhNZYQnI5aMOtqgKtd2ijmh9xzcGMQTyTMHS+QMUNO4O4688fAI9NU4WQk+O7amFfCkNOXLtzjNixiz+S3wSnp7e4c+dFGkpOsR8hI/O044+/9nss84TiVmYMHmPw5OFH9Q70Px/z5Ml7NCHefI03/vzrfLNVb2iZYSTG1d/gS1+YmSVgMaPWmJoix9cYB2E9DAiNeTkEj1ivW3v2nxy8bcKyLJ4PaR6rpepE5RCkG4IcrBr7lE+7ELi26iatjoH3w54z+rMIKSdit1SDg14sBLecV/UjfykEerBNqVzudizLzNl2R2lKCsIq+hF/t186nNXDQUV8U6JrL1R8OhDMR4Zm2lORXcuhZo6Fx+9DEzlMQp2FEFzq7WuZb0CxKxq9R+sn2aLuTwm9pKm9aRryysV0waGjIUbG5Jvr+11PxSIgwNFqRSnFKbDqtd5qyEgKLjsNgTFHmgRSTIjKFX/NTLlyX4tHRo0yeE2Fnx58xxEfI+GNlxCMVYrEFHpdeAim9C4xuCEmIAwpUBrkPHTd++TTcvPAkF2Pv4riVJjYBSiteX2e8sDJ9ZuM48B6HBCFfamcXr/Ff/LcC8SAo6inhf2y8FU16h/+336y6Uo3NeXhg0Brr/KjarwP/Npdcrm75PId75rI/8nusluquqRTYyJ/+otMw8Ame9NsWowQBp65dZecR6x1DUIAM2cgotZPXebko+a4+DEKObhhyen/vvureWPPVBmCQ1vn4vi1pfWzQghshoEgRrU+2DDXQpyuRnbzQqmNi31xZmJOXfqsbPcT+2UBcXZ/M/qExb9fUz8ZVFO/PyPQy5wxhh5nFvzhVFhFR4/vtdcz/X49xMapCdXUR6D9fu3nVr+nDqWU+UKwqKsofQTr2YVlKfh5xPsDTY3So+ldn/He11OxCCC+ayRr1BTYVoUQWI2JihtSYgyuH0ApIqj6kfLwq4Ue8yT4bpolEFO+QnKbSQ97NBDtR31BxEGkYl5LXulR8Re79e5x6KQc3wWNxXqDqXjnXAVWIZFD8gbVFdrqAELtqjVzz3ctje00s15lrp9ecxEMEMfeSwZfAAAgAElEQVSJNBe+0hrylb/lC16EaS7sWuG73xGm7Z8Q2HMQI6oq9x+8yeXlk4/wTTPMHnL2rlBSQf74d/hD+cFQUlitNrz4/CdYr48cuSaCMBLHZ7h1+5SjjVN7VymgVUEXWoyk6HWu171KaR0fh4L0Ql78bTMLLFQ3W8WAVEjdshzE2YeivnuHfhKrnVwdYvJgkW42mpfKVDzfMqbAUitbtX4ytP66u+BJLLGOgSRwMiSw6PATfGqxSfTAUvXgGAI1eB8ldRJ4CK6nsOZfE7G+0Am5nwqquhZE+39DHzkqJOmKT63eJ0PcKRk8/HWU93/Un4pFQLr5Qg1iSAxZfYhlDv0IOVKs8HjnR+YDfupoGBmjz55T3w1EBKmVpWgfA3nQqAhdc+27EDm4igtzVxl0h50RtOIc+UopisVAaoc5tJt1mgiTNmzxacQQI0mSN4bMG2TW8eEinpvnicuFUF1rr6bU5ieJUiPVGtt5oRkMQE0ZSYnNkBmyMiyFn/60ovwCY+wPWPBw08fnj0gs1Fp482zLbjdRlpmLJ/c4f/zOYPEP8zJsv31XqPp+v+Xi4uyqWUuvtPPwLDduXeNoY1c2ZEJk+MwX+fT6qKsVPW4r1rk7AB39bQi17wI9l5Mx9bAYEbYK4GKbJpVGj7Ire+bWGKPnL5TaMCmMA4Tdnv1uz8XOe1TrnNkMAfV0UAquNylL6fQfoWAMFthEX5A1BGqf7LmHAoIaUwiIGtI87OaAqVfwEaJ5Z1+L4/LVnG+RJXRwqTs4g3hce1Bj0UJV914Q4lX6cOsOTIlCFQX58KEiP96lxm43O3pKXOKqEogpcrwZWBblcnLpamsFFSWbH48cAiLdQ+ArdDmIMMQhEtb9/e43UEKIJBOaV3OIBVQOpAD/o/XRS4yCBWWaFzi8aepviPW6bgiJTcxXJwAX88oV/TiFxGYcMVfXIKrMGDFmUvRRUTVjVzyKzPRtI4nOBZPAJrjRJ2TBLJKjYQQsgAbhxnMv8tIzdynbPccPnvBkv2eaZ5b9GbK/dAcgfmL6NsIr3/oTuPduedKHdZkZpbzbsjnPO3a7DgLhoHsQ5NFbvJlXpIM6EEHs5/irP3eHcYWbvJC+gDfPUJTIKg4YzdFk+OhQQ+x+it6gNEcMDkOimItvNAgUvItuMC2dkhzd71DxOX6SjFA4tNlEHBijXdNQ6RMAem1P32CCl5an44p9WXrKk/c1tFOpxbxSKNpo1ZkGRzmRYyTFSKkzzZQUIqGj7KJBlU6QltgXOu8JmAVS/9r2/rDhp2MROMRAOVDSH15QytJ40maO1kc8e+OUcbXi/vk5MUWurVfUpWHqAiAfqfjsVFVJKTJIcsCHeWBk63y2ejjid6HQ4VwtvQG1X4p3c5O73FIMEPCEmUO9Vj2PIktgDJEUnSVwiLUx8WNaUceFrXNkFXyQazEwDqPLTw8KPvOR1ZhDjzZXxugNzKlWH2npQTBSUWLn8TWWUjibJnb7iWhCqZVoyiZnTld3OLr9PJuUSVGQELghkZ++eRfbXXg/xZRJlb/4ZuT+W7+H6lt8dD2Hhrb2buvLa9/jlati73Dd4/xi5TkAHMo84LNf4iuf/Et8TBshQtHQ4Sq+QJdghEMZEf1dRz1OHhxbX+fFeZPRswYdSOLItcui7NX7T0PoASAY1XcQ6OCWxYQoqSsxK5aiOx7VnCBNQPqJN4RIDHTBj3aBmpDNMPEelXs0XPPgugHfXLK4BVoEBgseYmKuhlRRqjj2PEZvfgfrys/3uZ6SRQD2zdCgWKveWAl+TFJJHRh6TMoLl7s9m5y4tV7zsG6ZSt+x6VCIVp3UGgdiSFiVDub0oxp9rCgSCUEJMXrJoIGQjHlumDZElHnpXXwcboolaoostZIleEqSKq0ZNfiCJG5P8wOvwCr6iEurLwjBcQLknF140gr7ZWFII8+dXKOUwsPdJVstDGlgLK7eK30OPJv73I8ETocVTSFrJVdjqhNEF5jkGDlKsQtKlItSGSwyJOFYKjdPrsPJda9TtXG2FD77BePLX/qbnKzcYPRkPzMrvPHKN/nut/7oPWfNH9poU/XdCwMPufdeA5PzR/zzP/4dhr6Qq4HJbe7e+RV+/mcXTKOv2GIUcRmVi4EaZtLBsn7SzBI7S9JPlVNxk5oGY5UzYxeLDTLwcO8agrs3r1G1cP/Rpd9TfUffN5dZJ4TB4GQQZjy0NqXA1JpnTAZB+uSDnLy73wSrPap8KaDNE5OTl84YLn7TbskO/nvM5nSlWt9+X0R4+sNHwHfwVps3jEIPd0SgNXbzwv0n58wVRI3tfqKUSjNQMXL0fkCzxm7u0MlpYciZROzcenEBUnBbcqlOyaV5l5Uo1NZ5Bebk30G82eNx3YGcegc8BDaD6+t18ZrM2mGiYB3L5acMN6BCOVhxzRjT4Ko0DCM6SrxVLvcTmYC1rm9HPDFJvbsdEIo1FoxYYT0IaUxsBFgCc3PpdFGvKTc5oyhn88LUiqfyhtCZio1W3L6ah0QyI+WJkEbWJyedV+dBMM/cuMYXP/dFf8B6fPb1oxX3nzzhX/3W/8Vh/H9o0M1msLyXpvHHvQ6jxndc857dvH8HNuMJFxcv8/LL/oNZ//nCtVvc+YW/yc+IsxVjdHv1wdwUJLCU0sNePQhEUmSMLhLLEmnq40ddGZetcnG5d1y8CaJKDkKNqWdGvq2MbMH7XKvkoNW54cdFDRA98RqD2gEnKYC0xl5dA7COfk8t3SsRBGq/ixJuNccCE24sksNvLtLNSe99PRWLgJlTfWOvi5pBabBoRaIw7SYuJ5d6FnPDSdNGjKkHLSoq7kLcLpX9Uv2EHxwi6n55n+4mjwtG8UCv1gUmZkpZKqU0H/f0rm4IkUWgSmTXCktRNmnwGXWp5G5+OpxcY4zkFF0D3lxjnmNgFQNL80Zj7P7/0px0e7raULUx7131mFvFzH30KkLqo6BFfYcSoFhl0cJSuzptHAjVk4aT+DQlxeB6ydgQq0iA46M1R6s1l+dnTNMEKTGaA0hCF0rtS2UcMteGNUMU9lWYLDrHPhkjK1bDwO3bG37t1/5zVkMkx0SMfkr5jXnH8pv/GMyxW4pRCuz3BbPL97sNPui7CrOZ5Z3j8fuv8do/+Z944x2lxgvPf4zPffbLhLhiLq66zGnNyfENNqcDTYs3sOUw2muOdouuOYgpYCFwWSoaIitxc49HjAmXs7MIJXiq9lxqD6dRrNOMvMvhJaNWT9ryslL6dANUGi0KwzB6NkcpVGBpMMSuSu0gL+2q2NBZl+93PRWLgALbpbLOkaNhJOUITbFiVBOaVaa2+EOAMgTvusL/Q92bxdx2pvldv+cd1lp7f+MZ7eOhynaVy9Wp6qru6pl0h4KGiKRBITcRSAQICHGTOy6IuIALbnIRKUICEQmEoLlIBEQoJGohJUB3R6SHdHe6BldXuVwuj+f4+EzfsIe11js8XDzv/mwf25XqbtttllSyveucb9h7rfd9n+f5/39/QIp569UShc0VZt3SgDcFldiKGHwgeAuyMIGFbV3itFlMpYWJFhvLAEkrBWFOM9tinvQszR+uDpp7rNadsMNoNztxzO57i5YG+ISxJMpki0/Ek6tnavkHHmskUrWRjbMFTpbKak5mBfZGuNnMI9F5ktrv5lxAQySEaBl4LcuvE480k8vQ91w5PCDPI7MW9ruOWipdyex3nilVTs43dN5zdRnwbtngrHbMjm1GnrOlLS0aRr2qAUAWEvjz+8foL/07qBpgZJUyb51UXn3phDJ9A5EtuVhZNs3C+fk5OZ3AD8Bif5iXlvKe7/Tqay/x2usvP/TqZZ54/Md57vNXCD5zN7WJQLcgXn+MK6JcbiEyE4553iDZJlB7ziOdZ5Uqc1YmLSzafrxOhVkLXd8xqDEIKBgK3kFOxql0oekiBBDrFYlTOvGUOVGwEagqzHNmagpFV208aSJ5bQalD34//siLgIg8h2UL7K5ngP8cOAb+I+BOe/0/U9Vf+YFfC0NzVxzFGRIrOIeEgNdKlMg2JYKHXjy92JuZd8KICkYPNqFQreYes7FJ6900E0dVZ6q03flQWjdXzSkwxI6czcJam1CoDwERh2RrKmm28Z7hv+RC3LQj12wbgTaIRaPXgnEGFRv9UJnmGVVYYaXEOM506tjvIzFGXG7UHRdAIaXMWEqzqtJWPYeGQkKYsmnQLX3WiDgOy0U4XvRUdWTN5mFXewQ6CQgWRHK8HDhadqw2M5sxt4BYWGlllTG5NErsI4vO4V1nASjRRFHzNBOwkVYuzVsgQvWRNBeGZeHHfuKY6wd/lug9Z5tKVs965Xnt5Vucn30HZN1owMI0b3nzrddJHyMQRt8TgHqX117/R7y+G6LsSpEYGb7wk3zBBxZuJ2kXin6B557tWQbz9C9RFiFztt4YzLQxKHGOw9jzqRvX6YPnxTduce9sbfV9g65EZ6dH1wxPXgx4qsU2GW8qOEAYFIaFkLHA24xrBi7Fu860CPIRlAOq+h3gxwBExANvAP878FeAv6mqf+OH/VpOHH0/mCMsK3PT7Ktaky4HsVpeTInn7ZtalFUuzVBSkPbABmdJst7ZL34RWFoKVSyMoYsto77arl6KYbgVY87lixOEabVLVahG53EumFYdO0XkkqnFwkaUylQa0jy0gAx7w/BYog/OUQS2ObOZZspodWNpYhVrVVjC8iII02xON+etiUTV5q1XplYf2qysEl0AZ5FVuVpEex/M0++rslmdk1fn1DRTc+FsHnHOcf3yZa5dvcKbd+7z5lt3KMVCRCjJakrvcLU2z4DDRY9IIM2tcWZjdCwvt7YphiP6iF9YBuOcMtM0oyEwTraE7y8cX3zuCNGfpjpPaBSU7XbD9199gXke8cFbb6VWfkchP//bHzpn74Ov9+lDpJnt7/8Tfuc9f/Y2q7Oe6LnwnGQV6mee47mD6xb/HgLL0LEMcPVwwZXjy6w3ibPVFooBUILuFgxvI8cWNyeY/6VUteDaJkIyY1pB1BSyMQgBYapKUutnDT/gN/ywyoFfBL6nqq+8ne/+w1/nqwf8v7/+9wDFPfk5PvfcV3iGgnhvc9N5BC0E/IXpZreL7x4awQIr0MKiubIKuw6zebSFXWiI9Q9MjptBTMIq2oCmXtrJwjU1YaWj5QgIJC2MuSJS8ZgLTaoSvEC1ME4EsjOlGo7dMNLy5aoJRQ46yy2cih3ZgneID5Rs7H8vgi8GinCeFp/esNlaGzffgCh+hxZ31X4fMfTXVCpzUS4tBw6WS8Zxy/k8Epy3U0aLIYuLJdceeYQpw6179+jUGHk+m/3VuUAuRnka54qo4gm20KjFutlHbxbaXaSbzgaKDXgS9aJMMh2PLWxV3nZ61mI04jgseO6zX6IPjj5Ga7TmwmFVxqPLzJONQRGl947f84GTf/r/fEQNyR/2+ibf+s77vHznde4tDugVw79JoO8/hf6Zf4O/+K8ds5mFk/WaaU64WqhpIomwLYW5VLpgehLRSkqJMc1MCntDx9B5ttn48ofR1Iiz0iAojk2aWCfBh+4Df+oPaxH4t4C//Y7//qsi8u8CvwP8J/+8CLI0T7zx2nftP956g7vf/RrfwKTDIh7hMo8/9kv8ws855qmQmoy47ubyDqulULjonJrEEm1JQhi+SZq+ey5W31mbzUaIHvtfQSgtZtzYBHYyiWJvcqqZlDMxeoIPjN468iUng0+4lprcXG6pmmpwaJpyvEmaY9dxeBTxTcS03mzYJHu4nRdccGRvi+p+3yNpYrPdkkuFGOia0jLEQOycjadCQWtmnO34uIiB6AOLoaPrIptxYmqJy96BFyWGyN5iIE0z6+2KPnYklDoVfLUFsDP0E+OcWedi5rlqGYfiBHrPbio1iDJ0vZVG48xcCrMa8svSks0sFtRRc+vDOGeuOIw9kMUYD6iiNWFx9Y6nc6E++RlmHOxm98FzKI71wTGUjLQGcRXljdcCr77yu8CrH9Kt/ke47t7mDu9WbYq8yFtvfZtf/cc986ystlsqQv/0czz3lZ/n2WlLGbc4V+ljx8J7cjFrdAiG59eqrEYzQA0xsE4VnHENXVPbHoTIiWbG+sEGog8jfKQDbgJfUNXbIvIIcBfbcP9L4Iaq/gfv8/f+EOEjDucO2N83wcyF7PSxp/j0j/88XxBPKoZjCs7byJAm18Vy2FK2x70LARHPXJJp1MUAIp1Yp34Hh/TO6LPVgYVtV2ouZLVj/GpOBOdZLnqmalx7rRaCGkSYskWFHfUdQzChR4zRSggf6LsFAiyGDh8Cc0rcunePs/VEH+37LftopUOFxd6CEBznp2fcP98Q+8AXPvUYjxwt+e6tu9w5OaGPoNWxTZkpC55I770hxLFTS6qFmgueSoyBinC8N/CVL38JqcrvP/88d8cJxZHmjJRsNN2mlpxKZporvuuYU7PTUqHp1adidKHjxcB+jMx5ZjWlFpkmHPSe6ir3VgnJEIOhyb0YugsUvCepTSp6KXQtv6FvN76Ksq3CPCtFKoOHWoXU+kin88y8nS2ZiI6gMzHuSr/KnBOv3nyJP3j+n77nTvsTT+kOkbjcY2huTWsuC1/+4k/yI5//Moozu3Ur9bbJHIydtwnBfnR4CaZWFdgqrPLMgXj+7v/6tz6y8JE/B/yeqt4G2P0TQET+O+AfvN9fejh85Ad/i0qtp5w9bFb57td5/qVv8W24qNtEnuaZZ36KL3z+gL2Fkqs3gktVlsuFQWxLudB172ymFmaRjfSCpQ87sUmCcfVLax7aPDY4YS6FOCe7kcWhHpZdNAKycZ8omlGJXDgBgVoypWxIqXC+CQydsN3OzHMmBMdmmlCUvWVPHyKn6w2nJ4kuxpaGXBmi8KU/9Wn+9LOP83/+1rf49efXoLZIaRX2mkJNMelrSUouyVh4XnAFNBVc6Hj6iRtcPT7i9ddeZZ5maoa+E7qho2bh09euUGrmrfM1MhmKS7wYwkpMjl21clIy65yJznHg9gjDQE0O5kJNGedt0S1VyMU0FIrNuts9Yeg4tZFv9Jb8tOiXZrNOW7wUUjXbb/Xa+kSFda5MVZlyZSxtQe48rvcswsAQo2nvS2FP4WD/mM999sttqqPNSQovfOdrfO/FrwNvpwJngZoS7wmj+CiunEhnJ+9B4P7Gb/8qv/27/7i9T7tXP8/nn/sSn3tmQSkFLdVOXH5BCp6+nZRLUfIPeNI/jEXg3+YdpcAudKT9518EvvkhfI/3v9Qy5d7N0nmBF154gRdeeOjPXr7Okz/9i3w5DpATeOu6Ox+4fHSlPZxtKuHMuyAuNImFouJbjJdx8z32Z3NrYvYxsBh6FrEzyu1oarLSmorVuSYVddQ0k4ppxHO2fsFUCj527PeB89NzXDTgZ4iB6B0n5xumlImxQ9UxjQVBzZocOqiezWxz4s6bRKlWNQybD9BssFqsp1bVavblIMRh4I237vDqm/cQF7l84HGh4+7ZCkrlsUeOub7Xs56FGjpevfkmt+6eorphmhXX6DailT4Y4KWLjgfbDTnPFLUQkQ5sFdVK54WF88ZbUAixp1TLCtCmstNghJzzeUsCojpCU2juOvW1VDa1sCnWG9Do2ZKNQOQEMCz4XCcajxswc1cI/uJkaY985tOf+TJPPPNlhpbx0PnAi33H97/+T5hfewlKaiIc2E7CNK1QfecE46M5SdT6fmSqb/DN57/BN59/6OWrj/Gpn/pFfoLMiso4F0zM8v7XH6scaIEjrwLPqOppe+1/xqYGisWS/8fvWBQ+6Ov8iZ3BnAv8wp/5CzaewdSHFi/2GDduLIh+x8ezuO1aC+OcmGttw1dDWy2HgUcuXyF64fb9+9w/X5OqxWEdDR2x4cLFOXIu5ijEvODSMvXmanmFu4+rWwwcDJH1OHF+vmZ/b8HB3h63Hpwwppk/96d/hJ/67DP87vPf4ze+9hIzepFFMM22CIUuEtSy+wRDWG/nmVntVDIseq4fH6HZHpy+81aTZ8f5uKaPkWefuMHVo33u37vH1cc/Ragb/ukfvMY8j6RUSHNiLImzlNgPEYmRzWbLWBIL75tsFY6GwJVlZJUK4wx7PuLFvAvqLGMQtcYiqAWVVkG9MhZj7C3FTgdQyFgWwHaeGUttgbDKqVZyrgwusNd1RAQfheCCMQmK8fp3npJdiE1pvMKKKUW7JvxadNHUgz7gqQRMI/G9VwLPf+f3Sfnbzfa7M0rNrE/fHwjz3jHkx3q9bznwx+4JfBjXn+Qi8MHXT/OlLx/Rdc1TLgYi4fIjHO4dEnNp1BuhqrA/DDxz4zqpzLx++w45FUOYxY4hepyqYaWaS03ECMFDbw20kmbLEvRC7+VCyeijoxSotbC/GFDgznrFPM7cuHrE5cWCzXYkTRn1UMWCNOaSyC3xtxRYtASfs+3MKs1YWrpNUbyj9TJskhEI9CHgg2MRPMu4YLlckKYVh0dHzFn5/hs3KSomtJoz63lmWwtHw0AXAtvtltN5Nt97wyJf2+u4vtdzNhZONtm65K0hW9Ri0cR7htgjTjnbjJSS8Y3UK2LJysvgW3ioZzsltpNlJoJQKmybIWkI0ZKQcO8Cy9Bq7apWGraBrkm57fCAF8NzRS8WeZ6rlXhOGRCGLlLFkGSbVBmLsq02lh1Xp7z6nd+Bli0Qg0nEa068+s4YO7GJUUF5D//to7k+sp7AH/taDMvGE3j3TDYBvI/99OO5fpuvf+2hl8Thrj/O9cvXWeZkkwtxeH+NJx/7PJ9/3DzcY7L+QR+9PUzoRdZcH4L5HEph6DouHSxYz5lVKfjOJMIxCtOUmOdElQ7XMFabcbSHqkLvPefnEw/Otly7tM/B5UM225k0F0LNUBNTzhSKjRDFRDy5mnw4RMdOUe6cve9jKoxpJnjP8aJnScd6nrmXN/gHjksHS/T0lLONxX77EBiCtx08z2iBcZqpqZBU8cEjLTyjYHLvisWipWIeiOojTmno80qslUlbbFnz63fOkodGNUWlVut9BHFUCY0cbLu3d+YYnqtpSyxijIaPKxRROm/pVeOcAKNGOTEBWRQr20IIxGiYekrBixJKZirC7By+WH9jHGfO02wLfHEUUcIw8NxX/kUEz6LrWHbBXLLzRO4OoFYzl4VA30denTdML71wMYXaEYRNAZv5qB2dn4hF4ODgkH/5X/gq97YT2zEZ2hnlG1WZ/9mvA62UbO/FPFdqHfn47K7t0kq9/Rpv3n7tXS+L7PPm6y9z61VrWK1HEzB1n36W5576LDdKhVxszh4Hw5yXyjROnAIEbyWbWu9hM5tOwDkh54TLphqjSVS7WnHRs+g8Pg48evmIo4Mjbt874Y2bt6nVUpjOUyK7ytAES2aGMWMSNGFPbUIoZ9OM0I7U4oT1NBnmCwv7OJ/NJBGCZ7lcoFUZ+p65TlQM5iFzZpLCRuwhic6hTTJd7NRtykYfSKpktM167GF3VEpDtcVg9mwVE5T5oiQqVSyvcpom1lO2lGIxNaaKLSA7M45NZM2qvdvhXXCQrHErAp2job4BTO+xHMwgNufcBGSOhRc0FaZSmMdCEGE7J1KtrZNf27Ra2uKhzKVQ5tb4LJVnPv+T0JBqQxi4sr/kmR7yM88RcmHKM+fjyOpMuXXrjNX5S8D64l6rtTaT24dXVnwiFgHnPD/1lZ/llftnrDbZ4shqYiFCPbzaqKqClkyZHd998YwHD/4ZcG63c5uXj+OWnD/uaDFQXXF69u33orbuv8m9F7/OnrbGjgLus3z+s09z9YqAOFZbi/t23hFibx19CfTeszcIJ+drajE7sPMBUW303spBXPDIlUscLJZARcT+HM4zV2MElFJMXerlog9gk7gG2KitTm1WVh+FXOx4W+ZMcJaAowhpzkzMFlPWiL2lGiS2C2azVVW2cBHkuSuXRASqNV/7IOwvHKvJ1JpeHNFB761hV6sQvEm9u66j9448JwueVbURWFHGMjGl0uCbjUlQTY1Za7GEoBAtlRnFW5AEtRl3vDNehG/H9twaprkqm2lGEZOQO0OyVxVWpTIm66+gzhYADA6i71AXehw4wTvLuzDZiCNQwFmadEkTp6vC9bDH0VOfI8ae7Txx92xF3RaeeiKT5s/gfLLPqRRWmw0vvvwHjNtzpJnm7LeD9frsjzTi/EQsArVWbt89ZbM2wcScZrY5czk43I0njO+nhTJnBue5fr2ynW4gkkyY4yPbcc33Xnqe27ffuPiapZhDa7U6ey+P/+O4Vmecrs54d/TgXc5Ofp/Y7QZQTWH56JM887kf4ymsV7B1ynYdmOeJsDimijTdvz3otVhNHIeOuVTOTs64++DMdr4GpURsty9V2dRMlSZ1vjgJNEm52tTCOYd4u5E380iP0Dlzr03FkpHnnEmlEoOBUfrOczAcsNoExo2wRtA5XRhXHaaZqmqqzc2cGbqO40WklpntVIkOFiEQYrAFPSUzJCGkrAb0yJmqnuIULWbw6gUkCKmak09FicG2/1XJ5OrIxfiBYNp6rbQpjxGtoziiE3svdFdOFNZr222ViosdiiUxbVqfY5eLoGpTCIsUA8SmPhaKa1oNVRs4qii92PREg7EK1lp44+yct1YroxQplFxYiHJ82NN114nOphhzzkypcO364zxx/TJD9Lx6+y6CknD81m/9Q8Zpa9TuYpHoayeksxP4Aff/J2IRKLVy93zNtpZGZjE/PU30oBgtp5RE0MRiiCyWx0SMD5DEszg45rEnnmKxHBAVzs7POT9fMU9bvva1f8LJ6T12RHZQTkMg37ttb87bE6e2kn+UHdyJcZoYH251rE75+isv8AfvelFYLvf5sR//l4DC0AUQRyqZUo7YpjvcvHOPvRgsvUmUEILlNojQi2u9bLO00mrs3vuLzDvT5tiEYheMiSizqgFVnLn9usZyzMUWoEU/ELrA2XbNol9QVBlVmTxMUnc/vlGenPXmU9IAACAASURBVLEWXDM61SrsRQd7kRMSY7IMP82ZQBtjsls8DALbe1vsXfSUNBFQsthnP6uJiGa1xUkVXK4X6sRSFBc8vtaW42eLr6qJjbRBxL0TtL0v3ilOxaTVGJsw10ppqLpdpmMIQvDClOXCpFKq4eIcglfDjzvnwLUEZTXEfXAQqtmvc7JoMe8tCyMhVAc4acG7LSfBw+H+Hnv7h8QYuJKtz4TveOrf/MuUWrlzesr5ekMQzzeHjpPf/TU4u8/t2zff9478RCwCQOMBKImZVG2Mb8d8uyFiw3wJxhsIao02V5sgx5nEdznso9K8/EVZLpb89M/8WeZ5Nm5fKx1+f2+P6bf/EX67NsZbE/fcuw/r9S14KOjrI08Q0kqdRx5eG6Z5y6/+6v8GsIMgo3i8/wWe/YKgWhi8Z9kHumHB4uqjXO06Si4Emh4Ak+RazeoILpjDEgDj/KMW7WVz84bMFsE5TxdsfLdNMyUXog/MpTCOM/dPVni3thCMnMwvYO0Aw7d7ac5GW0AKwlTM5LLsrGm6npUpN8OY2r2gWB/ARqgGcw3OkqiSc3TOEVXxpZArrAokKjH2dCKUcUacJfWMRdmL0kJUtZnIzM2JOOYm5LKMRo/zZlbLuRgoRoxClGqxMqedtLyHrjM1I7ONHsVr8/C300XLTZBarXxA8dUQelZ+GNQms8sU0HYqMyl18JZKXWqmxRBArdy7f8/Q7V3EDQPjPNM5IA4Mi9REcokvFsfhL/wSJc/88i//zfe99T4Zi4DayjyE0AAUmZQV7cxsU2smYko9dwH7VOYGecTZLjZNifXq3Oq6nPHOjpMiID5QMXJRFcePTIlLP/uvMHhvpJaScU55/juVmzd/B+QBhiuxh2G9PuX+vTff/pHVJCZa60feh9jNlt8u9yq5/t/8we8/9Af7geVn/hTP7h/jUyaLUep8OGbv6JChz5a72Gg2IXYmiGqBnYpNCErNFCy+aj86+uCYijIXq499sJNDGTd0MViJMKfWW9AWqSV03lnIqFgmoXMGFN3M9rAvorRoc4s9K7VaNkCbpNj7a/zIKMEWazH4jHcOvKdIaShuiCHiJZDyRK6WF9AH8zSsxwnvPT7YZEbFHrboLeI7Ffv5AjZO9RLYYFr9WtspoJpTtaGrcd5OrWkuF6SqIBZM0sVAFzxD8AScma4wy7lrLPa64w3uHm7l4vv3LlCycp63Jp4Ss6C9s7/iRchp5uzslDGBkol7e4xJEan0IbCaM6vNw+znd1+fjEWgNVP7EHDeIJFzKUQXUSq5mEvt8sEeVLUxlIiFbzoHpVByYiqVOGeTzxZDjs9lB1qyy+bFdkQdp4lNG1vFlgr7+OMz1x79Iq7tlqVYZ3h1fodbb3zfVIZOyDWzSYU8brj95qvYbWE7aRHra3zs481pZPOt3+Pdk81I193gyvVr7O2Xi1Sf4DzdY5/i0b1DBna7vjCXAaMYZ5LCmMD7Su8d3d6CqSqaK1ISCxe5cuWQW2fnaFmDOHIpxJbW5LEA0Kq284ZgO+g2FTap0EV7z7WWFgpix2HfeJBFrdtOE/lYIq/ia2VsY7TsO6pLuGaZNXz7jNZCcD1HyyU5zazGmT4KIexKPxthdiGQm3TLexuZ1mLJybWWlguozLUY7YfaUrGlSdCV2lKKcqHh0ZWhM62FZCXT8hBFGpnauJZjsQmCtk1w4c1Z2kfrI0zZzHLGoXBNrwI+GIJPgZSUeRoZM4hT+i6zLUJNhS6aXH2c8wWK/P2uT8YigO3wXTC08tJ7eh/o+g6t5qo7XHQ88+gV1mPmzXsniA8sFgsE4Xy7NSZBZ13reSpMswE87VgpLVTCIU4Iapl2IkrfD2hJBmmslTkXA49QbOwlDpHKleNrPHr1BiKwP/ScTlvurUZknnjskSfx3qjA0zSzQbh7fpd05+YFflrE6rx53KDTxzneTMzzq9x6/b0uOnf3Fnf2Duixm1cEVJ/m6mN7+C4j6hnFbt5+/5BHL11m32XmmvEow3LJ3mIBZytLExKzUu+IvtKSoZyYo03FYCe5mmhqzHbk342Eo/fs9REfhKlM5FzonKMP9vc2GaJYTB2luUUbOKYWRUtGW/imc+Zr8KImEBJrqtL6C6LmuS+pNdGUZo9WKBiDsSTLh0CMCo3pDWojOQnKlBpItPNsp0xw5kMwdkLjOHZK73uCmCt2Kra4edcw46r0zpkQquvI1TQN25zIeWf8UnoV+mBgkaRAraScmYsyJVto0tmapAI1t+YkrYT+4PvtE7EICA00WhXXjjE+duagU4NaiCqbKZGSEmNH7Dv2FgtqVdbjRMuooZTClOwYB3qxilaxgFOXC52zmW3X26q71QJaLhBj0ZlFWKl2pOs7HMJmMkT1NAvbKdF5ODi+zLXLV3DBsxonzs5XqMDj6QnOH32cMVu6cRBh5QInd29STu7iijWJRGAc4ezsHNUzPk7tQ33rDd7bKrrD/QcLfLQaWVvX1F1/ks/e+BRXnOJ9QCj02w0PznsyHmlhsEGtPOsc5r009G/j8zmmBmMJ0kJctCLNr4EPdMOSoe9I+ZQpb8kU4zFm5XwsxuGPEBv/oWA4OTt5tChy7yiK9VmKUaYO+mg7uNgUhN0RXKzMLNlOjqHZnY2sZGWCNmWfOEGKnSZicBeuxNBANpjinC4KZ2MipRYoWm3qUksl4cm1xZ75gKuWU+i8UJ2VTblY3yF63zI1jM9gceu0jcnsYSnbAmkVipLnhJMWRFqV2IxRO5rU+12fiEVAmwpsmpXiHX0f8V2kJIsTN72+cuveOTF0dP1A3/eYOSQZqDQXxmqd/lSyZQS0I+ayjzgfWG+35DQ3fr+JRUuT83rnLPREG/jC2ahnSoU+BroY2IqQS+GsJSEdLQb2F73pzkVJ5xNzmYkOk89ee4Kp2N3WOcdl77l6dJk6runUsQgB7+DkVHnz1ls43mRv2XPQR1brDaerNa/cfPlj/jTu8n6ZpJw9YHPzJRZiug6l4sTT7+/zyJOfaUf6FvrqAseLp/j0DWtqqRXAVLGIrdz+fdc8NNiSCXxqraScmvhGLo7uVpNXLHKjxYs1XsMyBLRaJ37hKuoc6+a9V4Vl59Hq7JRnhJe35/ktISi1nd65QOw6cp6ZVqYERAx8Yj+SNK6fkpL1Fkx9CdrCbsUb1COLbQA7S3BVK3ujfZl2qjA02Vxa869UhhBxwaPFkchM2aTmFciNgBUc7WRjiD1HIzSKnapQudhOsvLOhtJ7rk/GItA03FUsFixoQOfZ8F9aW0ffhBEhOobFkhg8c0pMaW7sdcss3Ekuq9psGqxr20XHpf091hvYbmeSKvOcOfDeVvjWCXelkkqiijJVKyv2xBG1x3vbb3DKIkSbKefE3rDAe7hZMlMtzAguWTc4qLnroghdhb5fot0eQ7CSxwkc7Cs3rl/B+2e5fvWYp64ccfvNt3j9zbc42D8kw0X9ufsoX3zpWw2F/jFd04b1tHmHds0uuSvcvfPmxU2mCiI9+4tztuf+IhVKUOpyH3/8KIfe+jKGwpaL0JecMg/yikKxXcwJB31gCLBJSgyBGAKglCqELtC1BaXikZoINTO1ydFkJbcZlDDr+C5ZygZ9IKVQsj0oRZTNzu7tPEUg8/YDSFV8Q0vPs6Uchc7uh52AT2n3XWP8qSqptrRpjA50gQuv9e0Gs2LBIWDWZkz3kNsiWat5DES1iaHMyVprRS+i74y6Jc4WnaJWBlSM5v1B1ydkEWgxTM46pmlOOEmGzhII3pKESin2gZZMRqmlMM0zY0qUktGSyMXmt4I92KJKTYkZZRh6CjBpm0E7bZ1x1+y1ijYgyFwrSe3IXrIhuqTx6Z234mMzbplTZrVNhGCOttKgo05NBhtamOmOexjUzCoBuaDedtFxsFwwlUqqjlUWCD1XL13lJ7/0M4zVAiVKMb98qZVu2KPUQsC64DPC6zlx9tr3YHP+jne36QE+ojJDVdmsHpZKnrPd/BanZw9918U+y+tPcDWEi0XDc8D+/mNcvb5P6DJzNcBqHzyHy45LC1MHOoG9zrMMDRnvI5evHDPNibP7p2hwBA3UZGIbp9kk0LWSsp3GhKbHx5gGXuyhygpx0bM3dKzHkfNpa/N854HczErW5HQOUrJ0rN34UypUaksOts+p7OhL7f1PuSJO8a4ifkdXsk2vyQEQEbI0zQRWspbWUzLhl7RwVlpa9U6pKIAlXVEtrq5iSVw1G4cw/wCZ8SdjEQB88PgghsUqhWrnKzsCObPyIkqtiZRmpASjzFQrBTZTotR8cXIYfGxzYXu4p5RZp8z5NAHGX1vEjj4Extk6wM6bc2wOgVmtltzvBmJtSr1SccFCR3M7ys25MqVNy34zUU2u9e2oKlpkuQsEtV3kQiXYfnfxgWXfoePMerXhjXmGnOgU+m6AajkFJTqm7cRYEp/6zBdZhmD9DZStwuVS2F5+hLw+t5TkGKnF8/rNU+7cfQU4ecd3xhbOP4LM9If6THViehgUPE/MZw84e+cPwZLF4lWO31wQOkO3VyAsljz2xR/lR7uBzTaxmSpDCIhEuk6osUPFM+uMDx1aCttpBrXY7oyaoehiPKxsp5kpZwv0bI9obeyI48XA9cvHPDhfcef0zJDf7dOyhqc2gZXV5iLCoosWJIIlFe8cmXawsfgva8qZTLk66MXu6TGbvkHb4mR3izkpt7mQRYm0E4TuyhZTDpZqVmhR0OYEVbV+hXOtMVErXu2k4MSUnx90fSIWAZSWBWA1uhTbTf2F1bKCeLpgqcOpFjTtZJA2m87VjmdVq8ksnd3cWYVNLuRsHEAVZb/rCHgijjlZHXe83OdgfwBayGhwnKw2nJ5vYC5MKZFxDDhCMF95DJEQyoVkNARh4YRcKzlbpzpTLlKRpcVNoW/XgzSXW1JbrJwqZZopamOo3U3iMZFT0cxUMoedUYe8qHWbRXm0i4QnP4uq9UNiiKQMw9E5l0+uobIhttk4Wnn9te9xfnq/TQakHXuV7Wb10WG22mz87WvFer1i/XCdESJ3Vg94KwZLEi6WJh3dz/CVLy/Z6Bm37t4iOMe1wwO2znE6Z5TSDD12aguxiXnUW3O4FITKfrA8uNQcffM4sz5fUedE54RUacIpWmMawGLO+4Z2P1h0dMExpUQtrpWU2hZ9m5LkXNqEqk0zvI1ES2knB5XGMLACpRaYtNKLJwRvqkdsg9F2ytyRjFVsk5M2ho07aE1bKOyI4topSPig64daBETkfwD+deAtVf1ie+0yljvwFAYP+Uuq+kAMN/xfAX8e2AD/vqr+3g/++pBLolQTmUwVWpVH5x1BtdVYYjP4qmaMAeZix3YceHW4agZZRRAxvcE6ZbRhsftgajNvnRlqSQx9x2LoON5b0HeB/b09Lu8teP6Vm9y7v7KcAm+utOCNfjuliVwyqpUYzJc350wVm+GKayBUrNdgx3IbtyFmUpELHkHlfJwpKbPsIkGVmnPjyNtOsIsjqyjidsIpmozVgBut24R3hlMf58RcCv0eXFlcQwSW0aYqopXLVx5hnka7YZ2JcVLNvPidf8Y8bu0Y21yEW/Hks/tQPyYPRk5sXvs+33vP/7FhM3ZkNYNQ9I79oWd68lmOpafHCEqSBe079uMVhuGAOWezB4fYTFZc1MxBIE0TD7KNlGtr2hnW3uFbmeiAgz4SvRmhYvscZuxr0XpLF6nEvYmJpBS8sxNkFPusisKohkQrsnu4oTjQagt+J8rgA6qOWi0HwlqjRr3UpjBStOlfhA6lOrWSt2K5nMH/gCXghz8J/I/Afw388jte+2vA/6Wqf11E/lr77/8UYw4+2/73M8B/2/75Ay+t2Y5eKqYFx1ZTUSXgiH730Brfbi5Wz89qXnUnDu9pJwD7lXfHrKbGuBglqUAI7mL0EmPH6XbD0DmO9q7ipbCdJzbjFmqlHxYslz3r7UjfdcTgON3MbKfR+hnORjY7jbxkE6IkWlOy7bK7TrPzHu8Crh0dd0iz4gxpVmulys6EYw7Kort0RrEGmUhrMDm8N5hGqSYsEecopbLOuZ2QitWEIiRvi0R0ketXblDUXHHWR7HUoz/1hZ/leOiQGLl9esY0J06GBatXvo2cP7BKtOHgT8+Us5N7wPl7P9SP5LrJrXeDe3kL4N5dHrTkJW3d8eg8Dx59nEdvPM2cJ4rCdp5BlhwdPMbhocFU+uDx2GkKrS123nbtPjjIFpaLNCWgtIi8Vs8Hh3kZGiG7FrUR9ZyM6iyWVqxqpwvrE1m6lhebcqi+04XYplcNyW4IfExYhD3cKtajwiQTFz+Hoy1IAFhvQOEdMvH3Xj/UIqCqvy4iTz308l8Avtr+/X8CfhVbBP4C8Mtq58nfFJHjh7iD73+5cHFkT7tGodgxKNeCrzZO0lrpYqAfFiYk2m5hnFAqRa3+DmInCW0iIR8ErS1SvPUZEME7IatjtZ0Ypy2HQwc4Yj+wHUfmNBOCJ3aBxTCwmSbmUtFpZj1OLXI6EqO/oBl7kQu/fhGj71W1ZpW23zOIPfj2MQleYNEFUs6stmYdFrHdp/Ny0RiFtuM7UzGCLZRVHJuUm6XXN6WedYrnYnNkk7wrU0qM88RiucRLYJsm1tuJKrYI56IsFwfsHR7Q9T01LhjHicf29tm7fo31+QpqInQdM8r9u5k7N9+g1lvmzY8R8Y6z1Tkvv/ziD3N7fTjX6uShQFK71utTXn/j5YsJgYFF9zk4/CyPPIItiMFBLcilq1y9+ggH1er5LnrEZYruGAbKyWYkqCOIMROl9VxF4MI6WaHmylhmRGyh8c2zotJAJU00VVpX38lOKG33LhcNPyMJK1iYSLETshMDxVgCVr2o/QtCujj+W2lbaSKoD7j+OD2BR97xYL8JPNL+/XHgndSN19trP3ARKCLMquRd6IXa0aYToBZythpZcqaPnqOjQ6YpMeVk3dZkiOXeO3oXWu1ZCd7jHEwpMzd9t1aj7Lj2ps/jRBc8U8q89eABob/CgzMLgzjcWxK7SCmWVFtrZZ6t3h+6nkcuXWLoA/dOTlmtNxbBpeYiK9WalGPKZC0MnT0gWq2T6yqWHacedjtxa4BaVl/G0xx/4qla6JztGk4CztPk0TYP9zsevZaL7EFLybXZdNfSmYNzLLpI1xbeWq2vIN4WybEUTjYb4nakUll2kYMusNftI+rs+3hhM274zBPKZx/7DFmfoe9tEZ1VOd+sOb58Dd2Rhdqo8PzsHq+/9tIf47b7w13zPDK/J5DknHHzgJP7TSm5e/nwEpcuX+MQG/OZjuQSe8c3uHK9IwxCycqUlDFZcOwUSmtQNqJAmzJZc9BWB22yvejchTBu0so6JybsZOudw6sJqFSNcbBryyxiCzGtM1OpeFGqs1BaqW0MrpUp2+ZSdz0oE2JgqVgf8XRAVfUPywl8Z+7AYnHQ5Lo7YUOrvZ2tdinTxnUZFcf56Qqt0HUdi2Fg6HrmNFFKNpmnGIr6YoVuwglVKGJSS6qjRH9Rcngf2EwzN+/e42SzZprt5/n0o4/QxY71dkupynbcMhel94G9vX2eevIJDpc9L3zvJdJ2Y18rNoG6JnrnSVKZq6PzBhw1yo65x2KjwI6zUXZRR/DgpKJZqM6hmknF9gerabFkI1Wz9tZKaHdyO02akEQcg4/NnacMwdPFyCJGC0epdlTdi9FUcqgJXMRCSXovFNMDk+bEOhdKziz3errYI2XNMjjE9SQCc1U2UzbI57DkM899iZrNF1yqpTflccvjj33GhDZaiSgn3vHqW68z3noFPpbswUqtG6aH14Zpy3T/rYsQTbuhl/SLY26/HvAtpbYWpSyOOHrqCzwhhnBX13wHrVfhvRDae0oVkIqKJ6dy0ccJLXezYiYirwaZ1dbvmWvlZE5kL+z3HYdDb6UjNlI8m+ye6cTjnUelXHATvQjizcyUakXDR+MduL075ovIDVpphmUSPvmOP/dEe+1d1ztzBy5duq45N6RUywCMrq1k+jZ+SpxSa+ZsdU7WzKXjY7yLdLFjiAPClpQzqWazgFar41KxnbliPEctmcG7xrZXasmk0bDhdIF752sb6YmgObN3tI93ymbu0ZoZnGOjZqqZU2GzHVE1yWkqNk4s2T5d5xyL3tMT2m9hoyZRE78MwVMFxlKs5iuJVO3veHF4bycAE7kILnpcyVQt9B7wjpFCScVOESHgMNZdF+1rpJRatqHdmMGZBLrMmYqyXAx0xVR66kwzsegCR0PHXCvrMTHOM5Na9uF+EYoUBm9H5qyeec6crkdqUbrOMbiIVs/YTjjeGbyj6/cJ1xcX8lenlWsOnrz0COmJz5lpBuMzrk47Xvjua0zT14GPYXGolVrnh2gSiZzP2a7g4sigoD5wdvcm9xqZRQTAEeNX+fxXHJ13dC6SMK6BVmNmumK4tN5BceEiaVvVdAneyYVXIVeYSkbnxCIE9ryz0JeSkWrp3FNrFke/Eylhfam28+9MWIsufuCv/cdZBP4P4N8D/nr75997x+t/VUT+DtYQPP3n9QMqSskzQczq6b3JdD02Fei8XHRqi1qtU1Pi5OTURmvA0cE+89xzen6KR/HSZsUu4BEoiVpBi5LEtN/mUTR/uUWNm4prLJkeRSRw//QBsbOZdC1wfHCI5Jk0bthut7zy+mtEr2y3W2bzHWO1XbVjvZqrLog3IUkz1ew+cCjkuWG6nHX+pzzjU6APRswpLWhSnNl5y5zBK6Pz5GopyCln2x68kW1nVYauo/NmAiq1NMBGo/yUzHqaqAqx8zxy7ZCjxR53T1astzNI4Cy5ixOWE1ilQi7KdtoiMxx2ZouVYg3QUFofo6VGe8wYE8SMW65kxprZlsrBcsnRcmC9nZjnieXePvOwR3Y7VZ/jyiXHM08/grgfZZwTUxYj/E5bfu83/gGqNlYGLkaaH76tW0HLQ2NNoBbmkzcvlqZdE1rk77P+tUZ22q0azuG+/FW+tH8ZyowXg75kkdZUbjW+C/TBt3LQEqiLOiNHTRPSRdMkt81k31usXq1vqyIHH6kBUs5krYy5sAyWmfFB1w87IvzbWBPwqoi8DvwX2MP/v4jIfwi8Avyl9sd/BRsPvoiNCP/KP+/rn5/e59f+4d+x7+U8/rmf4EtP/wiPlqmJfxwxDIS4R+hoOX8KDWKRFYauY2/o2G4jJc1tdi9kreZmK4JgR11FSWXGE1l0AQmeivHsq1Ybv+TEeU2mBzi5R98tiN5afiIeFyKalc00ASZ5rdJIM9VgGkl2vQFtk4DaZshtPNh6AFUtTy86wYtl+5UKYzaFWVWld3YzlZTZpEROmX0iqUjzj0MUO71IFQbn6RuExQQuNlatQQnZrKwhBBZDZM6FzSbhdWSzGZnmTBcrEWf6BIy+K6UQQ2TZd5xtR3YFoG/H113nuqYCIux3ziAi2UAaPVaqBSqSE1I8QwAvlg40kvHY+DTVSvCVy0cHLIcjTreJ9VTJAtTEv/rn/jIHe3vsD546z5xs1qQC//jX/j7TNKJtquKAEaVs1nzoqsl3aCn04qUT1u83KPnNX+G3nbuYKACEOPDY01/g0iNPmMHHR0qMBD/gQk8XrS+RqZSKiYtqZi6V0By3e96ALNuk9I0xKdkx+MBUMrNUnAuM71FuvX19MnMHWgqrvygFHD48ymM3fo7PPptIVRhioA8R2T+gP7zCgXgywtlmTU4TvhlQRAx1PeXccvhsxVzGSB89h8MC721G6/BGj1EllcR6O9M5x1EfOdg75vLxQTNvOLabDQ/WG7bzjGvrPjsDU97BQNWAJ21ktcuId2I/2yCGsKql0vUWvz1XE8bMaOMAmVpQsNpunGe2c0GC2W5TUeZc8M5myp236HUXAiEEcoXNNBqQs1aGvmNv6Ekl4RCODpasx4nVakspBh2ptdJ3gaO9gaJwvh1xOHO/Lfe4cXzA/QcnHPWZro+k6jlbTazXI85DcIHeO3OwlcrcGj1BzUN/NieKigFWG9+vVIuKiyEyFRM77Xcdl/cHui6wGjNTtV2wlowIHB0ccuNwYLtZcfPBKSmbkGY7mfymjzA4x7dK5dY3fhN9cKc9rEqtwmarjOMZD1OkPs7L+2iTnnZwECJd/zRPPPkjXH80Mc4tgFeEgyvXubrYZ8AoSnNVFtHCbDez9bnmOtOFyLKLjNNEqcrgbGH9u3/vv//k5g6858qJ/HC82Px9Xvr+a7z8ytsviQg8/hRPfO5LfCpntrlYg7EUji9f59LRNRZdJMaAL5GcM2WeEVUL6XSwGSecF/BGu0WEIDAmJTXp7zZnYh6Z5oHYBVRbFp9WI+aIkrMdf1GxcVwDd7pgNX0uht2iGViMylsbD9AUk0Wtk0sV00W0EaM4e1gW4tnkyi69b3QBXwu9t+hyEddI2pXOVwK78VRHEGMmDH1g6D3R1C3mjSiF9TiRW6CHFutuy54S44DMlTRtiAKaJu48sP0JnPEA1ey8fReIzk5pO+Wn4Ali79PkPTOVRDZk1qwNcCIkVULsONjfw08znQhPXDpkiIHk2n6erHYuzrT5tRRqdUxIMwqZHXfolaUEelcpIvx4F/iZn/2qTVJUQAubWXjjZuGV119A5DXr2qOknDk/u8+4WX0st3op6aHckYl5fp4Xvv1tXnxhd9iwpUs+/SM8e/UG18TQaHOFGB7jyRtXONrvyUmRbJqV0gREXiqpZM7zJ9w78MNd5ht4T2nzynd5+bXv8Yr9kYvr8vXHuXzlBkMDjZgV9Qo3rj/DE4/u7KQ2GlMEycaTD8Fz5fiAJ/ZuUN/hTtxfLFj2HUhgvT5jPY0m/sF49UJpTrKmGJRmuW3+cK1ANelGMrQdQ1twhGruMISp2hw4SBOCVGVOmb7vWS4WJJSNmBw65YzXyqVlj/cGtcjZODmKEqPncFiS82TxY9l2UFETV6kLpKqsp5lVyQ2MaRr3EB05VYIUFiGwyZ6UCq4KQszYvwAAIABJREFUU5oJWigd9qBLNH5jtGnOVGAqymoueLWYsVorvlj3e3COsZr2vcMq54yNO7fzTPCm5b92uAfBkyssYmDz1gM2RQnBJLnTtOX2g3bCq45SZhDHQe/pS7EFyoNXK4WoJj2uwBAqzzzpeezJL6DuR23GrpV5nrh98/ucPrhtjeQ23hQRbr/xEtP0fmqED/uqbfb/0Mvf/ybffvlbfOddLz7Jm596jKNLzgxHmC6ES9f59Kee5ZpmHmxHWuTz+17/P1oEPuBSQ1A/XNTcffNV7l2EhOzaNgfcPH6R167aAT43B5nsH3H9J77Kj5bKHD1PP/4kv/TzP8cyTLx5suHVW3cZ58SD1bllIqiyHTNBDSNVVZhVyPr22aWCYdJLNra+8w2SYt4vL6bw6pxQ8M1z/raisfEsiX1v3f1q6UBdjHSHx6YEzJmSE0NwDAFkSobndo5SLWHHu8m06lWZkxlfSlHGnHHe8FqmLLQutscQV8suotjpSXKi947zRuGx8sTGk6qChMp5qkxTIlDZImxzZZwrUW1BFCwrcOm8UZCdfXahQTYrxoWMyXO0t6SLnuTMiltUuXTQsxr3ePP0HFElaWWaCuv1RIgCxYQ3EcNyTaXSi4FD7WRi8mtXQdSixWbMRFXLTsJtY7tnPv1Z/DPPMmZls3NtIjxy7THG8RywBqsTeCMEHnz3m7A6+Sjvcru0wVjf9eLLvPTyK9aVe+e1d8Rbb77CFS2MpVL5YAPRJ7Mn8HFfzhNvPM2jmODoeG+Px69dY2/x3/C3/sYe3/r+q9y+c5dSKjlteOXWTV5+4zbUShcsLryU2mAmah9WVXTHKWjqLkNJ2c2KOJOqQnM60DgIBrcwj4GVE4kGpHQBkUqQeAGqCN5x7dIeZZp47a0HpGw0pKrK1Jrn0Zs2QQT6znQGZ+PEVDPOCducSNk06csQOR4ivfe4bsk4zUid6LzBRhWzQxdJ7PfeDDGh487pyGqzpvPBIsKrjUl36DBx1ngdvJUgFejEEwTjNtTK0Pc8fumY60cHbOctOCHESIwd1w8Gbp9seP3OfbaT8ffBdrHB20LiQ/z/qHuTWNvO687vt75m733Oufe++xr2FDtJpCjRlhzLtspx2RUYlXLKKKRQqUESZBAnCFCjDAM4ySDILEiAIIMAmQTIJEChkAIyqsBluxzEjttySVZkUaRIURS7x9fed+9p9t5fszJY37mPzSOlconUqz0h3nmPtznN2utb6////XFSWO+2bGphEM8qeIugV1P8RSdI02msSyUj50cSqjn+ohdKLpzMmbE2zYQTFjFSnR0DUzFD2Dp4NreuMd2+gxNhsYo4gWtvw/dffhnu4X74CV7/Gs0EPu2rFtJbr5zLHN8Gvv0SiPw6L/69aA7Ckm3f2/f4f/Pf5mvLixxud/jgoTSqjjpj01Xzfkfvm+7fAA80iq7zwfQLc2lR4s0/0CbspVpisTggJ8Q1Jdlsu/zVsu2BxbO4sOCBJx7h9rvX2bx9E1ToGkcBNUqub+lCDm8y4gZXrRXGlCwUw3s8hnqX0DF0gW1OOKf0scNTybUYR1ErTjI5gyPinMlVK2LJRRjtJjpBvJFzELPanvvhsQJZxOTVIrCIkcsXDlgsFtzaCNt5y8EisVLhresj1083bKcJL55VZxmEy6Hj6YcvMxZYn51BKWhNzJOyLZmSKgcEVhLOWRLBPu9WetWk1533qJi3d0qZbcpsczLTTjDwTKyJbbYQUlUYushBFZbHV9gNxzjnODgwC3tcJVaXVzieowudYc00s1kL3/r6VeBPfgJv9Htf90UncOHCFX3+p/8ef/aHPwB++31/tw9ovJ8u6Xo6H3DNmIQuODj4Cj/9U1/liSdnNlsbwnlvDsLa5IpeLORCnVDJaDLJbwjmYxBRpDaHobfk3bEoMUhzu1m8toqSE8QQWC48q+XAej1ycrajD47V0FFzZjdnHBAxP8auVBtCNQzWHkpR1YI+jdzjGPoBj4FTD4cBrZWzaeR0NzKnCec9vRcudEL0ERcD19YTm9H8CwW1lJ2iVAzUaZ98GyBGH84Bo7QP5HrKHA4dTz10mVI6NqNHZEcMmZwTu9E0DcHBahiowDbBs595kr//N77KtbNT/ukf/Ck1z9w4uUPR0gbLQlSIpTIrFO/NRqywLZVckw1oxCAjtShnU+Z0TqQWBqp4vLNuQBudyAFD34G0PAUt1Kx0zhKk5lpIqYCqycJRQoyAYzNPUEcTJylEFxhPb/Otr//f79c5tLnVx6HB/iWv+7cT8N7z+CMrHv/3vkDnnjWvPRaasd6e8f/87j96TwCj3UnmUn5iicU6T0zviwnZsht/l9/9vX92HhACQIgsXvgFfvrJL/CIKDMOJzakDDESCPhoxpFcleDNGxCcY4c2SKml7WqtzOp54OIhi2Hg3es3UZQ8FyZJpJSNu+cdq0WPrz1Sz0i1MlVhUrNcB4WVmAEmLnq6gxW3NxPjPHK0WDF0PeO4YTNl1EtTGjpmUUadyWrnfKqD7Cw5OgDO4yVbB1AFTyD7jFqMTrvb2h47FcOJW/CGOz9KnewqP3j3BquuY4hGgbp9Otkg0gmHweAYaZw4mxPZOaa643S95vKFFQeLgXeunYBa/t8yBiLKNCZmVwiuYxCxKHQa868ZubStdOdiVCnx7XdUwSnUKkzODFyDA1tVGs7eGAGGod9lcL7ivaOPwRKfMTT4OE+2KQ6KYAo+rwY4ufjgo/zq3/oPrdtshp/bzvOdq69z56Wvw3bdNCoGOUk5oznx49A+3BdFAEwNV1FcU7ypKl5huVrwN//ub1j7iMNLYCPwzRtvc/qNP4DdtrWXxmOb54k6bfnwaPXTuN4frU6a2X799/njr//+h/7lV77y13nwgUepOCsAUhj8isvHx/SLSJ0LoRMDXwZIKbMplTFVejeaJFlMZ66KhYeidocbM32IZDXPhTrznwtCpTB7wQ8Dq4MVy+WS1WrFlAs6Z8ZpZDtNZIXeBaZ5YjclNjmZoq23Nnyu9r1FK9OY8NXO1bWonZtrOe8EECEGa59RYEp4TBA0ptxi50zANBVlgXI27djMmTFlohdc7NmpQsuS2GXj+5Va+M4P3uIzDz/EI488wPfefZez2SLb8pxMwx8du+Sb58KWm3MpRgNqOQdZbcuwKxZs2gVP7zpUDViTzw1CmVw97ZBjyPNqqkgfOusIsJnQXAuuzWRCsG5jTgWd1PbCzpydpRg+VX1ziIqnFuWgwJevPM586RETjHnPpa5n7Hq+dfUHVhzG9fksKVfYbRKqa/5lisP9UQQEghhHIFWTCWctbFNl0oxz3nbpamvCOWeeGw5wv/R37Hdtw6e1E96+9S6b11/C7dZNySbMCU5u7cj5Bo3J+p7rJ3PU+MY3PlwY4HN88fkXePRRSLkSmhedg0OWDzzOccs1uLo1F2PwQppmxnGHx9PHgDjHbjeyqVucd3jf2UYjJaZq+gKCUYDTZseqFI6PjlguD9myRvNsZpTW1h91HT4nqnpoK8+xFpBi/AM8qWS0aeSd2P8nzjFnw26JS3RVKdkKeXAGxCgKwWkLJrGVqHeeVIW5FrYpkWpiyp4pK4sYKaKMWthW5ZIP5CmTxJFih5e7bl6vSqpCEmEYIosQ0XHGOzUAaZN3i6Pl/7nz2Y2XymLRc+FoRU6ZG2cbtKHLKhaQEpqHWPYQMidIMOEX9S4XYC7VALmd5Tou+sAkZjKignq7AYpYKrLSrPB7fcD597HVbqawShO/cuUx5KEnwJt/4GyaOJuFF795m5z+BIs/xwRI4rj17pt81Hv9vigCgtB1AXJBz+23ynrOqGsyU4EudHR9ZDdPnI0TaRqtGjci0cIpzx5dxv/Mr9D5wGHfMfSBW6eFl75zkzR+G2HHnqdfVfn+a9/+UMagGex+Ep3EK3z7xVf49osfePjgApe/9FU+n2bGho5yElktf4bnnukoCJ03R+ScZ2rJzNp4A1IJ3jFXx5yV6APeBeM0FOVsu+P2ZoOqYdFX/YLjgyPubDfcPDtjmRKDVE5SRVu3VoqebzU6V6jOyMlaHWmeORgGFsuOaydbtrNx/YDGSTAd/lxMVemdkZoWQyRET24zIJ9McViTI7vKrIrOmYwnN9ZCHz3baeS73/8ed25f590bt9lsdrgYmNVmK5Mqi1pZOkf2Hucar8K8tpRSGEtmbGIt5x2Hw4L+8JBuuaLPI2fTZKtStUGn84YFm+cCYslFvj2fVrxtu1Nck/piUu7adCH94Cmj2X9di0Z3cB6A6pyZ5Swf0d6HrlrgydAvoMIuZ0hmOpvUwkpCUL70C0uK/mpzO9a2nfJ879t/yhsvf/Oe77r7ogjsJ+LShkRjsnavoOd2WZzj0HuOV0uOhgFhzdm4baioPUKhDbu0qcmKME0FmHnuiwsW8ZcsyNJZuERWuHz5AWO3VXvzOSB0kW984/8lfcDWamK88uMc1Pxo1/oON//kd7n5vgc93t/hzi17m3igilAXCxZPfIHPhCWuKDE6QvQs3Z5Db89UzoWMhaKkJmIKndDFwKXVAbtpRKqynQo6OIoToggHQ2AuymZn2oXS4JeLRU/AsUVZdpFl9BwNkUX0nI0zKWcTRjkh5UJusE7vPIuh44HjFX3Xo1qsAE0mGhIJjLlQipJUzUOPrUvX2x1Tmrl5B27dvM12ntllNd++mBZEFKaU0AKlttAQVeZcqaUya2YzW4QdTjjoBx66cpErVy7TD0umzRknZ3fYNrydAzQD3uGi/XlvG07JjkAHztG7yAkWl1ZqRYppIipyN3EJc81a5oUiamlXos5UqdgHWKVSKRQt7FJCMKmwVjv+Ja2kavOYmiFLos2iLamJxDMv/Nz9XQTA2l9RtSfXOVwyxdc+pqmipusvhd57jobeWsWU2Pc9Xjydd01CK4wl4Yo09Q3kZGfEilVh8Y4XXvh5go/kVEm1IM4AD6vDi4y7jZnIEJwobyC8/d1vwq2r7/nJpbnzCh8+anySV6GUb/CXL33g4dgR336dd7ylJpld4ZDj4+d5+pkHWSzvagK07kNAWoINwjzN3MqWZ3/U22prdkJmRmvm4nDAgJDZMM0TzpkIaZoz0gV8DEgXzh2VwTuGzkJAM9qyCZXolM4bGu146FiGwDwZAixKYNH3zHWHw7GMjonMnM2daeLLym62yX4fApuizFWoNTPPQjf0xN42OEkNMNs1VFeaawtFscJQsWNj8JYkFIPwpScu88jlS7z+zk2u3rzOejdaSrCYRgAveNvC4p0SFBytsDRVomIhKSAWhAJU3xKBigFvghcWXW9w0znZTEVN+edoDsNqx5RUle08EcQTnEfEkHkF63r2/oPq7f9xao5RcZA/xkB0XxQBwbIFNBcT0XjPsu8oatz1IRo5bpsyfrelcxYptQw95DaAccLh0HFx6IkhsMuJebZVXRdtX+8bly+3kBKnnjRPZJnIuWmxvSejPPLw0xiyzJ5cL8IVqXzu4oMwblp0NFAcV99JvPTyd4CXkf0vpJCy5SF8qleaSe++ydX3Peh59+prvPnGkhDrHrMAw5KDL36V544uMDTG/ToltBYWfc9BH5krnOVErpmKsB0TQz8AMBdYYWfVO9PMXKqtOlO2HEAn9CHSdc5MXKm0Aqz4dpYuav76XAqbabJ/U+0uGETYVQuLXQSPkplSM9tg0uch2gfXe9NCbGppaT4WwQU2dMsqFOdAPJVKjA6cY+E6jhRLvg4dwdtA8cbJmjtnI7fP1jx8+RK5CjfWJ6RsTkgpjpzMAxE8zFWMiqzCnVzRMoE0Gpa0gWQzCQTxVOdxGHOgD3s8fsE3LkDJLVSlVMj2fKijKTtBKoB5I8xlWEDsWOKrmDhKzIH7w+Ze90URAAN/KobtDr29sHO2HfozD15m4ZSXrt1iN80UZzt3QelCoPOOKxdWPHrpmM9cPmYZHS+9c4PrJ2umlEhl7x3bh2M6Enelpl69DXyk4FGceEpN5AK7NKIIQ/QsnGN5eIweX2qYbtt1Hx4Xnnr6ARw/S3QWUzXlynde/v947fUX36dzEHGkeSRn20F/Olch5zvcuXPnQ39z++ZVrr1H82AiHvi3funXOVh0jFnYlUTVgjBwigl2xpKQ/RHKw+k0s53tqBS2E9EJiz5w0btzw5S2rY8gBtl0gaGL1ApnU2KcMym3FGmtUG0TkKSyioFOPSWXBtJskNhg3IVSC2HoUTzT2ZqpJHyxGLreB7wTC3cZJ2oF13n66OmcxyHM2eO8I8bIyemO3/vTv6SKcuXiBT7/+OM8fiWwHTec5JaLsce6AVpsPrIH2Kpy7vHPruDUEYO3rEZx2Me94J00NWc9R5mH2gqjxxIr1IopqudiJi9KLxWPZxJwTfKdtUBxhNqIWU3/oGKqx4+67osiYGf5vW0YNCnbcaZSOF4e8NQDF3n84oqjgyV/8frbTFM27FixN9Rq2fP45Qs89eBFHrtygYtDJNfK4WrFdi5cv3PKercl58JcIThvpN4gVG1UYApoJWUIUtGmj/eYWSW0pF0AUetYnBpW3LvCcNARpLMnWyFW5fkXfp7HP/eCfR0xTHXfDbzy3a/z7tUfkFvmnqoyI8zT1rQPH3i9PtEEoWl3TyPt//U7/4ftpN/zGglf5fM//RCHR5larKtiimg/kMMFlotDnItozZyd3aJqpfPOICbV7MKl2HnYecEFR4yOC6seJ45byc65GVubFcp557apSufsqLfn+KVkuYUHwwJXbBDno4Fca60IznQSVIKPxmYQoQZhETyHXbA7u2KWb1GG5YJZhVs3b4Iod842vH3jJpoLm11iHFM7npaGIW+y4wBeLcbOIKJKJVCzFQvvPIf9gpILm8ns512MeB9tK9KkzRSlZhsS4kxDU1FctUQh33QollkAeFM8DuIZZ0WLgjN9yarriA5ylfP/717XfVEEaC86iGnvJZOB42XHYw89zI2zDd47PvvIFbx3/MX33mQ3pfN2yzfGf8qFcUrEwxWrxYDfzTx05SIHhwfcOjvj9umG7WZ9HipRxGYHRik3AMaeWkwxVvvQxUYDUgsOoYWEYJNu72iPt0hsNauGOM/QRzI9Q7SWb7O1ruKnXvgaX/mpr5FrscizKXE1RK6++TL1nddwTRjlEFIWbtxcA7c+9Kx9kkeNfM+v/Ue8eI8ECXn0aZ567mf5nNpdfDetmU839AcPUfrMmJU5JRteCezje2uF3AJMDxc9pSibnViOhAZ8cSg7JpF2Z4UggeorWgq5JtbjRNf1XFx1DJ1nnDPrdQeqLIYFqoWUE2Z+tiOBUyVlJUWbA6RqIJmilXEcCQ4WnWfOhbP1lhc3b5JrZUozVQx1bxpv8yOoKKk4vFqsvRfraEVM4jiVdq7PpaUXmehIi1GJfWi0oOYcMys6lj9Rq81Bmk4hetMkFBWyWUJMneqEoxhtBgDMjWgcQjBm5b8KWegjgkf+e+DvYOC3V4HfUNWThiV/Ec7djn+sqv/gh30P1MQbdr6zNczFVcczDx7z5OMP8OIr3+c7b1zldHtk50XxzGVuraVSq8WQvXOyZjtXfOi5uZ1Z7yYODo+4crAgiI2H8zQyTxO1SnPlW2Zd9BaOmbLRjjVnmwc4Z5l12F0IpCUFWdGyoVptDi/rZmxEaNV7IYHeRdsni7WIVe8Ofbpo7MEnUZ79/Avo5798nrEQnGOz8/yLr78F/IWJfVqgZSmZt97+vr1G3O2maq2UHzti64e8fG+/xmtvv8Zr73lM5JBLF3+Oz36usp32KU2KWx6wPH6QC6poyYgWzrYTijdCQQh01XEwBA5rwlUYXbZtgsLcXJxg67iz3YzjjN4dctQvCX1g2QXu7GbGaUJbN+KCJ1SDcRqB2PboQ/T2BAqAMs8jznmWsQOd2FUTUI2pglO6zpKkdS/prbZudvtmTU2aXcVk3s4bPSnPldNxbmxJy6KsuZBlwolBdOympHhncxCKQVK6NvNTMbFc9JblaPmG0ItpL7rOsxsNKzZVZawmGw+tMH3U9aN0Av8bHw4e+W3gN1U1i8h/B/wmljkA8KqqfuVH+Lrnl2Jecg2KcwEFYgysFgPb9ZrtuOPayRk3Ts/oQmScC6iQc+FgETleDjjnuLXecmszkaqySzOlVNbrNVvnuH77hNPTLVq0sQprC3ywNtX70LLmqq2P2L+qxVozpGnMrVWr1boGbW2usA9+bAm3lfMkotQ2GF6tcs8pUb3hwRFHDIY1q6VQNeE04lQZsTvKX/vaFZBfw4lNmYMTSpr4yxf/zM7wztZeU05sxi03r77R2mF7b1dgLpmaZs7jcz/hS/WMm7f+GTf/9AN/cXDM0cNP8mBLZHZO+F68zIWjhzk6jsRgQR2bnSN0kX65IsyjfaBbBzfWglQrenPK3KGStLCdZ1ZDpNbCLk3U00IIzj7QYmAW74W+Mw1/LpkxZ4YY6YK3rZJa5Nti0SNaWU8zcwuOFW3hN6oYR8Isxd5Vk3+LI4qynw5HZ7qHORt2XcWOQUMf6cRBKfggxBjovLchoKqJCV3LFcRRE8z1bj7B0AjZztmRoWuDx6pKFlul5gou2ADRteLyUdcPLQL3Ch5R1X/6nj/+MfD3f9jX+fjrPCqBKsbdGzcjL791jVUQbt5Zs50yIkIOxrTzzkIW+hhZDb21e6Ww6AKb7ZZNSlw+XBKd42w3UioMi56ss2UZ0liF4kELqVgO4P6Ovt+4uJYVJ7XS01De7fm0XayJRMS1hCMt7fjg6eOSGAJpnu2Dr1bsbJ3oiG1r4QR8AdfOwMVOwzbUQVqS7mxRVWDDH+f4N778iyCWt5dK4WTccLJZc+XKo0gLowgKowjX1ndYX3sL3gPFKBXGsVDKhk9tvbk+4fSVE96fY7xiuXqQC8cdsbPduBeIFy9y4bNf4HhnRUDabGEsha47oKo5IVNRTnczU6oMQew4J46EhZBuU2HI2ZB0MTI4x3IY7IiSM8F7Ou+RfV5gM1ed5/rZG8Hk2aXiG/YttWAXm9nk8/xBDyz7ni5GtuPElAxKEqInOjgaOhYxMM0zqVQ6KguEuWbmWslig77gzRIuYoDVphu0bgLQ4G1TgXUlqShO7Od2Xok4YjXT1cdp334cM4H/BMsk3F9Pi8jXgVPgv1bVe+lj3587sDwkOscQAsuhR5zjdDNyZzuy8xZK4sRzsOjoguG9vLPQ0CtHS564coGz3cydzY7kMhs1Ku9mTGgVNruZKVn717KNTLqKQ8XZDju3GLRa2puhjQWl/T9qw73gg2UOsrcyNSMM0j64tXn3lRA8B4uBjSrbaWYzp/NsPynZpLLoubQzYG6+rErBWTvp9oPy2lx0ch4sYSvUuUEoDXYaxPOZJ55l2fWWhqS2Hnt0t2X98JOQZhsyaWVKnmvXtqw3r5LK+nwI6B2c3L7xKaomN2w3r7H9YChp7OiuvsGRETYxdJuC9zzx2Z82E1KmaedhLUccHXXE3vHQxQscHxxycnbK7bM122lklzy9mzjo4n58bx/y4MnF2IqqlaGPqArrKTGev9atL1TTqwQvVNc+oEWZa6XvwTX6rxfbPCTvCM7O7SrmgPFO8MEhxdtZvTkOa1WyYOSpWone8gia5rBpB9v7Rfc3QwtGzC2XgqY0jtIoz2pFQz+pwaCI/FcYz+F/bw+9AzyhqjdF5GeB/1NEvqSqHwywf3/uwKWHNXhH33UMMaDiOFwu2M2mD/AqLCks+whifm8nwsXVgieuXOK5Rx/k6smGV9+9zc3TDYdDTxeDDQK7GcQzzZmcLQ1Wm7jIwBxmcHEOcksowjmiBzCfu/07G3otYgCUcc72YXYmI43B471jnOwFkaowzpZ2lBKp8Q9zq+xKGzBKYw5iij8RB9VZC1etkOAE3e0MG+asCOIDPkBJxi8ULI7dqaMXcCURvA2uXBGOfeDg0sNIcESFnCt4z9NPKOvdQ9zeblCxFnbROb770tfZjCO1IblEDKN27d03Pj3tQ5qZ33ydG/f4q2lrJhndpzyjIF/g0ScPWC0dfVoT5yNcqchyQS+evN2xzbbbn9NsGQw+4LxDtekLipLwzCVxshuZG5hWWzutYhg4bdJjGyoLfTR9S8G6k1IKpYT2wTUthFPzS8xFiVVMLKSKaGVXlEmNiJTbrMHlTCcWaxfEmBIFK3rmJbD36z7pStrbubSStV/32h8/ugr8lYuAiPzH2MDwV1vuIKo6gXlsVfXPReRV4Fngn3/8V1NLia2Fs92uZQsIqeQW8V3ou4CijFNiO88EER65dMSVoxVVzM7pnLCeZkKDfOZakGKFZEqZPM9G/DHdaUuCLch55FNFxLLsLeTX4aOjZvuQISZBTU1tI9gZbZ8iVKoxC3MxQnAthd04tV26RU3nbN/P9ri6t5+AKLOapNl5O6LY422X7AUtJqHeR2dp9Wg14ZOII/pI5/ex6Z6Lq44748x2lxtB2ToFiaGtxTLLhUe7I0q35GCx4GjoCA4evnTM6TQzjplV3yEusJ4Sr7z4Z9Q8osCcTep65h23b74D6w/rED6pa3P64W0JfJ0fvGxn+1ekrXVDRJ56jscvPUg4vcM2GwAm+AMODx/kwSsW1FILTG1dmLZj8zyU9lxbld6PhuCuwae24eLxKjLmNlAW4xLmWqgmd7DzO9bRZYsXJjhPdUpN1k1OtTK1WQ7Q1oy2GrUPvVKy0ZBqozNpLU1oZNuEybVVrNqRU8VZ9PnHbJj/SkVARH4N+C+AX1HV7XsefwC4papFRJ7Bkom/98O+nuUAFNbbkVRT2xbYasQjLGMguM6is3NlmjOl7e5TLrz05jWunW04G+dGAM50atCu3Ty3qXo5l4oKdveQ9kHex0L7FgRhLAOjCJuIxvzpgg158r7yIm21ab/DlEvjAgT60DYdqNFzqnUMC+ft3dTAA/tN/L6lNQnrXuemjWjk8X1PpxWXTYlSSzLWjl78AAAgAElEQVQoR2sjynlHY2umLgYeuHABiSO5rNGaqdWOQSF0TDozl0zaWXBJDIHj5YpF59nudhwsj+gXMM6FRRcZc6XKlq98+aum5EuZbYKOyO0ucvXNV87l1E6N1LzeKjdv3Aaut2do/4J/UqCY8d4pZqcnnPmA1H1Gr8P5yxxfeoL1aWDw0abtteIuP8zx0WViTnjRc5FNFzwhOFIxhH11bU7ssBVcey2t9TdycW3iYdtiNXJUg67S9AVZDFEPBpTRKgTRNh+DpLb9UjV5epJKUiMsZ6yoWJ5l3c8jm8q1tveeORuTfvTM50dZEd4reOQ3gR74bbE3834V+MvAfysipr+Bf6Cq9yrZ77tUlTkncL5Nye3PUYTYm5TTCU1OihWJlHn79ilzqpxudpyOI2O2s1rsTISSc2WaC8vO6DdpzufDNruTynkoyL7Ev3ei7m1tYbtYKtEZiNMKhGsQzGIMu2CY51g9ocFDzs+BHmZNxsoflszJzpoiVihqay19azNTy6IvpbCKjuAseabWQkdhzjCmCTx03jdJ6l7B5ixEBcfZVCwCvfnUozeZrXcmcgntZ0SEoYsE75nmZHFWzhNU6KKwmWemlEyl1nWc7jJnyVgHEiKPxMBDTzxLfewZBEVKYVvgdFO49u4N4J1mA7eJzObsFic3r76/EIhQczJD2I/7KvlDR5iS3+b6O+9w4+r+VbfnwT3wKFcuPcxhKRS03Tge4NHHHuLSgx1zgrOSbWUYHD52xiuciqU/Ncy30hR+GDhFW7eqyaCuuVqeZajebM1tv7+/q2sTnVVtqDlMrgwGx52EZrCz9+k+sg9sVlAqpFpsBqXCXD56bfyjbAf+g3s8/L9+xL/9x8A//mFf815XLbnl1TnGUhqGyvabMXpCEGqy4A6H2YBPzraMYzqXX0ZvSUIO3wpAZs4N5tnaw32LpW0mYM+bw7WJrKo9KaZAMF9CLmpHFO9ahLRVdEsHNk9CcJ7Bi5WIRs7JxQIiCPbBHiQQnUe9/QxIowq377Vf9W1zNiNKLkQC3tnAUV21uSMwVYiuEHzPahUZ58xunkw/Hjp8sPY914KKI3SBmoWu61DniNnRB89cCpoKWpXtOFJyYrUYWA4LUqmk3cg4zzjNRCes58yt3cQMLMScdMMQTUdftDnzTPm3OBCeOHoAuEJqCG1VYbc54ejyI+ewVNNbOE5vvcPm7JY5NQGwQNQ87eAT0T7c9f23F5Xy7pu8++6bvPu+f3eZ3ekVbl+NqBrxaU4FDo85fPgzHGMQkegDXefQKiy6BV4C+MoiCjMFdfa+K60I1IJZmp1aCnI1F2HBblDnblWx19zO/WLTKb3bgc5qgTOosSqXIbCtmV2xuLpF6G0l+RHXfaEYNINFEzx4D+IpO4vbpk3EoxMIgVwsVGFP6HXOwiuD9yjCnKvFR2dLZZlSYXaJGAK+pbTun759EpATaUXCm4Ktubh2yXbE2OcYNDA0++jdRt6GbKV9mANCcc1ZqMpUkqnEsFiyk+3OUOLBtVQfaznnUnHeQiV21eTERWGsSmg+fNd6FlV7Ds7FQ86zGgJ937eiJ3RdNDNUmlkMDi2e7TihzniFXYxcOlyyHSdOtjYzOdmt0VpJUinOhm7jOOJqpvc277g9JWbRVphg0XV2XNBMDJ5ShLk2OJwYdTij53g4RRgOLvDowbENwPZFwAkXHniQcTxD1UQwAcfULdjcvEa5/g4lZ1xDlU+zcOd0C3xo5vwJXDe5ce0mN6594OFhyemtd7ndiokTA454H/ns57/C2Ip6yoVEAX+FYRUR5+mCvd9SsdCcwF1Bj3EFLQLOtBTBTFZivI2xVBbeDGxTGwpHZ3Mmby0uGWWiMoin9x7u9yKQ5pmrb3+f09jReYd6YdNf4MpiZenAIXKwHHAu4Ndb1uNkIZ7YB20ulbG0/X614YmibOdkbXJzX8UQiN6jai+Wazl/ANIIwLToDuD8zB32mX6YVMxjFVyxWYKKUpuizdo349gH74jqLH667ZjXyTqeC3HAOU9Sa4GnlNFcqVLJlDaEknaWNDKv1r1lVFk0qWnRymY7cbBacrQcmGfTKfi28BbXvO7FVHtzyuRSWQ49ly8sOVx2VDJXT2a2yWKr1mXm9rQ1ZLcXFtHi4W+NmV0yqXR0tgLzpVDnGW1DtOb7sXyFJmAp1Pfc1ezYYzpJm5ZLszOvjo5YXriAV0OC9c7RDwcsn/wc853bpFzwwUHJ3DypvPTKDap+t52BzaaMCtevJ2xR9Qlf45bd1R+w+8DDIo5pt7mrftnTo/3zPPaMJyDk0wVDjKxrYQpLHlke2LymdQpOzW24v9mo6YZa2rXYzEi1db6N9qxGcko1m7ZFxL5Gsdfio677ogiMuw3f/tafNDwV4B16+TEeO7rISmC5GFgOHV3XMxxcxIWlUVWcQ7zRdzfTSM6VPnQIsJvT3dWJCIuhQ9q5uo3iqG3vXNvwTwxe0M6u+ze62VT3xJNci3UibYW3146Ltg0CtGJUTbWFcQ4Qy4+z9GjbHGSEOWfmkm3wZq8oRJv2O7H/KuZOywVqnenFsOJpP1wUx7KPXD4YkKa4LDlzZ7cjj5XYtgFabaVpyrRKDMLhsOR0u2kI8pbSrNixIMKFIVIq3N5lNnOhqgmQalXUVXLK5JLbLtqoug7Op+qdmLqt0mjLYke+II5cbGV6fixQM+F0zhOdJ4hwoLCKHbujy5Z43PUmi+5HnusOyHqJXCuumKMUhcX3E1W/bV/PSSvqyltvfu9TWW+qVm5ef/sef7Nl2tm7JDRuRnaecOlhNgcXiM4EP1UDffcADz/wEMsVzMkAO14EcdUqgpopSBvUpVKMhVgLfXAsvWeXLQquSj1fcd7rui+KQK2Fsw+ufM5O2YZo1l7nWqsVODy+zOrwEillk2CGgdh9hgtXHuHoYqJz0QZlVVk6a/2jCJcPV2zmym67I3jYr3f2e5/SIsdQaf5za2eDGCBivw+umBjIid3ySq0kmrHDefMUlGLnxmKtrm9CqKM+MqVMyRlVO25MDaapgD+fN9j36lwL92ibi/08o2ArJu+EjKUN1VLwqiw7myFUOsaUyalSnbIa+pYqBCenaxMLjZlh4fH7bgO7O3fO03nh8iKw6gPXz0ZO50xVtbVWaW/KXBglt65IcNHOxcErvRZqlXO3m3hhiNGELMHTxUgp+W4OglrMeWwR3SJmu/XVEGi7lOm6jhB61ruJMY10S6XjghU0DMcuKF94Tii6BKD3jox1RodHl1nvdljUq5AQTtLE5tobsPs0sgfvkVgsgpyesGnDbxuWBrp4mdvXr7BcQinW8RIc4ZGneGR5mV4c4s3UppLb9suoQ4Ozm51vQydtycUfdd0XReCe1zwyzR82ud45u4P3PzhXTIg4nHuNowtXODjMTdBp9tPw+Rd47vKjLAS200QtptYytaAJMBA1VBsWVtmSAQhisZ9OrGVVzAtAsw9nbAi2nytJGxraTa0i4llE17YZZgHtWkDp3GAp2zlTqsE1XVMBJmqbQdjgrvPmdy9qFFoR22vP6prP3DYlN++cQpm5sOiJsaPvhzY8sqz7Rb8g1cp6s7Uz+6z84Podei+sp8m2BT5ALQzecdx7DmJgOxdubBK72Si6voJSoQhZHRsyY8tW8LWYWabURrWhMfqNgbhqvP+iipYEVYkiJohqv49JdUFrPQdsjNnyFyQrm+2OzTSj1QayXkFcsGlJG/gsF0plZf5958gt+OTxJ7/AeprJUpB2N71SM/mhz6CzbZdKLeAdN+eR9VuvfvLaB1V0HvkgPH/crdlu3zLF6P7zK4LceIfbwyE9doyyGdGzPPO5i3RLh5NgbEVrQ82Tg3zs1uX+LQIfcdWSqR9q6d5mGq9y8/oHHj65zp3FAd35dlhQPssXnn2exx4zmktten9BUbFVmqohn4M4QttE1CbVtNpTqVWMDiNCVFMijqUFjgTPQoLRkmplSomipmPfr/JqWzMuojP6TFtHuaZC6iQweG9aA7Xi4hxGoRFFq6c4RQp4KuOUuKkzmiKHiyXeGWprlybOpow40y/skrHvo/esp5nTUkEqi9gTXcd2nhmcrSYzlWtnEye7prQUwFU7Bomx7WqyrsiclZA6xQNBtA03W/hpqZRc8N6AAGm2jUlSoQuewdkRJ4iQPVQJ5JLZpcSYKkWFSWd2atPzPraCmA2kSjVRmGLGmlosCQo14ZdJscF1oZ2TbTaxkA6/XDU5cAUq4hzPeEd57EnOzk4Zs8XRl6q89nJi3P4L+MAk4Me/2iymYP3gdf0trp8P+fZbjetszgYMTWBHxKKK+8xneeyhJ3mwVo767iO/0792ReDel2m+ywefs1vXuSEfrAzvcufkmwwLd95NqAKXHuaxn/klvljKeUKuF8HT4zp5jxDHioT92eNFKcUxl8ysyoC10t4JWmtjylunUKuynWfmWgkOYohEZzITEHywUFMVwatRaLW55aRaEGiMkdw6CVOXtoFYEbZTperMpJ6tOqZi0+RaYTclYrDjS/TCQR/YTpa118VoUWpOWcZA78wGe7ot3N621F9oElX7kFdau45Jbs+PV7WiOGbH+Vlf2267aLUZgXeoU1yxgFAwbHauhSGagxR17OaZ3Wi5BL13SK3MJdH7YFFrKDv0PGo+F8uSzNmgHipNNqsWKlJqMYm3OJP/7uc4apagoyHaz5orq9ghFx4gDheYSpsDKQzLwjgf0XXKYhFN+SeOb33jD1mf3QHu8hoFIaXpx+vBqLWJkN573eD2Ldnrz2gvFXJ6m/Wrf8n3sY7oo677IobsJx5IChAi3eqQRWv9tQkwYvxb/PzPSwNgWjEo4giHl7gQgoEkMB5CLvYBG4Ij9jZQm6fU5g2mANwmI8EGsemvc45U7W7pxIFmm+pX8EFw6pFGnPXOVqGpeSBKA08gnP8cEiD6xi9o6sH9FLmiTKnQO1sp5arErqNUZdxtTYqaZ5bR0Fe314mTObNL2fQO7O1XmF7eC1ECThuxuCpdsG3ELDac9I3r70VYes8QG/rKu3MZtIpj1waMy6Fn6AKIYzfOjGOy1WsMNrgsia6tjAVhbGKawQUcLdCkWILPJAbb6II305fW8+wEaiWLdYe0Qhuca8cEW1FmVebaEoux7c5UbPgWY2A5WCQ7VE7PNoxzoffQdcEMYSHy6ovf4N13XmevHFSatTtNMH/qCVr3jCG7L4pACFEvXHyQFAJnZyew+TR2vz/qdcBy+YH1Sj+weP6r/NTyiKiZPRPPx44Lx1c4WK2src2VKWdKzQY0KYWkbVXZNKfi7wIoBfN8SvvQ2IdbcMF8E7mYnkLF3qBd8PbhzwXXoJOhD1w+OkKbYoxsfocpJ1SwBJxS7FyuhgffQ1iTVMbdmlVQ7oxG8y3tTL7LbXXabjcOk0EHiQTd5/FVeu84GnpcFxnnhGCW5tr8Fr1zLLwnxsBYLGAkOI9UmHPGB4PMJs1MKVPnamvirjM9RpkJqnTNTVnF7MWq9mHck/qdVkYqg7fjVqmF6EPzd5RG6rEOLGWbnYS9yw9AlPHck+HRYmGsei7LbcUtWHBKKTbFjxFibJQqhXk7m2FLa3OfKlsR3rzxNu7NV2yN7G0DNI3KyckG+MRizu/fLMJhdcQv/OK/Qz085JW3X6e+/rK94VRJpTKNyo1rp/ABhu6nc63Zbj/w0HbN7p//Hn/UYKf7MrpYrHj08We4cHTZrL+1UGpg6B7jkQcv4PtCyYkgAQm2SchqtJxSCk5sQGhWU0/vHbW0AWj7TqUNHTtnZ9q93Lm2fXLwHZcOL7BaDeRcWG8n3r55k92UmpS5MqdkXY4zG/XQdRwdLNnlCZ3Nj7Ees0WtIyRstbg/9ppC0jQKqc5kMbWa1kp1juXQ88jli6znkXmambNye722jYtawSlZzbgjdvde+MiiRaqnXDibZ3IpdNiHPdsBn6pisedS6dWdd0iipqFownKywOA83gnjbFizKmIuTiDiDOPd1pcBa5ldm/1UrJHySPOPCL3zzNSm2TdVpyVRN15AECTY0c+rkGrBdx5peYaL2BnKrFaef+gpwsNPc7zsOFr2lAqnNzOvvXqVXF60rYla0T87u8X69NYn5Le4T4qAF8ey6zgEHnj0KaaHn0DUUals55mTs0L/vVso3zVbpbdz9/rsJm+++dpP4Ce+90R3mkbOzk4QdxeLbfCQJ7j68GWGpdFzfVN3xed/hs8NK5y3QI+kShBlzhlB6fHkomjxSIs5R/YqMpBqwQx79R7FxEB3dhPL5UDwHqGymUbu7Lb25/Zzhb3YpJlqUs7k2Sb267mwqy01qAmyquxPubaREae44phLoRpJAVEsi09N9BS8IzmHc8UQX9gxp6hRiUVhIYE9Xlyc0aK2aW7++kIO5rkv1WTLToQs0ug5haW3aE9xEMU3slNl0gaG2W9w2vMWXevAnDCmhJRK762o7l8y5wS84GqBYkcGJzbHmFOxooj5NhbBGIW5dXBzsuyBrq05xdtRwzdPb5eFXKwoBOeoAuvJWALHx46//guPMacHuTNndkWpznHr1rvcuv6WEarVimLnPSe33uXk9nVq/Whz0I9y3RdFwDmhj9Fa1HZOq5pbJJWwWgqf/9JltF4itCeg8540bXj6mS+ayrANnhThjR+8yhtvvErOHx248MlcamKUD7wmOb/K91//sJlSbl/nZtcbSKQY0kzkyzz/whMcXjJFYuc86mJbXxazTeNA7UMi7Uwq7W5Vcubk9JQhmgT71ukp22kkiCOKFdY9v37vm9ilmVwS1MKUK2dzAWx1lwzdg9bmmvNWCOyn9ecWbAMrmPjqbJxwt0+J5/OLQhcDHWpRc84Y+q7aStBWe5U5WVHqvGMIA1NKbHNFpdKJwUYdQnSBSjYZbdNxaDUxkum6AuTEWBU89KHht5GWFaBIo0evut4w4QJjLhZiKnd1E63qmbS8WgpTaR1HLUp2NkCu1TgXpSpJoDgr9rXNn93+Z2udiPM210i1spm3SDWobVLPWJRZlOpgroXl0SUWR8fmNeCuT+bK6Qknp9eprTj0zpFC4JXb16mvf+c9u8WPv+6LIgCAQm5osX3rVlKzEg89VStB7G6Ssw2RQr/giaef53DoqVXZZTPvHBw9yMOfeRbUVmGOyon3vPjWa0yvfot7+00/yaveG+/09mu89aEHb7A5PTBKTXvDIA594nm++FO/wFNaoIpZRBuVhmw+iFkrGUfdbFv4ZcdutDOpaRE84gNHq4EQB07unJFzMsCIFIIo27myy+UcplKrQTJsLNiOH2LTflfL+STciAqGRMu1Mk8ZDSa57rpg+/dSiZ1nqsqumEbCuP/NSOU9MUR6R4syD7h5ZpdNDBMRYyZEx5QdoeHWpgqda8NPLabQbL568AQC2lr/qdaWF6gs+0jfG4AmBE9MM7eS5RJKzXg1yErLSiUEWLhAqIpIsORhzXg1f4RihCmVatbxPZKswUhxe2qw+WSi2ONmLzbYyOiVs1IYtZ7rLMDMRqVqO67Yiro/vMADqwMAtJhgbTUMPPz4E6TPPMWYTB3beY8X4Y/+4J/c8915XxQBVWWbRrLaOW25TwxygDgG7ylF6aIjxo5piuzGHbucuLPesN3szIgTA0Pfc3ThmDCskIZiDggPBOHxSw/Bs1+BlNjMM9VDmSJ/+Ps3qPW3ON+7fnq/+D2+423u3L794X974yq3X/pz/py96BmEC1y58nf55V+s5NTgkl7QmtlutzjMKDR0A9tpssOJguaCk8kiMFQpmumcspstgaio2ipKjSaEyLlhy9SMNNCqdR/BCZ04vDr7BrWay84LRwcrFj6y2W5IZcQ50154dVQxcjIihBBZDQsEZZ4mlGoZitIzo4w5E1xHHwAtLZbdnoshmtou5wxiHeGszVWnweThtZDUAl8635SM1dDqXRe4fHTIZpo5PduafVhNJLaPUHdA8J5BPMEZLVqDEFoBGktlm2zGQcEgoWpisdBWvEUrXgKLzhGdFcuieq5BUGcswVJr42CatbzBrGls7TbBMEkwDRUnwOAjhwE61+MeeZpKZR5nYvAf+86+j4qAUqW0nWq14IRgIIapZrOiFtiVmWm2eK+qimYzzMw146tBI3vvGKJv02Rr0X2FIXi6gyNqrjBNxgYcHL/8N1fE8Bssu2ix6NXa3d/+rX/INO5FIfbuF4H8KSO9Acgz050bH5hD3OL09H/mzTf2bxK75MrDPPnLf5tfX/YsWuu9LjPFOVZdzzhXsx2rMerqXjLtKzW4Frpq0muTQ9IGta07VjuHq4gNKJ2jd02B6YRSHUMnLPoO5z0qLSQ0RJuqS2XwHg1WWvY27xhM5FNbhLooROCws4COIAby3Ov/zWPgzvMlNQRytmFmVgUJRmmqUKVxElACwoFAxKLtTsrOMGXVPlQOY/3tU5il2rzAzuIWXJKzNj5ERICxVIZQ2aUMakevUTMHwZERTueZoM5mI0XpxRsjUW2dGX1boQbPWCtjUjNjUc9fV4+RnAy7j0W9eXsOvSoH3hSZ45jxES4PkV10bHIi67+Ci/Ajcgf+G+A/w5AxAP+lqv6T9ne/Cfyn2Mn4P1fV3/ph3wPaL2u+FrvniK1YclYKiaRCGvO57dgFZ1HOYNQVF5hL5mS9Po+dcvuZurOsekuSVIa2r67Fqu3hwuODp/OBGCKSzYf/a3/7PyJgyHDF42MgU/nzP/0dbt+4axBRzD6b5x18qtmDiurEh7Im33md7/6j/4X/6QMPP/7407zwxZ+liwdmb3aQ1dOFBeEo4Fy2WYvHIrxLC7yQYPFwbm+iNiBrKZkozuK7vLBof7d1zopvzozbkewNhlkRkEpumQ1911mw6Dhztk2cbHcMTuhDIMaAqIV8Bty5AUq94F3Etw+bOKGmwqx3h3DjeUFs7bZzVPUUkkmYnWPRB3COkjK1JNabMzM0VdMtaLu7irf3mcfSgKLYdiRpIjgYnLN0pWSr3+NFz5iFUsy3EZoPIxffpihC7ANdHymTsNvtKAJLH8znoHc3GFaznA1/ZT+cNeHUIoZzx6FSG3vD5NydF8ac2RVH0rsI/I+6/qq5AwD/o6r+D+99QES+CPz7wJeAR4HfEZFnVT+GbdReLIdYakojpIxzaq+kAHbO25v5xDW1HpgYRoTeCSkH0h4W2qhBVc12WbWcgxglBpY+2AqvVBCPC6ClAE2GiiA0Hh1Kv+hRZ5Sdr/21X6P3Jk6pCEXge+OGV1/+Brz1ffY/tSpstoVxvAMf2iV8wtc9jhpvvPE93njjgwPKIx584Of48pcukl3mzm4+J/rq8UNcdI7jfkEXA1Uz82zSZ+c8o9pGw7XBl/NiIpzZOrree3CVsmcwFhPp7BWIq25J9N64jeOEJjvSVRGy83Tt5pVSptNK6CKEwHaa7FhRFdTiw5xWpNqKsVQznKVq2XyqxmEM4prASRizob3RSuet2OyKtf7eWWeCKlorwRkMpgpsa4GxEqPnrFZ8hYWz7IBcoeaCNKiNQ1EHi25gFTrWybwtB4tgFKcMQywMznPYdUQxM51XCGrkJhebC7NYIndVxXvH4AOlVM6yOTgPnRGKokBnsViMc2Gsel5AP+r6K+UOfMz17wL/sAFHXxORV4CfB/7oY78HaisWJyxCYAjRKDbTTMGmr6GtWVSFOWWgMgRvaTB2ECQ6R98bP97ce0KpQspzg3fYGa1kW61JO/u5JmGtOLNlVhOOVFWmyfICRCtdhcX+bigGjMztjvqUizz9/NdwX/pFS8kVYS7wyqtr3nrrmyjv3J04I1y79v9z92Yxl2XXfd9v7eGce+831dxV3dUju5ukKFqUKDmMJcVCBsQCAgQ2AscBgsBPiYHkLa95iuFH5ylAHoK80o4TQVYSO0AsB05E2ZYoyaIoNptNNnuurvGb773nnD2sPKx9b3VXD5RJdrvjDTRZdesb7nDO3mv91394i+lDBFKf/jrl7r1/zD/6Jx/8F//lX+EFJ5x2vQlxcmIqz/D4zZ6dWYciZFfpuo6OgvOOYUhtZOgIXvBOKZO1TyIbz2azNU8ptYvTPitrBKspBGsl9JExJ2oqdAI1mb16UMcsQifCoDCMk4VuYv574NupaXoFkzG3GO9aGUXaSNFusKEY7Tw1HwSq0olvAia1qQd2ovpGe/YCCzEex6pKA/Ac61JRTMbeOUfvA5ZLIvQSrYpImdXSPvv9vsM7T4wdWgpOYdH1RK/M+p6uc9ScrdoplVRMnVpr682sVwMVVKwiLc3RaqhGiJL3ipA+ZP0kmMB/JSL/GeYk/F+r6hHwBBZGsllvt8c+sN6bOzCb725LHaEFJTSba9Gy5d1ntH0YilPz01N5OJOvaqRWL0KlMexQM2JscmTnXKsQYEwVdY7gSnMhbtRdMa/4s1FZ1oyXwI4zdphvCce+nQwgjHkiFyvZgrfAEm0o+c2n4Ymnfg70q1uloPOO11770618ulbDLvDCO2+9wrD6NGStP3qVb3+Dlz/w6Bc4O9sl9uZLEGNg1kfqY0/yuRtPc+AVxwoJntk8sNM7OtexmM1YT4kHp6cME0hw5KxmKyeORYzNf1VaMrRYDVvUAjUDeBe3gqAoiveBWM3RZ6zKqJVUrT5zakk+RTZNgWUiKjCPBvChViEOTco8c4FFCA0aUIsHRxnJeHy7NiB2jrkIinkpFqnNPSqbL6U2Wrj3JlZSQ/U7gVQrp8NAKdXo3QWmMqJ5wiBsrDJxnlQSdTCuw+b1eO/oHGibxCxUoWR6UYL3DFNFxViMCzGNy5ByA0U/fP24m8D/APxNe7n8TeBvYyEkf+b13tyBS5cf04PdhenUi7EEzWfOxjiLvsepWHhnNdfgmgvOe6N0VhvJeOShg0o1LFUEvAuoV7agQ7WLTbzgvbcgCSnbSDEQJhSk0kezLj+fRnY0shONF55UyWrVS1FDeVp/Ww4AACAASURBVDvvCOKNhKNWJUzFfAG9KNXZWM8Vx/Of+7Kp4NSCVFObh1+9cpNxdWJOt97jFe6GjlfffhXeefVD3kf4dKcaL/PGB58Gcuka9648xn51TCWh1TGfPc6XXvxV/tJfuMDNCwfcX654+W3H/ZPTZmhqisCk5hIVxBuLsH2WxRlt13ulOCMuUSqz6A3j0UwQx6KL5DHhKzhv76nzYnHdpaDiKepINW0tujtxZCChrToxx6mNqcpUzTpcnBnJRJTOR5IKrsKKSkqF4D3z3uOS6UQqRoLKVEqyKkelGeUW5ShNDMkqo6xCEqXWTFKYeaXznpKVrJllLi2boqUONUOWoqZ0nMWOPYmU7BvZyrGYB7v2fWDRe07TxJir2fZ9xPqxNgFV3fowisj/CPwf7a/vAE++50tvtsc+dvUx8OylC0yT2SJNU2KcjLYavWcWIot+hmrlZDWQkhFBRCtUs7QSDLAxf3dLYxWx3Pra0oujmFfbVBRtlksOpVZHrubgZ9Sb0kI3DPGegNV6YpoKstvTx27LRstq5ZeN7JXWINvPVyPsWFRUbVbi1mZMaWpCllZyqin8rl19kj48g29gG+J4IkRuPv40evhlw0KcQ2rh9KzyzT+8A/yefRbtf2zC1wCQT2np4V1uH959H7Fb5PvceueHfO+VwCwEplI4W0/Uazd5+vkv87nG+weH93bwr1NmLJlOAvPeTEKcszK8VPPcz038471VDZ1rKjnNgE025vPecivXaxTHrPMs3cDZeiCK3yLvG+KPd/Z3M+2E6Ky/tjPFUVE7mRGmTKsmlV1xNj1oRJCxFIZsTj6LEFEKuZrj0pAyq2wbV8SIW1MxAHLuPIsuICKsc0bUxq5FbIQ4lMx5SvQhmLuV90gtlqdRipmMqLDf93RdR+w2+otCdGLK149YP27uwA1V3Zi4/WXgT9uf/zfg6yLy32HA4AvAo5GUH/x5CBfmM2pnfoFrcZyJMK7NdWc1njOfDWhxDDkRxJBaKS3gASsJs1a0GAJLQ1NtGR6gwXbRzgt0gfXSMIfN7RLE2ItabRToMGNTwdPFzHrK3DmrXJhVdrtop5U6OjzVmSV0VhP9lDbX9d4TfSN76CZ9qBlvCsQuoKXiq0clczaOnA2V/XmP9tEyDqc1N/pd3M0LJioSIUihZMfVq0+CPmfOOXsLLuzt8OCB57f+z1dI6R9au9hm+9r0DJ/WUh05Pf8Bf/q9R/7h1tvc/t63+bbIe7Yp5XPPfpkbN5+ni50p9pLFvMcgUMxUM1cbT3bRGy9BBClKL5DFNdZpxdWC00iInv2dXaJWdErUPtomjOE8vZjTUa2W/BOcsCOe3ks7JKCbdayrcrha26Giiiewu5jRIyyHgfNcKNUs6GsDuoN3HOwsEPGknBlrxiUzrPEIGwP7IjQeuG85A7ATA6FhZZYt6Ehihrk5W97F6XpgXQq1KD2OLjjIypX9wLwLPDhZMow2ERk+5nP/cXMHfk1EvoLVoa8D/4V96PodEfl7wEtYPNl/+aMnA+YCFLtIHzrW44gPjuGsUs6FdRpZTwUZhSieWTDjUY9QxdJeS6kksR3dtPsNW9CKVCF4K+uGlKkq7C8iPgSSL0xkfDXEtW9TAYsKs17O+0hwnpALVEdRm8VqqS0rABadyYFTI3OkYtuKCNbWeI8LgVoKzinUxsH3LXbMC85VYvE4l6lFGj3X0oSKKplCKNZwmo24zeQPLngcV+m8Y293h/29OVcuev7zv77HlF7kfMycnC9xIty5/SYvf/cPWa4eelypmIqx5unPTDP9iVcaGdP4gXnJt779u/zpS7/XJkQ04dTTPP/813j2GWFdrHoKMeI1MPZzeiyjQquyCB7YMBAdY57auE8ZhgmH0dOnakCxE9h11lIWEdbZTlaHCYy0KslZL77TG1fhdByhKH2MXNqZ0alwa1obHuUgaMOKNpMstVb1fBoYm5W+COb5V+0ANBuwynqaWoSYMJVq4HUTVNVSmsDJ7PGJAc0Fam7xdMYYTSlTz5X7qzWnaULV+DEx/ASVwL9M7kD7+r8F/K0f9XPfu5wTPv/MDU7ORt74wR3OVgPJgmVbXJY0i6rAvPnUlWyUSEVQZ37s0lBgWl8uFWhZf07Zml8ux4mQpjZ9NP52cNYWVKyUDp6mXTB+VvSRbhap4syWO1ULRfUbVNZsxn17w2utrUQ01qKBuL6ZfdoGUVTbkBFACV6YRQ/BlGdUc/1BnWEKNKZeo1hbGIsRTkqB6bRyOqw5mPfs7faUssciF4LzlFq5+oWf5Qsv/AypFKL3BHHcD54/vvMWR//in1FOj1oqb+O054KulzyKOXxSOET5kJAQeJnvfOcVXnrpkd+3d4Hrv/rr/GqpTNh72Tugv87evudsGPEKM1FYn5MmBbHW4eL+AbUKy+XKeAjB4UJgd+E4Xg+MY8KjeGcag6lUFmQuRAsRXdbEmBJVzOHI4ZhFYd4FijqmaSLU3HCAigu+VYUBKaaHMfDRqopeBLznvNgkykxYjQOQ1JSjTi0Xo/OVIooLsVnlt/SjBqyvq7EElREcBBdJm3Sij1ifCcYgCEPxpOI4WU/cPxvMElxAxRt0ow3lVYxK5pqQpY3zjPDVbrZNj91CGFUhTZmi2kgVvplt2M+I4eGISsUYaELj5QtIc+8JLpDVnG7wRiipqM11oSXJmPHFNgwS04rb/Np4BzGawiyPmUIl2M6FEwsVZZMxoAUvYZtyJCLNOtwujCCyDVTRokiZzJijC+zPO/rQsZoKZ6sRzcl48jmTsqPvO/oYWZTCs8+9yPTU57h3fEjvYT867q0n/p/7R6Rv/3Pc1rLJXtP9+8rDbvDh+uRajfrBIuX0iHf/wdf5e488LPKX+fV/x3MyjkQHs8ZoLHuX2OsX7MaO/dkcXwq6hho7Zn0kp8o0JlwxHCZhbcK8i4bjlGwK0NZaLkthmBJzLIq8ZxMaamf5Tt8bl8Fb9kAtNjatauDze8f2ozertdymW1FswpUxYVlu4OCkylAS3nt6V0klUze/15gpqEiLHHMEFUrNqHjiJzQi/Kmut995l+WQ6GNkfz5nzAkQQhRytRTi4APeeYpafPjmRiv1IRq/4YZ3vrnXAErdos6CeeJtzBfNAttEsqZGax2deDuxt8o1b/FguVLFkpBqYzSGVn0E2NqYixivPrYbXFvpZ6wIZwrAVsHUdkWIQBeNR16K4oOJUVTqlrJrm4G9Xl8LpWUMWjCqo4sBrXB8umZ/YVXJouuYxon1ejDtQHv+u4uOIVWOzlcmFFLhdD3hq6OvlV/a2cP9yn9A52OrZhxB4JvfTIzTP24Ub5vCoMKde2+T07TFPTYFQ635U4s5V/1N/uFvP/JgiHTPf4kvXrzKBee4P+/QoqymngtXb+BiZZ1GSkpEHLGLOIG5g87BoEKq9llXKqlpI45Ol3SLiV0vjBVSLkSBWbCE4lnzQRxSpveeIbdkKmhJz43t2sRCivEoRs1otQ0jeCF639KxlCGVpuzMFLVQU98qt4phGqV6y3Zo8uhazbH7o9ZnYhMwim8h58zVnV0OFoVbh6eUUrmw2zOlrgWBWJ/mlGaZtWkcDRnO1JbS27KAnJX3otDFgI+yvXnTxkYKS5ClWh++sRP3YlWBbPwCVdDS9PXVAjVMm+8o3j5soeLMjRPVh4CjlxZdXt3WG76WJp5pZbVzTce+0ewGi7oy4wozzIhejV+vdgFRMzklVCJd783qSoTlOHE+DDxWhMevXWJ/Pufw5JxxGs2wxG8o0xUf7LTwAk8/doUHx8eM64FeHIFKzhkXAp3FNIMqX/sa1Pzvk6aCFjuDiiqvvfEy03RO7HsrlxVElfv33+X05AGbV6uqZFWTen/AGPITWDkxvfzHfOv9Vx0xXuXatSe5dMncebVWuhCZXX+Kpy9fYV5N07IqiiuW9LwqMGRzS+oQojZjU1F87EhTseOkgX1FwXlPD2iZKGLta8BAM8uCtCovNt+G1MRGrgHVlizkGlvQuBPaxHEbm/oYPKilVBs0uXF/sHddt3/+4PpMbAJVlePzkaKV/VlH32ifADf3FswWM1B4cDpw68HZ9gXWRp5QNSuvWF2Lz7JUXFXTd0dnxp0b743axjOl1sYJj01eB9Tm6y+G3qq0cqyWrTll2bzBWvGuEVva92yYhqkdgzllxCveGTAj7fU6EZw3gFGwLMNNsJl4j1NH0YmSq8Woi31fqGbEqZoZq2/BEjbLFucsbKJm0Mrh2RlVbH6N2ms108/K6WpgNSUkRFKtzLrApZ0FOVXeXo04rfTeTjfvHbO+s1SbYsnQ1IzmxDQOjXXpeP7ZLxJmPaHrSDnZxiWOs9P7LM/uG8+/KMOUeXccuH33LTi8+55wImEcIeclHzBl+KmvQkq3eeed27zzyBBb3n2Ddy9cZh9aKKmCKi88+wXcfI8pZVSVqXqWOmOni3Te0Xlhmvfmrdg4I8tiNmlamyOz902Z2IJGtmxGq+bslnY4Z5WusWmbOrG1W8H5Zq1m5aHB2W5LHgwt/qyZUuHa1OKj1mdjEyiVk+Vg1tDNUcbTUlrEsYgBL3DqDTX1GLXTOWmGInaSq3eQKyLa8IR2Q0Pjgdul5Rx4Wqpre6ul7bSbpa2UL9WZrFPtzQ2uo6bS2gcTp2yTxtUmErY5mLil1GSOsy6gLkDr98B46WwsyqrdnDGAl4BWiyuTzS4u1sqknFrenNFUETPRHEshlsLMR1y7sMZh5P6UrM3ZnFjAVDKjKkHN2rwqrJMl2F67uM/ZOLA8PsI5h0phKvafimzDQsV7sptYO3vdu31PvzPndDXANBL6SBonJHRcuXqdJx6/STDdMOtx5Now8ORjN6lnJ81s1IRDr79eOTz+DnC03RycOFarU9Kn5AOhh3e5dXiXRzOEjk4e0HVzU/+hOBWCe4qf+dwzXLuknC9NqDRRCbM588We5V+AnfCNyVqqTZDMBF2pxZEbuidtM9gMSDZtINo2Auvl7BqnfW1jOG7C0MUKWrsvWgFQP+sJRKU65n1EtXC2TqzSmqrC3HvGXDldjky5cu90aaWrc1u/tVqaxZY3nYBNADy+JbpE18gmTs3cwd4xovcmna3aAK0K2I3L5g3GQkloF6l4R+c8lKmNhFyTytp7rW2jsfl/ZGdnzpQmjk/OzOpbaGES9oFuPhdpr6cqpCJ4yagaZhC9MGYxFV5w5KKkMTdswyTAVc1SqxZlJvacBOOrO285hKmh1U68uRsHz2I2w3thNZhKc5UKz106IHGNl07OcJKJwPHZOcM0EkOHp3KwuwfANKzIjZnnukjXdbjBxlyhm7FeT2YwMiXWw2Aj1+CpJTMrhRs7Fyg7FxvXo01/dgrrYQ9xkxmRqKPzkdde/1OOjky0Km1TDD6wXJ1xfvaJGXO+bx0dP/iQRw85PXuFeb+58ew+5cJVHvv8L/BcGbfxYuvqGNlnsWsTmIiJ4KZqLNnqWgUqNh2qWltvb05QWatpBBCcM0EUokxqAKI4u24t59EwGK3GfxH5jGMCJycj79x6HS2Jsylxuh5APbv9TYvIriPjVFhPuaXUCLW67ekp1lxvvfCCGDDimyOt92YqMVX7dx9s7GclesU1g4zqatuFtf28YidfLVSBMSnOBduJtTnTti27gm3BzoA/VaWmBDljZ7hduM4Z4OOq+fw3/UyrIKwCqdgG0LvAoo+U9cT5amBMLZa6SgOMKhWbZmj1ZuzRvBRQGGtpG0thKOYD2Ad7brO+4/LBHiUlSsoUdazGzO2jM8YykdS47vMonE2JMSkpK4tgTk+lKJGARKEPkXnnCSi788jJOjOtR+PTNzywtAwE5wQvdZuqVBsF1qky5ZF+DrPFBRyQSkbVXIZeePHnyTk3joBDomfRz3jn1mu88frLpstwdk2MWrkzjtDyAFuR1tZPe7Q5sFoPrB5NJT094uzO27zVunEDqCOx/0Wee9GelIWtOJJ4yt4F5mJ+CiLNoajVgbVKA7/thFGxaDeVYkGlbfTtMF3EPERQw5SmpiH+uCHhZ2ITyOWQ//V//7rdBLQyhkDX/SJnywt0EXIurFNCfWR+8RoXugVdbYGjTqhqdNKKMcbEmae/yEae3Dput/EwNOzAeeu3Kdr6MhvPTzVhvZmZU4rAmCYmqYQQ7Qe156pK6+02XRykVDjP1jt68UiwmC27WGUbHgKyrSBKgegtitrIRsKYlNUwsEwJV62FcSLMCeYejG5HhYYriIGnTtFiN7cRLixwQ1urJCWTpwnBaKkpVc7Ozzg5fkCumegyXXT4al6DpTaU2gsPTk5N0ZYKXScsusjVg10u7+9xvh5ZvXOH1XJARBlromhuUt6GizSzkqwGbl3c28E5x/FqZe85NtLdKOVSyYjvmPmero9EiTZ7l8LNJ5/jmadfMBcq76mpcJYzf7I8o37n97dzdBOMFe7eGdD6SKHfxrc/1aVKHZYfSCwehn/Ct//I/ry9LWc7zJ56kRtdDxtlq3T0/WV29haEFhpTxRyNNgnaKtKmZubvCMpOF5mFSK4W55aKMjRLuo9an4lNAJSUHnXryazX3+Abv/vIw92M2ePPcHP/Eou6mQ9EXLjC7sUDZvPG0Gu+bTV4OiJo07u33rpUoDak3FVKldbzG/3YYrbbDSZCbKBgKpnqnEVkV/Pxb3YZlLZD08QcVik4otPteVCrAXnmh2fyVsMvaGNPKxVVYZwSZ8M56ymhGwGNmIWYiG8Bqtb/16qmuFRv76Wzk7FWGwd2TkhaGdKEE6Uk5ejkqH1vSxdWI8hEhwmnVDkbLTF5Fjsu787BwelqYD1lC2mtjnnsuHphj5uPXeLofOT20SFTsmi3MU3m2oP1sd57Sq6M2UZdfWfhpAc7CxBYDskixFrfvZk81KoNIwF1FS9GgBI1WXJpjDtt8XF/fv+A9G/8e/TeTtVcldPlxA++f4LX70Hrz4MIeRy4ffcWwzS0+b2BcrUZp/x0rb4LH9hvlqcsv/sH/OB9D86ZLW5y+eoB3czCXVUUcYF4+ToX5jPTHzg7ZDrf5Mhijs1DzpzlTKqZVJUgH32rf0Y2gX+JNQ0Mr7/8yBvmCeE6l65cZmfXxjdBvE0ObzzJkzsXWIhFdosqMKfve0KwlN2iggtuayjpfaD35lmnQCo2T/Di6NxGO27l2ZAzpRql0zvjJSDmf+DaDBda2SuYhwFY1aONPmo/zgxKVNFsXnhTsditzjuCt3GSilFfo3g8lXkzvxi1GC2pKRgNJQ6tnDQKqtNKaCKplBNTSlYVtM1FsBuLRkaaUgsdqUZi2p1FUqkmYlExFyVp7Vkp3D464d7JkqkU+i5YWAemvZiHNv0oZjeOWJlfivLg+JRhGMm1MKXMlIsh4m3EiCoehw8mnrLPt4Jai7OIkWUxXMTGfDb+9U1mviHmzDrh81/cYyd8DXGeqRmf+DRw6/ZbHC+XJt7BsgfPzs+4/+A2aev7YNXkWAs6rj9hmvWaYfV93nnjkYddIF57grP5gritO62tePzp5xnHnk4dk1bO8kQpM/pZ3BSuH7r+/7cJfOgq5PwOd29/iGDxwR1OFnvMtsCIgj7FzRtX2NkzQwzjvFRkNmdv/yK7uIbEKtOm2qgG2DjX0NlWzAUvCJY130VvvVs1TwHXNopajYTU8HxES0N9zQCjVEN1fWgtBtaqFFX6GMyYQsQsu6Q2JpoQJTLzzsrlZoaJmsOvNo9+QwgtUr3zVvqvcuV8Mt9GabNobbyDja1gaIYb2tRsi/kcdZ7VMDJME13Xs+g9Y1FKzdw/WzE8OOXdo1PGNOJdsMSdWsE7YvBIsRQjHzyhNgOMqky5UIehTT1aOyMtEUgt40CcpwsmqhHV9npN89HFwEgx844uEL3FiW0nNq0SDsFTkjn+umppzikVdvuOp59+kSeqsk6T5R2ocLJcsnv5BjmPoNZGZifcmVakW28g2SjOm+rt+Bjg/k/74n7/qpl0+w3ufMg/rVan79HV2NhY9SZXH79IP/tosta/JpvAx6x773KbRymudzk+3qWf2WkEDYe4cJUbTz3P9Vob4lqo6um7p3j8am1EIqswRIXgFCextRMtT9A18BG3tULbpB9TW2pui/LZhIbUqoiLRGdiKmntQPSeRbRe3ykksUzD2rwSdmaBPgTOJ4dXhUZAkvZfbRTWGB2uWj85FeU0FdapPByftue5YTc5NXGWSIXqmXeBixf2cSKcHh6ymiYuxJ5ZCGaokQun52tEhFnvWY6VVEbTSWBGr6sEshnKimwJX5s4NN+2RBFBgrlD1YJJjdtNbKSyZtGt1r5lhfMhoars+ICPATDHn6L6ELBVWi6hJSY38ImhJFLNzL1jETwzKstaGXKFGLlw7Ql7LtXYg+ICizyQ9i7hcm1jPWXmA2++5inlW4BuWnTEOe7df/dTGW/eu/3mhzx6xHq5IMSP/r5//TeBD11HHJ8cwaPR84d3Ob73Dm9saZwGUM5mS9afM693UbtxS4j0N57lhgvE5nlvjD8rv60ktzLNBTW2Ya3Uav3sZm4vTtBiox6l4QM0rznfzCJUGxfJNp6NVHUWnNmrNXed0ibPpRlfgIVpeh/s/xuiHJ0wGqSxrWh1Uw2VhiVoQ6PVMXPCMA5MGYYxkVU5PV/hckecdUxFIWdms45531laUsltpG2kqnFKqK/gBK9Cjxl+OgddMKKNtVnCLIY2RrWNt2HipqhsKLkXQ8lVlTRNliDtnXnzeKiyAV6MmivOuhfXXKO8wFQ8Q6mMtYIqM2fjtE2kmxfbcEqznLPPUzgQR7rwGKKNrCrKXAJf3ltAmZuwSECrMqgQX/se03AOYqIgmuJ1dXLI8vyTHm8ec37y8b/jM7EJdP2cm09+wbj8Trh7+03Ozg4//ScyDawf3P4Aoiuy5Pxs08rasaRdz+zoHsfepKtWewrs7rN77Umuxpmp2hoTMIhQC9Q2zkRbdLYV/9ScUW+noW7Gj21Uoe1rwbQJ9qscq8lizNfTZPp5rGxu2kJqVpwHH4WCpTTNvePKLBAcrIqlDeeyGT81kClEqlZyVpxXshZu3b/PlI3gJK3UTGI6/nUulGzAYsGiu0Rg1s/wohYphiP21hbUUmAyMNWpGg22TS1oQPc2jcq59p48DEJBjAtfFBtBqtnE1ZrRpHQSjYatLZWqShvHZpwKMXRc2Z3TDYnl/cPGyKOJxzy+JotqVxPzOFWy1O2YzTWFZ9tnUa2sqVzZDez6SxA6a62ycjhOXH/q80Sx11NKxjlhqnBydIfz00O2zaXCUivHJw/geGPk/cmvz8QmMJ/t8NWfM7Cmaubda09wvjyxclMhlcRxhTcOb8G/guxB1VPOH7X9Wwmr5Qln7cKQzQRmvsvi0mtcij1RNv+wy8WDL/DMU/vMumx+gu2iKo0oMpVCVE9EKGKAl9ZNObuZctrX26ZSWWe7GceSmTczyZItqky0kovdJOITYzXTzb1OWERP5x1jCYy1sEwbtN5GprMQGBuoV9Wow1PNzUzDeAl9jFy9fJG5h+++e588TKyjsRlDFQieLnpElZIs6nw3BJyYwUXRTZ/vCWJZwpYvZaev04esN+dsA8vVbmgXPeKdhZpqaUStQiqKTIU9BInOZu5qVYRTE3o5NYHZtQv7+NXA3aNjmwZBs/DaBJMa8cwLFFehiFVgCnPv6V0jkjnPhtprmQcBJ5adoMU8iebzWQuFMflw76wCId7kwtUbJoptG/9QlaPlMXJ6aME5TV5++13lzp1XUf3pYw4/bu7A/wx8vn3JBeBYVb/SXIm/C2y8ZP65qv6NH/kkgufmtYuEOOd8tcaL+fsfzGascmYYB5J4nhqeIz35ebQJf1ThwX3l9TfeRPWH7/uZVuEZLfMTWaroNPKBTm8cmU6POJGH8g3omM/f5NatOcHXJn4C9ZHZz/wSL8R+W3aiUCTSB3NG2QiQwKE1t6rDphClmuOxBYE6HMaOzBW0PJw65JSZtDKmQsqw13vmnWMnehbi2Y22CY3F+OZRrNWIPm4rhQ0BOzXgLji4vLtjrsu5MOZE1mJRWLFnb2eOc45hmlq+ZCNogdm1tZYpOkv6iaoEZ4i+23xq1UhFxqNoUw8xkHbK1W68Wo10JNo8JCxKPWglOsNdUqPuzrwHp/iaOTo+4WxK7HQBHy0zcRP4Gr0neAsHESDgzP1HrUqZNeu3okI/W+CC43SZcJoIPiAhUoeMF2UevFVcxazIAfDOPCo0EJt5Khj9twcW3VXchSt0wUZ/iz5w8VLh8o0L1HxOaEzQ4ANvv/UqDx7cbnoRtviJVttM/yzrx8odUNX/ePNnEfnbvL+7flVVv/Jn+u1teee4uH9AiB2lTIgXnHqc90zZXIUXzrG3f5Gye7FdHMbMu3ldeP7ZJ0n5Z9p7aUrBN9503L79HXK5xcZrT4BxGj/EuOKnudSUjO97LHN+/kPOz9+r6wKcI5wecuQDqG6rCeWX+PmvXGZ/YZHc5rIs5BiYxTmuVFOiOSuXs/GbN3cOTg0bEMEMM7zHt+thnTMqSibQBdeCMm2y0XfS5pYVT+DizMZoQ4FhSiynzCoXsjr6LjALwtkwEp3AzERDU830vudgb5+DnTmHp6c8OD4hJ6Ne7+zuIblw/OCQ83EglInOeRbOM8c33YeSiqX6eGfVjOVHAJjX4NlglYnDNoYYjIarwcazw1SYvBKltWtO2phWSEW5d3LGVG2SsiuOwck2/qtuwBJl24YZw89Sj2beTF6WVZqSzxMkE9rtlFIiVYVaGeomWNc8DDsnjSJe8WpKRLxdzdIiM3ywz8R7I3+tU6Xbgeuza1CvGb7jTDcw2z3g+nrZRrvgvGU3nNy5xTtvv2qbQbvm0vhoo2vrJ8odELP2/avAv/2jfs7HrY3jb995G2+h5KycrCf63vPsjacpOvGDdx9AMVVbo58QdzyL3QNq2du+2Ojg8kXPN2nyBAAAIABJREFU8PnL5LJsiLwi6nnt7e/z7u03yCU/HNmJY5rWLD9xq+9HAkFqId97h7sf+Lpzfv/3eoLfsCfb/X3zeV544jkupGTpxF6AjtnsEvt72rzps/XBYjeI87aZSjEuXvRmfhp8xIdI9CYTTnkyKWzOrFbL5ndgN9dBLxx0PUOKHA2Z1VQRsTj5IkavzlqxQYsj1czR6owpDZyvR6bJ5N3LotRpYDkmhpLa5yLMELuxmv9DFU+WyrpJrqNUottcqtoceYQgEBESlU4NaCxin38iUCmMCr3zrHNiSuao1HtH9Fb251qpOZOzvQbvbdwbnUfU+ALBCwv1uKp411Fp73NVpjHTRdOoRLWgWCcKzlmL1fggG42oOEfK1tpIw5G8c82TQjD0RLb8lFzNvyB6y+RIWhhKI0lJJc732Zvt2pRCBRELRrm8f5UrTzxrwq82YfnWN/7Bh16VPykm8KvAHVX9/nsee1ZE/gVwCvw3qvo7P+qHqCpvHh6z03ecrUdyFWZ9pA+Bvd0Zzzx2lZPlEa+8ew9VI3iIOGo1N2FLjTGvOJNQFvq+0M92qbqzBbNqVT6/83M8+9wXzd4JxavgQuT06C6v/OBPtqfFZr58J2e49yMNk3/K65APyyTl/JT1698lNjKQiU32uHTpq7z4ojCMucWMQ5gtOLh8natiUmsRTwh90zy0hNtU2I09O7M568HhYsfuHBZ95PBsydH5ikJh0QUWwbEIjmuLyLozJt3tw0OmVponzcRoJe2YEneOji2vUM0p0TtnhKBpIDWT1S4EesxjbxP+ISotbtx68EkNjNyOPrFR7KxzzazEPtdKa4+8ue4a69I1kBGGrCynwiJgAareUoBqqmaEogbeguEA8+Asv0+M4w8gXijOWWJSMR1GqZk0FWYxIrhWZRnVuhNwSVmlzcZgrVSulnOxuekpNiXy0AJ2aEQxC0VBzG05WFy3bQwYh0O1SdMb8SyIGCcm9uztX25jbsF7+ZALytZPugn8J8Dfec/f3wWeUtUHIvJV4O+LyJdU9fTRb3xv+MhiZ4/X7t5vVtowk8D+/oy9xYwxZb7z1hucDSuyZuZdx6zryLmy8eeTFlkuTaZbW6+oFHNUcdLGVoV+tsB3M1BTBbpaqeLYW+xy5erjpGqW0E6ULIE/GdfIS79PKzxtPJjhzTdHVH/w6Mv6ZNe4YjWuHnnwiPPzQ+7etYth61K0e8DBzee5KZtkn4a4711i7+AyB1pYeGGvXYiZguTMou/pFnOGkjlZD0wJ1glW40TnYd5F5s78DG7du8e6+O1MXoJxFYwP4c37EbvAozMH35kXqgi9wpSrUYRFqGIgolZFG1CqVQn60FXHYaM+1xx/KsKyyXILzRtCK0U8E9lAQaVlEIiRiNoQx8SkQmnSkk0SdlGbZkzNASircm4dUpteGWt0ERyz4FimzKiVLpoN2WwW8N2c8fwcdTZliS43N2vLs9CGZ5lDlE2IUq5ksd5nM7adOSFX46XMWgZF2ZSSahusXZPt5zkbSc+9NwwivwcT+Bh244+9CYhIAP4K8NWHv0dHWuieqv6hiLwKvIilFL1vvTd85ODSNc3FVHRRHOJNcz3mwmqYOFydsi6FThx99zBJyNJeXCPXaEuBaVoKDBxTeThqE8xZN5fcvl+Q4JCCxZr7BZ0Wsy0XS479hRDxP/8XKc4BzkxGJuXSpYFSruNEGynIsVye8dZbP2D1qSYIVXL+kOnFsGI8ecAR74dGde8ieweX2fWe6IR57Ij+GgcXn+HadStzAU6mCe89OzgySqpmfjk01q+vipAJNbMISk1CGgtUu9hjMB8IV8XCXkMEtAGpDqe1mV2AD4HdnTk1Jepk5jJeoWuy6EbS3I4QfZsjhBhRaTz5lFCEeTHgbpSEV0eQ5kYlwl40Qw8LfQGSjQJ9u0aKmqZkXWAom42g2c6heGBXhL0Y8W7TSprTU2mK1r/4xc+xd/Vz/O43/m8enCYCwiKYEa1Rt21TFKyyoFHRpQpDKZQKnTOuhHeO9WRhO1MuLbbc8ARrZR9+slFsmhKcZW90BMZcGRpF++P0Dz9JJfDvAi+r6tubB0TkKnCoqkVEnsNyB374UT9gsxzCwWzG3mLO3AdO1ktyKZRSyFSG5ukeojHB1mWyqkGcpdi0WbpFNhsFVnWz4xZysQ1BnOXAbe2tNOOKI3gb80Tv0WyxU/azCh1KCXHr/V5KIcwdX3wxgPsFvLeop6IwDCsev/4U03rZPPmspD07P+G117/H6aekewdAK3UaPhiDeniHo+N7huRuJxhz5ouX2du3EBBoUW/zHS58+d/kmZoJ3nT9wZv8epmMlzDzwoUYWUjmPMOoZuW213dmwOqtd4faLNndVvgUMNcnp8o0TXRiYbDaNlWKufpIU3eCUa2NqWk3YBc9IQUbRVYli5GFehcfOlIXm20sOm86B6FZt5lsNzpndGW1G7sL3hKOU26TECF46Hxkxwm+6UzSVFim0nrxQgjC/dXE6f0jzobEfHfGeiXoOCLiyC1yPbYYdnWQtGUzeoFiISYxmFXcmC3ebJM83LeKxlqH9rzFwnQEZ3oVYJVKi0xrkmNMC/JR68fKHVDV/wlLH/47j3z5vwX8tyKSsAr0b6jqj2T9eCfs9j27zeBxTJVSEohyPmWmom105MmNnGE6r9IopM0bQDaWSmpMsVIZq1pZWkG0NiqtOeQIFkXlvUdzYdAJr9Z/Oeep5CY5NgGQZrtojYOKVSHVmHq5Cl3oeeLG0y03zsreSZX1euD6lSepxeituRq6/4dp4vyPvwHD8ke9RT+9pRUt9RHzrsTZ6Tnn59sJU6P3Ou4+uMPtBmiaj6Lg3F/iL/zKHNHChFl97wbHohRWWZnUyv/OwzgVaq5EIIRItqfQelzLdihaGYcRic6o0rOehe8o5yvW6zWO0MJBpaU12cZQms5w1jlc6FhPxiScRdd0+gauVWieMTZGTaVyPphf/zxaWExtITZgjlYBj6swUBAPsy4way5QuVojumqW4HNnI0OVyh+88gZO3qFXx42rl7h3fMats7PmGAwUWhqWbTqlHUi1TTmMiFU5H1PbAGzuZ/iIMHeOmVREgvEK1LQUGYswc87Z+LRhCU0PyU/kMfgRuQOo6l//kMd+A/iNH/UzP/AknOPxCzukCifnS86HAYoyrgLZK9FDdL7xsbWJQzxowTtTqOGEKdWtAk+E5iFmwhqT1Wor3wzs2ViBRi+MU2JKk0VExY4YLPQDtV6y7zq0ZIZp2s7pLT8+N967iVpQ8yTMmE/BkDMqjguXrjL3weyl1OTEB7ky7V/GqTKbd+AjRTO/8/9mjo//Pqrv3Rzkzzz3/fFWpfmabJdSqQ9us/HTeXgZ/S/89v/1UHMhYmaa3c9/jV977HH69cRyghwDq5TQohwEzyIINSvrZNHlTgtdc09y4qg4cuvTO0lECtk7Oh+IvnIyJouL857zaWKn74jBMXeeXhzzhX0OUYu5BTdh0ZBroxFXCto4JE2au2GAbqYwIgyTWcFHjEacipLHRPK2sdSCgXwUXFWWObOYdVzemTEU5XyVWKtw9/4xZ9OED56SExsLKo9JzItTat4E7DY9hYilCauRyaSd5uaJYM+xcxbfPpbWJuiGVu2ZtcMnbT7MBtN8TPbIZ4MxqKqkZJRhc/d1DHmy2baE7ckaxTLhqtiYyGFzcu+dAYXZ1HQqIMHT4Yhhs7sWBjXk1mMx07laGdZ5hyIM1Wi9ZWNGiFhZjWm0pVFJbUyllhyEMOVCyYUOQ7ELLRwEu9CcmnZg0kJ1jiiG5u4DebFDEWG+WKCY7v1rvzwypP/I4qkbc0Zdx0vf/T3eeuv7bKzLN5LhWjIlf0Lch/f0kg//dMr52Yd87e/8I37Lufd8T0fsnuLmU3+OF5+d0DGgmH33CgvUXISOnT4gjV6sWlivJ04x67cojk5sJCbRo6mwzhZiuqiW7OtUSDVb9LczExUtFecC877gZbKNCuNd1NqCZRrXBGGbYVnUqMYb8VepylorGcdQDSvoVJh3jjELq2wpwr2366p3dnOvcuHk9JQkQufsxjaLRQU8rmUvAkZBVgOeDTqQ5lpNa21px5U5EYuDMStTpbkKWpXVhUCIgm+vrcOo6kFMq/FR6zOxCaRSuHd2zrzvGFIhBEenwW56reQ21rL+zUJFS6VZhk2Mqbn0VJr3n4e8SRlUcjWteaKQHTgXCQ6oBmA5YLfz5NJOomJiFSOYFLQ4XBPpaOvtTPmmVGceg2AfXkLNLRhIOdMJRAkb4YE5H9eWeYCVnl0XyU2/bxbVhUjEOzvBCo4uBH72K7/Mn/vqL7fpBRyt1qxS5uTOm/zwlW9Zl+Jszp2oVlGtP8VWIyfebw0zkdJ3+f7L3+PVV97zsHjCzRd4/vmfpRtgCoZyT1OF3QMuzHaYiacq2xBPFeHixQOu7+1y7+iU1w+PGUtlx2aE5AmcTGb86gXJhRgaV0LEvB3abV+LXQsVZ+nnYoBaI2wyD94SstURAvTFPAKNFGygdK42Qo5euDSLdA5KqUjocZ2JrsQ5JJvTMzQmZ3Or6qsYBbvYBkObPJkcnKYka0e5IXuGe+AYBVJzDYJ2DcUOJ7BMhXXJaN1UHbX93M+4x2BR5XA94PJEqLoti7R5/QVMabZhUJWm+tqwxZSH26VttgYUbtxbBYuLkqoM04SI46BfEL1vxhmVuRfmfceYSxsH2cXivY1oSi2GFLc5cOddcy8yumn0rpHdG5AjBohtOYKCxZe1VkArJFFms0AXPDVDDME8Cc2L3Da/ahl3XfB0wbGaJlZlYhZCQ5mF6098jptPvUjfRa7v77LoO96eEv/01Vco3/0jw0AUUs2cnynr5THwaU0wDJx9f7xAobz+Ei+98V2+u3moQS288GW+fP1JrjSdxGrKTGWPCxcv8JWfe5z/9K/8h9y99S7//d/9TR4cn5jGYXOyV6vWhjGzcCaSAjtNi43pbWSphsDjLT1Iam0ZFXZthTbxoY2k533H+TiiRZmFaEBwqXgqNRrnYCoYuWlaoShDUoREbIayAWdqRafNRNRi58QLQQMRu8YUJcvDUWDnHk4gnFjlq0CitWJNRVpyxkVPQYg+oKIss73pnqbI/Ij1mdgEBCtz3FjAOzoXSNWix0Vtxu+dR5oE10oi2ZJ6NjmERs1uL1bsRLRy3mS8VAiq+KrN8MKAl6kU1sk48wKG6te6BSBzMYdj73z78GwiMCYLm7RcDod6SMU+zJmD3ntLhmk7umAlZhUhlUpyEBSitmjunCzZHEfSJjWm0jlvTsWlWlnnQJ1YcKlzzd9QuXGwy40Lu4jA/ixy88UvcfbcFxExQPVofcqbbwzcfeeHlPwOqgUnFYfn6Ogu52dHnzDu8N6lbYKz/autV77Ft175k0e+9jGuPf40j+8v+a2LjuXpiluvf4c315WnXvgST2llkMT5aDPzdc1QhT4GUAu1rGo3mGun/o4LxOCM0IO1hxsUXp0pNV0RU1jW0hyqrWRPpdAHT6ie86lwkiZm1bFCmMaBi31v3pA1sSNmyhJESdsb+uHvK9oCYTGZ8ya4xIvhG3NnLWfSzfsljLWStRhDsyVg51oIBBbBUwTOirKaVnTesRBh/OgJ4WdjE1C1vsU7TwgOac6QYzZl2bzNh3Ibl+SSm4xUCBiwJE5wIbSd0RBwdUJVT865ldp2arha6QR2dxcM40BeFVLKiPcE57dMLtFKKmY3FrwdK8FbstHGekxatYGwzQgEcyFCNylF2th9VvYlVaqHroutUvAIbRykVsJ57wjBUUvLCsjJJLvNdWe/84Y5qLnR1mqbTwyeRd/baLUocjY0TYL1UBcvT1y8+BTinrMbohoF+OjoLqeHd5FqaHgppgc+PTnkzbd+8LFz5k/ginjk77e5e+s2X/+N3+Prv/H1hw/Pd7h1co9bVIZiACAIEr7Kl7/UEWNn+gmx0e44DdSNTkJp/AAza62NNeRkE0cnDVto6sVmVFu2Xo7KUM3uW4qxiXwTH1FhHoSaLEez0VSQhuOIeqaS28TKmWtU02yYEZM9D99K26ma58FUDUCMXtj1YWtXPmQTZ4VGlDtLiXU1PoSrJtDLP8mI8NNativbaY8IXVBGNgCJBXECpKqsc2FSG/XNXcD7luDTNotSql3ExTWLa6HrAmNWU5iJqRD3+xmdKMNQkaDbGXXOeWvv7BRCNAPSdTKGmzjr7Z13TKXgnLdNSSszZxlwDm1ORFYDVMwR2RKOKqFvcdFVGHNhSBO52CmQzVzPvPZyYR4daGBV7HXvSMCpOf+038RQMqdrs/W6sr/gbD1xu5xStI1K84SUgitCpNKTyQWohd45Ll29jr/xpOEKXhhSNnZbGnj6yecaeGWmKENW/mgY4aV/9q/qcrG1XnLy0h/w7Q/8wwnp3DNzjdGjoN2M+c/+eX5xZ5e+qsWBidm7l2yjYEFw1ViFVhPYhCR6xQZyZlziNVBysRvf6jti6NjxtikHJ6j3BF9Jedxy92vb8qXhXLFJlRMt2k43djEmAEtqo+2NKS7Vnk/vInvRE2Okhmj6jGygc6qFsVhYTnSNL+M2StQPX5+JTUCwXHcj7djpev3CPuOYOD4fWj9rpIfaGFMbmedEZawCOCjJQj2qjeuKVGqx3X4RIrHRKimV1Xri7vGxRTbljEMgK+rM+UU2KLe0fPjmO6jVxoYJJYg2Lz6zGO+cjTN92/EtT841AwxlqiaaWYSIC5FUzeFmSBNTSs0YU8hqp7ApwIo1Eq7t6C1hSbUSxWy3FcNDTtaJ1+8eNl1F4XQ5sk7W+ozjmporvXPsdZZLsEqFLkZ2vWUBaBlI7bUsE0hw9LHjmWe+QHSeUgtDSpRUmKeCv3KF4IXzIXNpd85zN2/wd3/zjMPjrwOPukd/musN3n1U7uEc/t673AmhxbEJwg77e1/kxRee5tIlk2Wn+jAq3nIjHwJ2O100YZF4EpHgIUyZWjIuRLImUJjFiHrPOo3WgjrYiUJOrVLBNnpL2RLLvdCWDVC1jbStJTFrem1+Es06vVb2sFQur0YiAuMI+DLSeeNpKAaUFyrpY4ZHn4lNYNMZGupaqDmRk2MshRBbSosKWorpx6ulDtl9a9FkjoJz0tBbpVRjTKGVh8Mg08kTzL5qXE+MGOZQW0qPKkhoOINzrfwzAYZz0mS9Co1aSqWhzYpEIw9JbUnDruUMVLabmzhHiL5dCJUJ3Zb5my/03uGjN8Q6R6JTQvBc6jtKSqhUQggc7HiWYyY3A0+Ryr3Ts8ayg8OzFeuSmbRQktGuF8HopFNV5t7RtcirJErOVuHU6pBoRJ8hZea10gdTGE65QM1c8x69coM+eHZyZtbNuPnsc/y1v3rOW+/8NZtbN3Dv5PyM77z0Te4/ePcDg6pPrc2olXLygPcz14T7h2/z9rudKTY3z6efs/P5n+dzVx5nUZQiNm4TMq4TAws9rFUJ3rAflydyVUIXGcVxvh44X414KjsxtOBQi5PfbNJVbcynWs3WTFtIKbZxiGw0Du0eae/VpIWhKL46hqmwyhOo0Du7Z9ZZyc5MZYsq65y3k4QPW5+JTcB7z2zRobmQm3hjgdAFz8xHy4VXYdDCGiW1+KUgMA+ReehsWiCuIfkwOevBjJ8uTC2Q0XzioNGp0DbysYScNpFIVnH0weMwN5xaTfBikenWOnhgKoWk5v2fCqg6mxk7wWnzzxcr8koFF0CdlX9jTqwmSweicRii9yy8p1bDKZwzNDr6yE4MzBZzuxnTSO8CvvOMzcnYt99zvhoZS+ZkvWadE6VCL0LXOyvni4WX4o0KTdsECUoMHvWFNCSmWmzzE4tlH0pqYKtrKLdytByZUFYl8Sc/eJXOBa7t7ho5B9Pu+zjnZ37h1yg1c3HWsdd7NPSsS+Wlb3+T1197eTMIs80RqGl6H0fhk1mK6sg4ju+nV6/PWX/ztzkUtx1aCHZI/cov/zrXrl4zp2G1JKmqHXEW6XrHkB1HyzXjkJtprKlctZX9DsOvTVqs5JqpbXSx8cjQWinJcCYRm2rUNjFD7ZCZVMhTZpUtri0CZ8nIWBbCay3HkGtjTX7G24FZjDx+sM+t4zPUVWqC5WqNtIz3qRQWsUPcjCIjPYE+uO0G4ERZri0QQ9VRGukmNBKIdxsPt43CzRhbm5ve0mdkS7Os1RKBcjOX8CjroqzT2IhLziYXYv4HjhZc4h3/X3vvGmNZdt33/dZ+nHPuvVXVz5me93A4JEeUKEqk9bIjC5YtO7EcRA4QBP6SyIGBIIAdxB8CWIm/+KMTJAYUwAjgQAbkWLbsIDYkBA4SJ5EgWYloURRFUaIovsl59Ex3V9fr3nvO2Y+VD2vf6p6eGZG0ONM9YC+gMT23qrr2veectdde6/8oWKUiDSOfd4kGA8JMeWc2aWfrVG0q4Z01+jKFOWcTHBHXHialj6YFcPniPgf7l7hx+xavHp6gOuOdKePkUglBKAJTrqaG44RBvNFekfY9jmUXGZyRr3JDUoqnTSVsjb33xM48HRNG9XUNS18VhrDzHKj4CsfHaw663iTM1dh7WgrBYRiQ4pjEE11kEEfsej78A3+K933XD7CdEp0EVn3gt2vi5sd/GTm6ZbbeXU/XBV55Vcn5FXaOxXdyxNuQLGo9B+LsohT4pV/+hTf55u/nT/6J9/PU40uOp4lpszHrt+Dp+z2k2qHOO0NF7gRVbbJlVZsxiz1DcNQCc6sSvFPE2yHCiT3gSY1s5KqhMZ2o3WcptymDMJc2EVGh89GS/lvEA5EExmnLF776ZRb7V1l0HVMZeW0z0ntv55vYs+x7VquFab9hc1raGG09bowCmnMjWDiCmp6cYOILrSVzft7yQrMab11UJ2TsPK4VhrbDOjxrzZRqRh9Rm1UZMNVMFSE4e1C8mG10ESM6JXXMageIjMlq7RCGc1OciV6IBOtNAFMr2zwgIVBV6cSz33liN/DUY0/w/HNP86u//inOxpuW4VXRXJoMl6PrOrNKcyBb2Os6QvBskhWboZmipAqGPLTEEMR8E+ZqfZQOg9eup0Qfhegcc03UaniNSqWLHVNN9vnWtpdLY/7h6YI1R6sYYGtTEl2BjsYrcK4pHdvXx7ny3QiLj/4onfdc2F/xwge/k+eff5qf+YeVz33+vyOGI7bjhsOTNbUqJ8eHjNOW1ycDeV0J/fbGb/Cr/+9vvPHlR57ksee/m2t1RtUs8Yp2+HCN/Qt2b0bvCVjzunc2gRgBV0qTodNz8dladsdKQCpdc7nKWkmym0q5do/75oKl54jDt4oHIgncPjrkF//FP+ajH/kRBNikzJRmvAt4/15e+OCCR65c4j2PXaPrOtKceOXmbbPRctaZDW4CNRqqFiuPO7EurcMaaE5tlzR2oe3SpuHfXIE8iG3CVAxjcO706mDpI5TKXDPbjEGcRewD994eJuwiTWr4eAVUKtEJXegM+pmSjYict0YlIM6ajiKWgFadZ8yVDseVvYH9GCkxMitGRLLCxYRYs7HGQjRLriFak2gzNS59cCz6Du8D6+3I7kHddWNi6Ag+2JiMYiYqmlhPxW5GClNyeB/YGzqmVJvHnakbda6VqjtwlvNoSWRaQwus14GZbObatAeqosWqMmnwXcQwIzU3yve6cPP6i1RR/sKPeRZ/4T/jA08/yUsvfZl//qu/yeHphi9/9bO8fP1rQG3ATBP1GNfHHB3eOCc97Er7qmozu7c7brzE9Rsvcf11L0b6xR/j6ecrqCVbFMJqxcFjz3Cx9QXEO1r/tt0fgnaWSLwzCH1ojEnUjr0VbQAyt5tK2/vlj8gifKcizRMf+9i/vOfVQIi3SPOSG1/e4/L+PiqetFjin3gPT4hnL2VqqeRiZx/fMOBOBFqm3DEBTSm6zdbFGj6lgpcCGHc9qH1ouZpIRYiOmgulVGbMocc48J4Q7ohmlFJbc861qQAUaa7JCOrCOcchazXAD4VSrY+xGwUVp2QPPkQWrpKySYVlVco8c3Jywhe+qtw4PbXUJVaSR+9tJNRmy7kWppwZsxF4JYB3EeeNbFUQUxeWJsYJrKdEqYkYOsZUzQRVDF1XG8c/hMjQBeZpYlMzdbQbMHrPELtGwxXmDHPN7FiCxs9vFwE76nTOZty7punOVRrnGFO1/s9ckVdv8NLRCcErzz3zHM8+epkyF9Zp5nh9yv4jz/C+R58xcJfabrjoAqcnN7j+4pfovLN+icIo8LXxDHnxy+dZwbAWDVB2D7/ybbjTmba/zuc/fc/LixXLm9e5Ju5OdSoH7B88xdVrewxdmx60eWPVytwedEvTVsFGEZzYUUZwqHMMQcgPviHpW0Ump0/z+5+G37/75X5g8dhTPDasGNSEIaccid2zPPnEVfb2DYlVGvZefCQ4G+lpE8mwtiuG8w9i3HM1NpzgEM1m2tF2t23rstLKs947QBpW3xJD1weiD2xnpWK6eKK2SzsRSil2cdpNaaW3WhnomnCoOFKpHG8TvReyKsdjomrAucTZ9Vd56cYtTs7WJoXmpdmsG+x4LpXDzZa53NFh2KRMPhuJLiEq5ALrvDtPCttcCN4ALPM0A3PbRpRZKrkIvQ8ohe0Ei9ghzmy8tNj51kumdz1SlW1NrFNhPW3PpypzNUlzwcQ1vQgxCMV7s21rakKpZLrgQRxZlEk8N7cZNqmdfb9GyTPrzcR6nKiq5DIzq8HNwR7jmgXfrXjPd3yElQ8M3tE7z5kql9OaxdPPoMA8GRbj9m3l1evX0fqibRiN61FKMQbg2x3bNZsvfJovve7FgcXiS7z2ypIQ2sxfFV3usXr6/Ty9WLHfhwZ1T+2zja0KMLTjVAoF1yjJbx4PeBJ4i5hGtl/5/D0fmMN7+8Bib+U+2Lm6f+GjfMfBBZatoVUBkaHtnoqKM3BGSngXGHqTzAJsVm/dFvK5cqs1Dxe+0WB3g+SKsbBIAAAgAElEQVSqEOw4QbHXA8ZcSzVT1B66Lnqc82hRkmtOxE4QNV262gBDtVZiZxx7Y7sJSmU7jmxzZlsLjsrCB3yzYq85czpOzMXwBNH7tuFVKk1ktDVLXbTu92o5cPXCPovguH7rhM12w1nKaDI57wjsDT05Zc7GuRmDGFCmiJWq0Xu0Vs5S5izZw1ypdtwR08wrqu1YBOIgZdOCcOKbI7RRfAToYyCLVVdjg5AHEa7fPOLodI1zlrjxgVhBxSYspuSrjcZRcAmSQjCtcnJKPLcauPTBD1ERxm2ipMx6o7z3ufeg9YiUsgl6AIe3XuHWqy9S0nxH3EQcJSfG8d4+xLc6RrbbF9neKxLsHP61F7kVon3u2qTYQuTDH/pBkIDzduzZ5sJ67Oi6t+YSfyOiIk9jcuPXsHf891T1p0XkMvBPgPcAXwb+Q1W93RSIfxr4cWAD/GVV/cQ3/wF8s1Ep5Yjj0yO4l+Z6csjtEO4wtAD4fr7nI4+yWjSUlhjKsMQVBE9UoROYsQaNNBCHQrthA1phnguxdfCzSlP+tmPCTjPfpdRw4ooET6SjF4+6wiY1GDHNwLQRRHY2ZcE5VsMCqfbgdT6AOuayYZsy+8sFy2FBzQWpiV6afkIDMoXWWd5ZgqkzAYreB/a6iMeQZTXNzEmJUllEz9mc2pg0sAqevehZ10rXxpc7Vd4ueFbRgFjreeJ0thFqlJ3Yp5XZu0Q3BMfgPUWVMVUzO4lmt2a6juY94EVIqTCpgaWcVkKIOAk4b0e8KdmsvQtm2DqXcp48ab2BWiqz2Ig5iyFGBzWF4VLNEWjOBXzm4HJHkMeYknkw5Kos9i9x5bHn8ChDjAwxEmPH4a3X+Ozv/5aJ37RQVc60wtm9/nbf4qiVcnSTN9OiPT09Qu4y380K8Mf4wPdefMt/Tr4eWENEHgceV9VPiMg+8JvAXwT+MiYl9rdF5KeAS6r6N0Tkx4H/HEsCPwj8tKr+4Nf5He8QYuTuiMRoOvdtFYDCsx/kA099gCslsRw6cq1s5wlxkeXehaZGE/Him0WYgTwFwzv4GO3mbzuxUpnHkbFkihqkOAbjIjgxzb5c7OHqQ7SZvSolF6IXvMdwEGJWWt55E78cJ7alcGHoGHzHdp5ZNKbhq5sN6zERxNiTu1FlxXoUOWcOYselYTDwyg5OVWtTzYFXT7ds0ky/6Fi1h6xkqE7ZTjPBO1KxJLHfd5RSubFeczoneif0wVMwVyAaZNY52POeRxYdY6kcT8pyYU1PSmabMknuNAqnnNkke8gCYkxQHKET5jlx82xLKpVljCz7jlQrU5pNpHRX0qP03rMIEVGhSGax6Li816HqWG8Tmzmd26h7kzNlx/4sWGfeaN9WsRiIrJLmCU+r4rBJ0yfTlvpbv9KQrbDbEk5PlVIOoTWhdyd03XVN3+Z73UdHSdNvqur33fvVb0RZ6BVMRRhVPRWRzwBPAj+ByY4B/Czwy8DfaK//A7Ur8OsiclFEHm//zluG97aUHdklZwMFv32RSOlNznqf+ySf/vynrFF9jhQRrly6xnve9yG2OZvmnHi89CyGx1jsZXI2xdlBjFOw0x20UaKQ6g4cAlMuuGJuQcGZkYXmSpaC89aDcM52u1oMPeadlbdSrQm6EmF/uSTlxMk4kquRWrJ4+mg3/BAD1y7uE73nZJy4vdm2RuFOhrvYDuz0vPqYamXO2c7wKvisbJp/Xicm411RUqY1XKtBqUs2X8GKSbpptiOUIbIIYsKhi2CQmCkbQ3OeEkXhoPM4DRTvzxuojnBu4NnHjj4EppQ4Xc9MeT5v4U05c2FvSShKTvm8MaYKUTwHi4FOnGlVuEBVYTNa33zO1hwOEhqewyqm4Az/QUuSVFOMkpoJOPoQICygVGJDoTrgz7kl6U/9u2ymwliV0Jiwn/lMZhw/RiU1XIZVKmenlWm6hbwOZq2NxfqtSg6J8oe0Nb6pnkAzIfkI8DHg2l0P9nXsuACWIL5214+92F57yyQwDEve89z7Cc4UfoLzvPhSx3b7e5Q6ns9GtSo5J6N2vp3R1FzPr4HCjVsvc+PWy/d84x4HBx/hmWcruZZz74Owf8Dy4lUWKtRSrOFVTWzEhx1FxJpjZndVSFiJGqgEH86FNYsaKrFTZ5JWTqCa0OXQdZzVTKrZaERiQibm2VeIvmfVDyy6DsXcglZdB3FgvR05HmdrNPnW/1A7mkwpMaWMd0abKRiDUxsbj7Zbgun+5WyMSwMh2lyqtASxU4JWMQpvcK1HUjDxTVW2tTJIB51VHKvFgmmecNNM1cBmmpnUjhnS+i/lrgLOOWHZe3J2rKcNkdBqm0rXRZbDolm1TdgD5pjnnUKVEHxoD5z5MBp02obyzmFHMW1IPyf03iG+6TsZCs1Qez4QfEQ8zB46p3bdVPnu7/YIf5KpCptS8G3E+8pLmbOTzxHc2ngozrQBjo8PWW9ODXhF81aolZQT+i0eb37DSUBE9jD9wL+uqidyFytJVfWbLenv9h3Y37vAn/3Rf4/93nM8JarC9RcvcfvkSQ5PXiYVg+mWnLh182XWJ0eca7fbCpjmifxOdHFfF2ecnPwqn76XxnZwiQvXnuaqC6arrwosWO5f4+Byf65EPDshh4jrl7haCd7TqVKLUY2lJQppcKNSbQe2ka9D57m59DpyyUypGCqtCDkr0zRz4/AIJ8Z2rCk3kRQjKo25nnMxbMeud+bSO7UKtd5CViFju1r0HaK2m5esbLSYhBt25q8oUzW0WnQO09en7bKQsqk/rbqOOVdwnuSCCWXkSl+rKQGJ0gdHyoHTObGeknHrxZx9vECIgb1oxJ5cK9GZ83F0HhCGzlNyIZdiY2Mxc5JSDe9RqOe27iBcHHoOusg4zYzTzFRMS2BoQDGoRtwRmwrRXIwcUHNmrFCyiZR0rcJDK1WNCZu447JctHLtSc+zz32Ii4sFB8sOJ8J6s+X2rdeYxlP6oSOXwvF6w9l25MarL7Nen7TjhjVCJ0DHzb8xMOobSgIiErEE8HOq+s/ay6/uyvzWN9i5ab0EPH3Xjz/VXntd3O07cO3RJ3Q1DAweXj07Yz1OfOcLkVG/ly+8eM10BbzH1cwTTzxD2p4aaSfZha1aeO3GdW7fvmEnrpYc1ihpfQLvdHI4uc3xyW1e3x4aGJaPsH+xb24wDRnYDSyefYFHKoxO6AWs53+Jq5c7Fl0w8I1mavNMUDUceh4nQMi1sJkSMifD/leDozLOzJPRomsD84RQCd4EMroQTFZbFWiClQ1HpFWQYhOA3kW8M9gz6ozSqk01WQyk0sWdhZh9+B6jgXfeKoNUDGegVZmyUtoO6T0Neq1E54mLYLNvgSqOgjUho9s9vFZxddGOUd4F+ujZzMbriD7igqcPJvv2xMU9pjlz8/i0SboZgWfMmTFNFDVDFcRYg6kUco3nWKJSGovPG6Rsbo7Sq9j0HrTikn122nZrbaNjZfe5OyqFWQ2lilODrKtJsHsnXLiw4Omr+yiO49OBZx5/nKsXD+i6wPF6w8s3j9hME7dvvcLR0SGbcWZKmVSV66pML34Bne8wIJzAuHWkdIzBj946vpHpgAA/A3xGVf/OXV/6ReAngb/d/vsLd73+10Tk57HG4PHX6wdUhc00cVISJ5sRgLP1zK1xzdHZKWhTCPaOK5evsb98DyUV0pxMA0Ar1649xenZaZuZmsDDy6qcvfxFWB9b1dBoWUe3HdN8nXeW7joybr7GeK+BkDj8ySGH7ZAQWvkHH+I7XhhYDRFp5WqumbR3iYtxYBk8XRfZzJlxMzFms8ko1TOEjhi8oRNVEGz398787IbOM5fKVEoDV4mx2Srn/Pvdn6qm4tSJjSlL1cYktKdExRCKw84nbzItf5tICHOTgvPcEb2YizLXgk6znadjB0BQM1Odkp3zx5SZUyYXxTvT/TdHHlvzDu3oY6BopehkoK1aqMVTnTLnvuEohCieIJbgtMi5VmNovRsUxmki6E4opAGpcjUijsLQdySFsZrFeB4Lm7kQYyT6hvkQx1QS1GJKw+qYsoIr4Mx2zdROzJr9scsHPH3tKlcPlnjn2V9MppyNcrLecuP2EdtpZNV3LJ98hv0LV1lvRvssq7LMhWlxYKQrsYqmD8KtVwPHJ5+j6hoEjm+9Hru4i2+kEvi3gP8I+B0R+WR77b/GHv5/KiJ/BfgKZkwK8C+wycDnsRHhf/L1fkGphVduH5PyTCnQRcfN0w2nKQHOyr52Ct2OM1qtE6ul0FeHU2VvdUC/vMBoVD4679hHqJcuwzzZ3L999i+9FFif/R6lntk4qZ3Nj08OuX30rfd//0NDK+XGy7zRnGHNPBnGvzl92tn6mQ/w7N4FroRA1wVOx5mj7URY7LHcu2iGnDFysOjP1WpQg9F65xi8sOg8Z/OEUAnBG8ci29kXMSZak603XHrJRnIRmkFmM3BB6V1g1UW64MgSSaXYUQAMDIA2dR9D8hUVRGz8WbGddjtOzCmfJ51a2xSjFMpOPCaERq91RrmtylQFFz1DjHit5MkevlwzMwnxjpcPT4jtwu91ES/W/KwYz8M7w+wLkMXhq/Ewipo/wVRhaiIgXey4vH/AJHByfMwwKTlX5lzxUei9wadTNWpwHzydM8bfJJHqrAcixTYk7w0zcvlgxYW9BaUK8zhTSmWcEykbDH4qhRDMKv3wdMNmO5JTpneeToRLtZAvXrUZVTuqRA97K2E9DiQSIsIn/9X/9qa34DcyHfhX3Jlo3Bt/5k2+X4G/+vX+3buj1MrxdsIoPI48VzaTUWB3YBdzUjHQiSRD3ZVWfgmKpmIXq5rUklbP4Dx+cQBLaZBQOza9bxnw9fvJOpOSmVp2XeTW4atcf/WrDZkFNFTbZrvhxRe/+M28pW9B3ObozQyL1qdsQzQ9RGm2VAiri1c5uPwYaOUo9uwPA+KvcvnS01y5Yr0Co0RXUmNSmrmn3TSdCLOYN8Ou71qQZoqZEMwiXZHWmLKyQXdwZzWW3BBjc9K5S+FWTZlHMZRkkXYmrlAojNksxAyld/fI1hJC77x5ADglhMA8Z1KxqURJEzjT6+0cSPSM2SoNFM7aqHQ/BrQ4spPG2d+VO0LnvakFq9qDmLMlCrFmbMS8DwbvKaWwHDqOnONonFh4Y2hGimlgGtCSXoRFbP82oDGwrHA6zYzVfBJRE3x57egY581DaBon5lKYU0bEqrdCJTjhZDNyvB4RVaKPdN7IQ7umuVIbHsQmO65T9ro9itxtWPbGeCAQg1WVKVspI1qaqQd2TrAUgEca5VLQbGMawfDvO1MGxJ0rzO5ktbSJiuzsnmtVoivEPtJLh8RMSpXq4PIjT3P5yjVEK3NRnA+sesfp2RkHe1dAzM2oC4Es8AfbNflzn77DkOEdmPtOW8Zpy3jPy+P2jKPXXjx/ryIO7x/hypXnePSRwu6Gr1Vx157k2tVruKKUkone4cS3zn5FvLYEY+8nV/v5HQXb2gsOxEw8N7mYBgDQ+c7UbkttD32lFlPw9WIw1jHXNj3Y/akNxWmjUSeGh/AYFn5wVsWEXVfe26zeTQlPQucZH4xeHvAEB2My5aQEhOityy4gzo6PRay9ob7ZklWbjMxts+mHjj5Eg95Oc6tkhM04kbL5VW5zpXeevvMGMcaQquo8fRSG6OlihBAZaLTxteOwTuScKS0x3j4dyenQAGhq7lCl+WpGL6xWAyrKZpqNKeudqRx5yG3Eejc1QBsmRDHVq+rq+XPxZvFAJAFpu5KordRUX+0G2Ykvhkb/FTEMQcUzOMP2l9Z69s5ZCRYNIjmmwjjnNi6y36TAVApJjSBU1LjempUQHN5HSyqi4B0JIQ4HPPf891gSCI69xQJ1wt68hcuPMs+Jebb+wnojvHb9NvP0FZowNGCoubdztKm1tIf1bgrM13jppZd4+Z7Jprz4CFcvXqVXq6SCc2R9hEuPPsHFy75JpilB7WGX2Nlu1j5Dh5GvxE5SzNlk1/sYCd6x8JFcKnPKVAc1CN50sc1KvtRWxe2sv+xfdk3ktaKUUojOsfCBZR8RvxN/sSrw0qLnrI0fozfIbFF7eDpnx59SzJBTnIDz7Qjg6H0wVyIVCGYVl0tBnMeHwLILHAwDgnCynRhJpoIdO1KaOd5s2+8x4xsRq5gomSSgeMoiMnvPVBzXLqzoO8/1o7V5GTQZPSfuvCk6p8KsGUToYsRjegDRe4YQOZtntrlBz2kS60mZszYNASiN5o62Z6k9+NEJj1xYvuW980AkASfCInTEhhPITXopOs9UCtM8n0tFO3GY570x2kWEJgRs5a0Thr7jwv4eRYXXbh+z3m7N1qnh120sRiOcNBVIrFQWtQ81YMzAlA1z37YqXIbNbLoFj7uIe+aDTFMiZ5vdTclx9dENJT1tHX2137leH3P42otM4+auMZwlhzSNvD3Vg2n+v2FydOs6r76hSXTA0a0vs9qzm/p8Y1ld4MJ7P8TjNZMbRTt4h/MXWPb1/H34VnJmNTxBDJ6UEkGNo+CAbdF2FGn4/ool1lblCXpuHoraOdtHOReGof2sC57BB0oXSVXxTZatZtP789iuvTdEQjUkp8Ns6opmFn3HMnq0FLbJGn7Be1bDgoPlCqolsM57Ou+IwbX3ZyCkKWeiYAanAuNsabd6YZMTc63kEaCw6gaeiB19F9mUNUfbyY4unTd3qlpxauK4uTkR9f2AdzAlJfjOmpMpMxcDLNHIVq4qUzEhkbsP7NLg2iYy6nhkYXocbxUPRBJQDFHmxVByvVjmXi0GTqeE1KYETD3vFDsx5yLXHHfAKojNNKM4lgtlf2/J1VrJeSbN5l6ccmHGegc7/j6YIYU4O9AZTdu3G3SnACuIOrtgo54nnB3AVMQSWN8rw7ACVlaWqf3eOU08cu0ZUrLGj53dHGkaeeVrv09ODR3ZQCSnAMe33uTTervihLOTE85O7nk59hwe3eBmS2ZmSCp4/0N81we1cRwM6pxFkQuX2J8Le9Gf92d8s+Wuag2/nerj+RvGxojnx4P22S5Dx37XnVeJczE8w4ByelrwsWO1GJjHLdSMl8rgA+Idaa50Qc7LeFdNgr2UdC65NTWZtf3FwNBFoJnLFGWcEiVUghf2+oFUMnM2pl5wzVodU4pez5mDGAnRWKNjyYzbgm+EreOzM6SseOLRq8wFjo+PiSEgTknTjGufaWo8EkNlVrZzIcbMit7QqMEqLLQJ5roGeGpJU8TjnRmdaJMt7/qOi8uO0+1bT8IeiCSw6yCnZujp2+hmO0827nNmCFpQpu1IyZnOmQoO1Syh47l4J2zmxIuv3WR5csbgBbIy18om225WmrddGxgYGEcLks0wVMQkxc7htNUAJa7tWFpd+zk53wV3Z+XQQFS5WlNrt8P33cAwLLFmWks+YlZqFy9ebU5GsEvpX0FJn/1NaL8brJt844aQ01c4N1l5uyNNTK997Y0W5/wKaWydgh1RyDncM8/zaFyw5+5g7vuu49rlyywXS4YqpGS8iiqFquUc5GJK62YQG71nFQNL7wzMpJVtStaAjN60DZwQ1DOjDZzjCL03H4F5287n1lyec2GTUuPgmyjtYui4uFqwGAZygaP1hvV8ZupQpZBSYtFHhr4ja2Ya0zlyzzksWaRmNx6sFO+dsB+EbTWexdk88vLNStorPPLoVS6tlpycnCISCE7Y6GyKTwXmZGIrU7LxYiqZlD1UZdV1XFgMNjnJZqtu1dROPcuaiMshAo7aGrWLPpCzMk8PeBLYVTJZrVVijjFKzY0e6oSEIwwrBg2sT49NBNMJqD2cvffgA5vZNPo22y3TNHNh0VNrZUqGhxehHS0434QoNEPIxsBj1z2wB72JfhODZ9Gb8GdKRo5xnibumBthp8PhrHnU5B2TOU/g1LEbxLv2rp0T9vYvmXlKm0gALIH64R8+PzuaWo7w1a841mefAsam4mOl5O1b11mffl0X+G9hvMbhm9HYNqdsWlNvFzF23Lr2OJcvXmI9Z9ZzoarHhavsXbzIYtlGg2ogp45K74RaMsej9VW6aCCrOVU2qbDyHq2F7bih4gnRjgYqBrst7Uhstl3KmCtTsQbwECN7i46D5UDwgXFO7KAPpVjylyY+O2c7p6cMXT/gXODs9KRpP5iGQ+ccIQZA6cWOFo7KmcCohXVK7G3WvHp95jQZQrNrSMM5K5sptWmK0IunpEbXxqYwtaFJ9/oe2vQi5ZmUbffvxXodB8se8Y7bZ9azEHHkapblfxiY8MFIAo104xvpQ9XOPrVa6ejEuPxRDCBz5qzpN4i3zrYNmO1ogP08YjfWmFNzorUH3Ad7sJ0K6q0TK5jJw84ewotJaZU6n48KpZWVQ4wsu55cswFz+sB6u2VOhXGe2aZMdGZW0oUOoXIyz6S6s8Vu8uDQcAvOHG1o628Vh8MRhgHTP+f8uPLUex2lfK9JmTmPc4bGO7z5CqdHN2jKeucONke3rvPajTcANt++2K7fUDWICNv1CV8NwfQctE0a/BX2LlxiWNJUgez74xPP8dT+RchNyl2EfWdChFMujAoa2g4YHHhlKpVUhDpVquZG+w2kaudnHz2DmnPvYugJXthMiaoTqCP4jiEEoCBqHU/XJONSyqwWC55+8lEOzxJ5XNMHm1j5dhx0wbMMjhI8U1XqnCglm1SYU7yrlHnibNNMZxHm0o5FIlSn5yPVqZQGfrIjy1wKDkNNrrpIFz1jctRpIrhK7x3RwRAcswoxeLw3paa+eWxu+CPoCbxTMbeRSRQzU5SSwYGrTbSjJObNCbVWvB8QElmTocPUOuJOMlO27J2bMWjW0saL2jqnBugoxXr3FetHSGtQWXc20PnINBv2YFeqVFXmlFnGyKrv6fsFXQwMMTJPM7fP4GizYRZMvKMJdkYfyGpDNBH7nbXNiRVrRgZsMepgLo22UwqIEVOcq7jqESmoN+UdaRWCc8rVR5/kytXHqNhYqW9SadP6mJOTQ/OrUyXVwku1cusrn4XDV9+Ra6uqzGlmTq9nhSYm5vFVwxTd1RiVk1uc9Qs8OxVeIfrneeq91+iWCupIWtmqslDFq00TchK2sxmAdAfmMnSynSjAft8RvSH0vDMVJVD2+sF4Fo13IdXuRSeeZRfwDtbbiZNxw4s3b3F0NkLJVBcN/luMgBWKJ3Seoeuh6Ud44FTtzF7EHvzohZOxcHttbtG5eVKoQhbDNoQ2GY9CY41m1AXjbThHUIcGmFMiiHKw6PAox5uJM1UW/cCFvSVzSpytN4jXBtx683hgksBuVOSj2XRLrfRinV2tZu6YU8VLZDEsiS5zcnbCmBLRVdNYa6Me43HblCHVYvm1gTioAi5Ti2nwi3eIN6OMXZc1FRsr5nYoMNCeazu1/ROKiXEso83Gt9HZWTY4zkZDKJ6lfP67TX3IW0IqSpJW/rYOuWuIqFx34zJnO4YCmk2SvAF9RKz6ETy1TZu99+DMRm2vCyZNVivL/gqXLj9CVjuWjLmwr4X58qPIvAW1hp0X+MoXI69d/zi1Xued6TmUZvZ5z8uHr3J4F0HN/naTk+MFPrZKTlq189TzfM9H/wQvUDnJmeko4SSQk3I2T2zmTIi+waohYvZvfTT4sXeejWY2YyKVbE3bpkplak6GTj1db7l1tiYg7EXH6TwbfqFBo1edNhap0BfHVux+WGhmVjhOhV7MeDa4yjhPNhivRtDqfKtDFVbBseq0fULmnRk9LBYXeOzxJ4gRPv/lLzNOW7omJ59m8zcYU8G5iicw9I5Xbp+SdIPWxVtehQciCexomkFgGRwuRI6KoaqcE7alNNHKgg8RH3tT6g2eTqtNDqoBg4ZgDRLF7JyL2jhQsWlCrYasULVjRhRHdAGlklqpXrVRZ88bh/Z9nfMcLBaNcOLJJYN4DlYD+ysBDrh8tuHVw1Nr6mjh6GxknBLOC9EFw41rMiotrfdQIYlpzNsIzhhyUm1aEiWY20y1HolrydFXQ78Jzj5D54jOcBNjs0zThrzK7c2sYmCJp168QnndlEQ42PMMH/zTZJ3Pu/cuCF/9auSzX/gtVD9Hq14BG2+WtwP7cNfRAHY1wjFnJyfnVdl5ijg+5Ne+8vt8Qg2sk3NB3B4XLvw4H/m+Sk4T67Gy6YQh9sS+51LfsRo6ghe2k0mm2XWvDaxkDdtxGps+pMNjTtLOB4OZa+vHCAyh48IwEATmZC7Ie/2Ak0JfHHP1aLMLMyq2dfOjDwzBYGy1IbOCF/Z7Tx+EsZgRqSkoVUQSh7eus54mTs+24IyYlBt60mOW9iklzrYjqWSmlBDpWMa3/rgfjCSAjeQu+krvFVn0zLmwXpuE1pQTM9bRl7qhC44YzYhUmo9bqjQNQGHlPV00Tr6N5ypzrecmJLrb4alolXP5Ky92lsdhdNwmiB9bD0G80EVhGTx9P+CcMyOSap6DsfOECytj8mnFOcf1wxO+euOIKSdUoTSdQ7uJ77qjW7XmG9vPjguegz4y9JH1diLX3N4ThnFnh7LVc4JUbarHvn0uWbVBrm239yHQx0jV1qtoCkYhCGGoDP0CcQubixelivCB9zne954/jvBRRCu994bBOLnJxz7xK+cKOtowFlkV5nuF8b5Fd4reuWcAmEfWhyPr133fEWen/5AbN+6MHRHB7R1w5Y//GH+GTJkCITiOzjbc3CRCv2QVTXeyoqzH6Rz7ETvPQGTMmVwLpTZ2ZAUJykHf48R8KqRJuXvvCb4j6QZfDK8xJyv/S3ORjp2n64U0F2pSglf66KF5V7hQicko4CnNVFW+9NIrHG0Ty2FBFzyKkNRTg8cNkYt9x5Qyh2cnzMlk7/09x61744FIAlYJK1kceZzJubLshLrsiKmyqIHTeUa1cmnoeGwvMqXCUS2MNaPFsNOubVGi4CvQVGp6UXwpNlLyQi1GDe2DZ72dOdxMTK0sQ6wpV7x1Zl21Zp2ibMbEy3nNoxfg2jWy84sAAA4OSURBVDAgYvJkWRVXM2TBBeFgr4MK28lK3UUX6ftISomcC65i51igqVLA7kZF2BmKLLtACJ45pTY+tXWIMzNSUZrF1E7VsOKDORX5Kk1OrHkZeAO22DGpgX7wLIKYQ07T1Eu1oNkRnfE4auNP9KHHS4dXO17Nqly89Bh/+kf/A5I4xjmxmSY2ufDVcUv53Md2SOWWWIWznNDTNyNEfKtDqXXNeG8emra8/H/8L/xT584rCVWlXrzK4+//MO/fv0RU2yy2M1T2uXiwIEZHro4Qijk6ixAVOg/RB/rWbKqYrby2z9ljTcVxM7PoO/ZXPWOBdcrMU2WejcFQSzVz0+CpIkzajFDVlJISSjd69lcRH4RcM9txhGGgiz2zmIiK+MrCdxQ2HG3X5yNwdlyYt4gHIgmAEWHWs4E1XC5IhRgd7338SZ68fInPvPQKX7p+nSCm0zdJvSNk2bTYvTOOvG/Iw5aszQzS6zmVtjgrN4PYz3gneDXggAmCmv6/NWwqU1GGrqdz1nh65eiUjOPxy/sc9CsTKa1q1lXZurxKs4nOCQVjwQGzVBb9gFKZmvV4qZXimh0VihajvlaUs2mmNA66awkjuoB3ASpUrNvcNfhqFGHZR0qqlFybk6095D44VATLTQUfHD2G7Ctl52foKChjKXQi9J2ja/PUOddGLrKx2aZWEM+yIdL64InzzPMukj/8YzhxdFG4vBqIi44vnh6z/tzvQJrO+RbjKBwfnzDPt/h6vPc/cqii8/QG3gWvvcRXb17npbv6ELBgf/+DPPvsFfqhMOWZbSrEEEjPvY8X+oEghaK+eVgkanXNY3JuAqfBRnXe0fd2dkeUvS4yzpXtlEhV6JxDvcnOocLQg0q1xFxMaXmcE0Of2F8tWc+FuU28alXwdu+ZX1nzWNgdkatVlX1860f9gUgCO1qqUV8Vp5W5CkNVVsOKq1ce5eLJKXLdeOavrrdMNe/uo3PaK40TXwS8KM4b3mCerIyL3rHWjK/gvGt6f4oXx34MjFoM8pkTXROmqGrU06KV3geW0RRsbhwfUerMJiUu7y3ZW/RILUxzagCazHq7ZUwm93U2GvlDBPb6yOX9fbxzbMaJzXYyb4BamEtuRBTjrDtVaHp33lkvo3ceh2OTZ6pWM9do8/BcMH0+70EdTgtQSS3JOSkNngoSMehqMO2/nCsr1Qa6coiP5o/Q+P+1Fjov+GJag14zJ2XGJc9eF21SIiAU5pLMEr7vWPUL+uD53ouPID/wY7gQDIVdlc1a+NrXrnN6/HmqnpGqiW/M08TRTZP6fgduQLSN8+5E4vbt3+D4+LxmaIWNELdHbIYlTrMR2gRwH+R7PnjAMJjuwk5CrvMeKGznxJgSXQz0MbA3ZNZ5RqvZ5E3J8DCRgo6mq2giLLbBaa6MZyOrCwe897FH6YYFt4/XnGxGnFgVaQxGNTKYCEma1yb2O94qHogkYCOg3ggwtZCdNcy2qfD5l17kxvExxydHhMYDX09bzlJhzgYjBmssdk4ITpt+myn2iphltwBarHzOaqSKIGbXFN3OfVbuTClEWODJvjJpYSoZkrIfAosusMmFw9MtR+uJ23srHru0z95iIGchl8R22nLreG06/XeV2zEIKWWiDzx6YZ/b6w37yw4Rz9l6zeHpyDYlxpLQ4m0ygUFVOzHyjnMedbAYIp6O6B0lV8ZkclnjmBCX2ekYqjTkYhO9lEaeom0evQ/NOTlBNrnNIMpYC4qZrCatdOLoO49LxrLU1rhFQXMiqLL0Ed9DrIFlZ+ImqLkTG8y6ItkUkQTHsKg8/759pH6EzhnL8ChlxjlzfPNl5nFDaZyPCgwBPvu7Hz+nhr+9Ud/gVqbA9Jnf4uNv+N5XODw0olCtBSmKesE/9wLf+/SzXMqZV8aMHzNDFyB6uijUBhUeSyGIJ6AgNgnS0nQOvQGLNmkmzBNXDlYc7C+YxpHbZxXnIn3vmWYbQzsXWtPZnhcTuH3AEYPrs2M+9mu/iF55jGvf/UM8ncwCO4TAyWbD8dkpOeUGAmrnL612TsYssoPYbHwZPcHbw5Oy6bh559DaADmt5PMO0ErvQL1nTjMT+Xz8V7Sybcy/He11m6wj3jtHFUfAmHS3j0+5fbJmNfRc3N/j0sESlWACJxjXoao55KRSKXPibEpcVTvyhNARY09tfQRxgkut6eeM1+/UTv3BeUOnNWGO4APBC9M0MxfTsZv1jtVeFDNoFWe7ftVmwdaqLqmOXGfEOXowdKYqkyqpVlYNGefbKbpWG2Nltdm3bxgOc2FsZ20n9KFj6I17WIvBWw0z4Y3IVZtHXvt8B48d5bywrYWwWHD1+Q/Z18tsKsNzJnj4zn6PwUEMjqCm4/dJFzn7rV+B+Q3F/rc+9A7z4U58mS9+6Y3fKtdf5LXfu8SyVjalUovSxcAzTz3LB174MEfr7fnm0InDO6WoTal0N4oWM1CZcqWs14RodOhOCkOAadqQs7kZ09i20QtztU0tRn+Hbv8m8XV9B96JOBcpdZ6wf5FhJ8nsBHTF6uDP8qGPmteaYkioKRsl1ZKAcc73h8iqi6g4Zi3GKquta94ShRMheENXqQjTZCVdqZXbU1PM0R2qz3jiuQlwuibksZv7L73nwqIjiOPwbDQBi9jxyMUViy6y3Zi67Vys22xQAaHrIjF4Vl1giI4iHnEDec7UOiFaSQKHx2d4EbxEuzFUCcHTd5FuJ0fswKGMc2YzZ3IxTT7UxD865+lCoBg2Gth5JCriDAotajdg1yYJcylmda2WLA2FCabX4Ohy4aRk1jXT+Ugn5m2gNTPX2rD8nr4LoPbvO990+0OzZi+VUsyrUJx124M4Us6sS8J5kxiv7XrM48TxZmaqyd5T9AydY88HDrznUCu3btzgdMo8dnGfZx+9ROw8v/avZz7xyf8H+Nw7fl//YRFjZG/v4JyZCeDd4zz62A/y3AsQxNFHGxn5ap4QOFMrWsbIcuhZDANVhO2cSVmpzu5LL8q2JMZkjkvOeTzKP/q5v/umvgMPVhJ4868iMhA7vXtCBJce4cJ3fD/PDCt6rQw+suwifXDkWhhLse77HT0Negk4p6biIsIQPOM8MzfNgikriJIoTWWGneCuIbh2Ml9iPYiDvufR/RXRCTdP1xyuR0ptoJEQmkWUzZyXg52Z+67j8sEFjk/XfOXwkFKKUZ8PluickTIRfaCIcLrJqFiTo1Qj3XRd13QJhL4PxBBJJZNzYS5KznOTY/NkNRswL9KAR/bg13Z2VCo4x9J7Fi5YteU9pWYodqwqYEcIzEPAqRC0cqKJKrCSgAGUtZWdBcSbrVstOIHF0JttmP06nEKsEFBidOYH6YzxtplnxpxY9itElGme2UyzTXQUTtPEOGcyFSfKMniu9JFldBxPleOUWa0u8sJjVzhYrnjtcM2N2zfJeWOS6QKdC7z41c/y/33sl96uW/rfMBzOdYQ20z8ndL/3BT70gQ/zXasFg5NzTk0MHcvVwN6iwznHWJSUlegMbHa4GXnt9Iwq0LnIP/lHD3YSuAGsgXdY4O9bGld5d68f3v3v4d2+fnh738OzqvrIvS8+EEkAQEQ+/mZZ6t0S7/b1w7v/Pbzb1w/35z38IcpjD+NhPIxvh3iYBB7Gw/g2jwcpCfy9+72AP2K829cP7/738G5fP9yH9/DA9AQexsN4GPcnHqRK4GE8jIdxH+K+JwER+XdE5LMi8nkR+an7vZ5vNETkyyLyOyLySRH5eHvtsoj8SxH5XPvvpfu9zrtDRP6+iLwmIp++67U3XbNY/A/tunxKRD56/1Z+vtY3W//fEpGX2nX4pIj8+F1f+6/a+j8rIv/2/Vn1nRCRp0Xkl0Tk90Tkd0Xkv2iv399rcMcF5p3/g0H+vwC8F+iA3wa+836u6ZtY+5eBq/e89t8CP9X+/lPAf3O/13nP+n4E+Cjw6a+3ZsxP8n/HcFE/BHzsAV3/3wL+yzf53u9s91MPPNfuM3+f1/848NH2933gD9o67+s1uN+VwA8An1fVL6rqDPw88BP3eU1/lPgJ4Gfb338W+Iv3cS1vCFX9FXiD9+lbrfkngH+gFr8OXGwW9Pct3mL9bxU/Afy8qk6q+iXMIPcH3rbFfQOhqq+o6ifa30+BzwBPcp+vwf1OAk8CX7vr/19sr70bQoH/U0R+U0T+0/baNb1jw34duHZ/lvZNxVut+d10bf5aK5f//l1HsAd6/SLyHuAjwMe4z9fgfieBd3P8sKp+FPjzwF8VkR+5+4tq9dy7avTyblwz8D8CzwPfC7wC/Pf3dzlfP0RkD/hfgb+uqq/zfLof1+B+J4GXgKfv+v+n2msPfKjqS+2/rwH/HCs1X92Va+2/r92/FX7D8VZrfldcG1V9VVWLqlbgf+JOyf9Arl9EIpYAfk5V/1l7+b5eg/udBH4DeL+IPCciHfCXgF+8z2v6uiEiKxHZ3/0d+HPAp7G1/2T7tp8EfuH+rPCbirda8y8C/3HrUP8QcHxXyfrAxD1n5H8fuw5g6/9LItKLyHPA+4F//U6v7+4QEQF+BviMqv6du750f6/B/eyW3tUB/QOse/s37/d6vsE1vxfrPP828Lu7dQNXgP8bI6//X8Dl+73We9b9j7GSOWHny7/yVmvGOtJ/t12X3wG+7wFd///c1vep9tA8ftf3/822/s8Cf/4BWP8PY6X+p4BPtj8/fr+vwUPE4MN4GN/mcb+PAw/jYTyM+xwPk8DDeBjf5vEwCTyMh/FtHg+TwMN4GN/m8TAJPIyH8W0eD5PAw3gY3+bxMAk8jIfxbR4Pk8DDeBjf5vH/A7sgkAi2Ft1eAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -356,15 +356,6 @@
     "### GPU configuration"
    ]
   },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "physical_devices = tf.config.experimental.list_physical_devices('GPU') \n",
-    "for physical_device in physical_devices: \n",
-    "    tf.config.experimental.set_memory_growth(physical_device, True)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -434,7 +425,7 @@
     "\n",
     "earlystop = EarlyStopping(monitor='val_accuracy',\n",
     "                          min_delta=.0001,\n",
-    "                          patience=20,\n",
+    "                          patience=50,\n",
     "                          verbose=1,\n",
     "                          mode='auto',\n",
     "                          baseline=None,\n",
@@ -860,44 +851,6 @@
     "    #     print(i, layer.name, layer.trainable)"
    ]
   },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "with strategy.scope():\n",
-    "    base_model = MobileNetV2(include_top=False, weights='imagenet', input_shape=(targetx, targety, 3))\n",
-    "\n",
-    "    x = base_model.output\n",
-    "    x = GlobalAveragePooling2D()(x)\n",
-    "    # x = Dropout(rate = .2)(x)\n",
-    "    x = BatchNormalization()(x)\n",
-    "    x = Dense(1280, activation='relu',  kernel_initializer=glorot_uniform(seed), bias_initializer='zeros')(x)\n",
-    "    # x = Dropout(rate = .2)(x)\n",
-    "    x = BatchNormalization()(x)\n",
-    "    predictions = Dense(classes, activation='softmax', kernel_initializer='random_uniform', bias_initializer='zeros')(x)\n",
-    "\n",
-    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
-    "\n",
-    "    optimizer = Adam(lr=learning_rate)\n",
-    "    # optimizer = RMSprop(lr=learning_rate)\n",
-    "\n",
-    "    loss = \"categorical_crossentropy\"\n",
-    "    # loss = \"kullback_leibler_divergence\"\n",
-    "\n",
-    "    for layer in model.layers:\n",
-    "        layer.trainable = True\n",
-    "    # for layer in model.layers[-2:]:\n",
-    "    #     layer.trainable = True\n",
-    "\n",
-    "    model.compile(optimizer=optimizer,\n",
-    "                  loss=loss,\n",
-    "                  metrics=[\"accuracy\"])\n",
-    "\n",
-    "    model.summary()\n",
-    "    # for i, layer in enumerate(model.layers):\n",
-    "    #     print(i, layer.name, layer.trainable)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -921,136 +874,1247 @@
       "Epoch 1/250\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",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.5027 - loss: 0.8569\n",
-      "Epoch 00001: val_accuracy improved from -inf to 0.50398, saving model to landslide_classifier.h5\n",
-      "14/14 [==============================] - 49s 3s/step - accuracy: 0.5027 - loss: 0.8569 - val_accuracy: 0.5040 - val_loss: 0.7772 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 2/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.5638 - loss: 0.7553\n",
-      "Epoch 00002: val_accuracy improved from 0.50398 to 0.53785, saving model to landslide_classifier.h5\n",
-      "14/14 [==============================] - 38s 3s/step - accuracy: 0.5638 - loss: 0.7553 - val_accuracy: 0.5378 - val_loss: 0.7356 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 3/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6149 - loss: 0.6962\n",
-      "Epoch 00003: val_accuracy did not improve from 0.53785\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6149 - loss: 0.6962 - val_accuracy: 0.5359 - val_loss: 0.7441 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 4/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6566 - loss: 0.6314\n",
-      "Epoch 00004: val_accuracy improved from 0.53785 to 0.55578, saving model to landslide_classifier.h5\n",
-      "14/14 [==============================] - 38s 3s/step - accuracy: 0.6566 - loss: 0.6314 - val_accuracy: 0.5558 - val_loss: 0.7152 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 5/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6526 - loss: 0.6359\n",
-      "Epoch 00005: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6526 - loss: 0.6359 - val_accuracy: 0.5398 - val_loss: 0.7142 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 6/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6774 - loss: 0.6009\n",
-      "Epoch 00006: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6774 - loss: 0.6009 - val_accuracy: 0.5339 - val_loss: 0.7319 - lr: 1.0000e-04\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",
       "Learning rate:  1e-04\n",
       "Epoch 7/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.6804 - loss: 0.5988\n",
-      "Epoch 00007: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.6804 - loss: 0.5988 - val_accuracy: 0.5378 - val_loss: 0.7520 - lr: 1.0000e-04\n",
-      "Learning rate:  1e-04\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",
       "Epoch 8/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7017 - loss: 0.5779\n",
-      "Epoch 00008: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7017 - loss: 0.5779 - val_accuracy: 0.5120 - val_loss: 0.7925 - lr: 1.0000e-04\n",
-      "Learning rate:  1e-04\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",
       "Epoch 9/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7112 - loss: 0.5569\n",
-      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
-      "\n",
-      "Epoch 00009: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7112 - loss: 0.5569 - val_accuracy: 0.5100 - val_loss: 0.8326 - lr: 1.0000e-04\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",
       "Epoch 10/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7270 - loss: 0.5282\n",
-      "Epoch 00010: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7270 - loss: 0.5282 - val_accuracy: 0.5159 - val_loss: 0.8136 - lr: 3.1623e-05\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",
       "Epoch 11/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7236 - loss: 0.5335\n",
-      "Epoch 00011: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7236 - loss: 0.5335 - val_accuracy: 0.5020 - val_loss: 0.8296 - lr: 3.1623e-05\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",
       "Epoch 12/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7280 - loss: 0.5409\n",
-      "Epoch 00012: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7280 - loss: 0.5409 - val_accuracy: 0.5299 - val_loss: 0.8433 - lr: 3.1623e-05\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",
       "Epoch 13/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7504 - loss: 0.5148\n",
-      "Epoch 00013: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7504 - loss: 0.5148 - val_accuracy: 0.5319 - val_loss: 0.8690 - lr: 3.1623e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.4051 - accuracy: 0.8125\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",
       "Epoch 14/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7449 - loss: 0.5103\n",
-      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
-      "\n",
-      "Epoch 00014: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7449 - loss: 0.5103 - val_accuracy: 0.5359 - val_loss: 0.9017 - lr: 3.1623e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3902 - accuracy: 0.8299\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",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 15/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7672 - loss: 0.4831\n",
-      "Epoch 00015: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7672 - loss: 0.4831 - val_accuracy: 0.5279 - val_loss: 0.9036 - lr: 1.0000e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3942 - accuracy: 0.8289\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",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 16/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7538 - loss: 0.4952\n",
-      "Epoch 00016: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7538 - loss: 0.4952 - val_accuracy: 0.5120 - val_loss: 0.9364 - lr: 1.0000e-05\n",
-      "Learning rate:  9.999999e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3917 - accuracy: 0.8371\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",
+      "Learning rate:  3.1622774e-05\n",
       "Epoch 17/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7623 - loss: 0.4870\n",
-      "Epoch 00017: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7623 - loss: 0.4870 - val_accuracy: 0.5378 - val_loss: 0.9247 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3561 - accuracy: 0.8689\n",
+      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
+      "\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",
       "Epoch 18/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7350 - loss: 0.5125\n",
-      "Epoch 00018: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7350 - loss: 0.5125 - val_accuracy: 0.5418 - val_loss: 0.9386 - lr: 1.0000e-05\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3558 - accuracy: 0.8473\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",
       "Epoch 19/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7717 - loss: 0.4804\n",
-      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
-      "\n",
-      "Epoch 00019: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7717 - loss: 0.4804 - val_accuracy: 0.5339 - val_loss: 0.9490 - lr: 1.0000e-05\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8617\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",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 20/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7772 - loss: 0.4741\n",
-      "Epoch 00020: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7772 - loss: 0.4741 - val_accuracy: 0.5219 - val_loss: 0.9684 - lr: 3.1623e-06\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3553 - accuracy: 0.8432\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",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 21/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7628 - loss: 0.4934\n",
-      "Epoch 00021: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7628 - loss: 0.4934 - val_accuracy: 0.5219 - val_loss: 0.9629 - lr: 3.1623e-06\n",
-      "Learning rate:  3.1622774e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3665 - accuracy: 0.8443\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",
+      "Learning rate:  9.999999e-06\n",
       "Epoch 22/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7737 - loss: 0.4792\n",
-      "Epoch 00022: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7737 - loss: 0.4792 - val_accuracy: 0.5438 - val_loss: 0.9646 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3450 - accuracy: 0.8504\n",
+      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
+      "\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",
       "Epoch 23/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7826 - loss: 0.4708\n",
-      "Epoch 00023: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 37s 3s/step - accuracy: 0.7826 - loss: 0.4708 - val_accuracy: 0.5199 - val_loss: 0.9883 - lr: 3.1623e-06\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8576\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",
       "Epoch 24/250\n",
-      "14/14 [==============================] - ETA: 0s - accuracy: 0.7692 - loss: 0.4834\n",
-      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3498 - accuracy: 0.8668\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",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 25/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3420 - accuracy: 0.8555\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",
+      "Learning rate:  3.1622774e-06\n",
+      "Epoch 26/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3466 - accuracy: 0.8596\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",
+      "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",
+      "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",
+      "Epoch 28/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3350 - accuracy: 0.8596\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",
+      "Epoch 29/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3689 - accuracy: 0.8402\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",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 30/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3417 - accuracy: 0.8576\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",
+      "Learning rate:  9.999999e-07\n",
+      "Epoch 31/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3539 - accuracy: 0.8555\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",
+      "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",
+      "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",
+      "Epoch 33/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3303 - accuracy: 0.8648\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",
+      "Epoch 34/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3472 - accuracy: 0.8350\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",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 35/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3492 - accuracy: 0.8566\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",
+      "Learning rate:  3.1622773e-07\n",
+      "Epoch 36/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3585 - accuracy: 0.8494\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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "Epoch 42/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3356 - accuracy: 0.8658\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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "Learning rate:  1e-07\n",
+      "Epoch 52/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3373 - accuracy: 0.8596\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 53/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.8525\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 54/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3509 - accuracy: 0.8443\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 55/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3343 - accuracy: 0.8689\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 56/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3468 - accuracy: 0.8637\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 57/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3403 - accuracy: 0.8627\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 58/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3415 - accuracy: 0.8494\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 59/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.8576\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 60/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3292 - accuracy: 0.8709\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 61/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3455 - accuracy: 0.8586\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 62/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3670 - accuracy: 0.8381\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 63/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3401 - accuracy: 0.8678\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 64/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3288 - accuracy: 0.8648\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 65/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3427 - accuracy: 0.8699\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 66/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3437 - accuracy: 0.8566\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",
+      "Learning rate:  1e-07\n",
+      "Epoch 67/250\n",
+      "7/7 [==============================] - ETA: 0s - loss: 0.3523 - accuracy: 0.8453\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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
+      "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",
       "Restoring model weights from the end of the best epoch.\n",
       "\n",
-      "Epoch 00024: val_accuracy did not improve from 0.55578\n",
-      "14/14 [==============================] - 38s 3s/step - accuracy: 0.7692 - loss: 0.4834 - val_accuracy: 0.5159 - val_loss: 0.9976 - lr: 3.1623e-06\n",
-      "Epoch 00024: early stopping\n",
-      "CPU times: user 24min 23s, sys: 1min 57s, total: 26min 21s\n",
-      "Wall time: 16min 38s\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"
      ]
     }
    ],
@@ -1087,12 +2151,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1104,7 +2168,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1141,22 +2205,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "predict with test number:  92\n",
-      "./img_data/resized/Negative/3838087_landsat_7_rgb.tif\n",
-      "0.5856288   :   (0, 'Negative')\n",
-      "0.41437116   :   (1, 'Positive')\n"
+      "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"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1196,15 +2260,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.5856288   :   (0, 'Negative')\n",
-      "0.41437116   :   (1, 'Positive')\n"
+      "0.9843275   :   (0, 'Negative')\n",
+      "0.015672525   :   (1, 'Positive')\n"
      ]
     },
     {
@@ -1244,25 +2308,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:From <ipython-input-14-6c336b480794>:2: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
+      "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       0.56      0.56      0.56       252\n",
-      "    Positive       0.56      0.56      0.56       250\n",
+      "    Negative       0.63      0.58      0.61       122\n",
+      "    Positive       0.61      0.66      0.63       121\n",
       "\n",
-      "    accuracy                           0.56       502\n",
-      "   macro avg       0.56      0.56      0.56       502\n",
-      "weighted avg       0.56      0.56      0.56       502\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",
       "\n"
      ]
     }
@@ -1286,7 +2350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1299,16 +2363,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8d7a5e6c50>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ebc088d10>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 5760x5760 with 2 Axes>"
       ]