train-efficientNet.ipynb

{
 "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 09:16:35 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%   43C    P0    35W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
       " '| 31%   42C    P0    36W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
       " '| 30%   40C    P0    34W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
       " '| 29%   43C    P0    39W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
       " '| 24%   37C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
       " '| 19%   30C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
       " '| 18%   35C    P0    59W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
       " '| 17%   32C    P0    58W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '                                                                               ',\n",
       " '+-----------------------------------------------------------------------------+',\n",
       " '| Processes:                                                       GPU Memory |',\n",
       " '|  GPU       PID   Type   Process name                             Usage      |',\n",
       " '|=============================================================================|',\n",
       " '|  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/\"\n",
    "\n",
    "import functools\n",
    "top1_acc = functools.partial(tf.keras.metrics.top_k_categorical_accuracy, k=1)\n",
    "top1_acc.__name__ = 'top1_acc'\n",
    "\n",
    "top5_acc = functools.partial(tf.keras.metrics.top_k_categorical_accuracy, k=5)\n",
    "top5_acc.__name__ = 'top5_acc'"
   ]
  },
  {
   "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/3851114_sentinel_2_rgb.tif\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7face618c390>"
      ]
     },
     "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\", top1_acc, top5_acc])\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: 0.8930 - accuracy: 0.4800 - top1_acc: 0.4800 - top5_acc: 1.0000WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n",
      "2/7 [=======>......................] - ETA: 2s - loss: 0.8422 - accuracy: 0.5167 - top1_acc: 0.5167 - top5_acc: 1.0000WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2884s vs `on_train_batch_end` time: 0.7248s). Check your callbacks.\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.8544 - accuracy: 0.5210 - top1_acc: 0.5210 - top5_acc: 1.0000\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.54321, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 30s 4s/step - loss: 0.8544 - accuracy: 0.5210 - top1_acc: 0.5210 - top5_acc: 1.0000 - val_loss: 0.7015 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-04\n",
      "Epoch 2/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.7470 - accuracy: 0.6070 - top1_acc: 0.6070 - top5_acc: 1.0000\n",
      "Epoch 00002: val_accuracy did not improve from 0.54321\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.7470 - accuracy: 0.6070 - top1_acc: 0.6070 - top5_acc: 1.0000 - val_loss: 0.6948 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-04\n",
      "Epoch 3/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.7023 - accuracy: 0.6213 - top1_acc: 0.6213 - top5_acc: 1.0000\n",
      "Epoch 00003: val_accuracy improved from 0.54321 to 0.54733, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.7023 - accuracy: 0.6213 - top1_acc: 0.6213 - top5_acc: 1.0000 - val_loss: 0.6978 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-04\n",
      "Epoch 4/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.6226 - accuracy: 0.6725 - top1_acc: 0.6725 - top5_acc: 1.0000\n",
      "Epoch 00004: val_accuracy did not improve from 0.54733\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.6226 - accuracy: 0.6725 - top1_acc: 0.6725 - top5_acc: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-04\n",
      "Epoch 5/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.5617 - accuracy: 0.7124 - top1_acc: 0.7124 - top5_acc: 1.0000\n",
      "Epoch 00005: val_accuracy did not improve from 0.54733\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.5617 - accuracy: 0.7124 - top1_acc: 0.7124 - top5_acc: 1.0000 - val_loss: 0.7006 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-04\n",
      "Epoch 6/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.5630 - accuracy: 0.7073 - top1_acc: 0.7073 - top5_acc: 1.0000\n",
      "Epoch 00006: val_accuracy did not improve from 0.54733\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.5630 - accuracy: 0.7073 - top1_acc: 0.7073 - top5_acc: 1.0000 - val_loss: 0.6960 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-04\n",
      "Epoch 7/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.5514 - accuracy: 0.7165 - top1_acc: 0.7165 - top5_acc: 1.0000\n",
      "Epoch 00007: val_accuracy did not improve from 0.54733\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.5514 - accuracy: 0.7165 - top1_acc: 0.7165 - top5_acc: 1.0000 - val_loss: 0.6900 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-04\n",
      "Epoch 8/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.5193 - accuracy: 0.7482 - top1_acc: 0.7482 - top5_acc: 1.0000\n",
      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 0.54733\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.5193 - accuracy: 0.7482 - top1_acc: 0.7482 - top5_acc: 1.0000 - val_loss: 0.6879 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 9/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.5036 - accuracy: 0.7441 - top1_acc: 0.7441 - top5_acc: 1.0000\n",
      "Epoch 00009: val_accuracy did not improve from 0.54733\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.5036 - accuracy: 0.7441 - top1_acc: 0.7441 - top5_acc: 1.0000 - val_loss: 0.6943 - val_accuracy: 0.5144 - val_top1_acc: 0.5144 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 10/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4857 - accuracy: 0.7605 - top1_acc: 0.7605 - top5_acc: 1.0000\n",
      "Epoch 00010: val_accuracy did not improve from 0.54733\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4857 - accuracy: 0.7605 - top1_acc: 0.7605 - top5_acc: 1.0000 - val_loss: 0.7043 - val_accuracy: 0.5185 - val_top1_acc: 0.5185 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 11/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4699 - accuracy: 0.7748 - top1_acc: 0.7748 - top5_acc: 1.0000\n",
      "Epoch 00011: val_accuracy improved from 0.54733 to 0.57613, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4699 - accuracy: 0.7748 - top1_acc: 0.7748 - top5_acc: 1.0000 - val_loss: 0.6857 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 12/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4876 - accuracy: 0.7646 - top1_acc: 0.7646 - top5_acc: 1.0000\n",
      "Epoch 00012: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4876 - accuracy: 0.7646 - top1_acc: 0.7646 - top5_acc: 1.0000 - val_loss: 0.6970 - val_accuracy: 0.5226 - val_top1_acc: 0.5226 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 13/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4397 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000\n",
      "Epoch 00013: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4397 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000 - val_loss: 0.7062 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 14/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4570 - accuracy: 0.7779 - top1_acc: 0.7779 - top5_acc: 1.0000\n",
      "Epoch 00014: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4570 - accuracy: 0.7779 - top1_acc: 0.7779 - top5_acc: 1.0000 - val_loss: 0.7150 - val_accuracy: 0.5021 - val_top1_acc: 0.5021 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 15/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4587 - accuracy: 0.7820 - top1_acc: 0.7820 - top5_acc: 1.0000\n",
      "Epoch 00015: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4587 - accuracy: 0.7820 - top1_acc: 0.7820 - top5_acc: 1.0000 - val_loss: 0.7155 - val_accuracy: 0.5226 - val_top1_acc: 0.5226 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 16/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4452 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
      "Epoch 00016: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
      "\n",
      "Epoch 00016: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4452 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7078 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 17/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4542 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000\n",
      "Epoch 00017: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4542 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000 - val_loss: 0.7360 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 18/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4282 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
      "Epoch 00018: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4282 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7230 - val_accuracy: 0.4938 - val_top1_acc: 0.4938 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 19/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
      "Epoch 00019: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4227 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.7254 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 20/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4014 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
      "Epoch 00020: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4014 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.7454 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 21/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4401 - accuracy: 0.7892 - top1_acc: 0.7892 - top5_acc: 1.0000\n",
      "Epoch 00021: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
      "\n",
      "Epoch 00021: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4401 - accuracy: 0.7892 - top1_acc: 0.7892 - top5_acc: 1.0000 - val_loss: 0.7532 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 22/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4413 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000\n",
      "Epoch 00022: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4413 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000 - val_loss: 0.7500 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 23/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4212 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
      "Epoch 00023: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4212 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7354 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 24/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4280 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
      "Epoch 00024: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4280 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7625 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 25/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4185 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
      "Epoch 00025: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4185 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.7314 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 26/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4405 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000\n",
      "Epoch 00026: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
      "\n",
      "Epoch 00026: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4405 - accuracy: 0.7881 - top1_acc: 0.7881 - top5_acc: 1.0000 - val_loss: 0.7579 - val_accuracy: 0.5062 - val_top1_acc: 0.5062 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 27/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4327 - accuracy: 0.7922 - top1_acc: 0.7922 - top5_acc: 1.0000\n",
      "Epoch 00027: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4327 - accuracy: 0.7922 - top1_acc: 0.7922 - top5_acc: 1.0000 - val_loss: 0.7568 - val_accuracy: 0.5185 - val_top1_acc: 0.5185 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 28/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4133 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
      "Epoch 00028: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4133 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.7752 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 29/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4155 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
      "Epoch 00029: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4155 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.7434 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 30/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4042 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
      "Epoch 00030: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4042 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.7319 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 31/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4174 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
      "\n",
      "Epoch 00031: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4174 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7586 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 32/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3909 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000\n",
      "Epoch 00032: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.3909 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000 - val_loss: 0.7856 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 33/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4224 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
      "Epoch 00033: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4224 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.7467 - val_accuracy: 0.5391 - val_top1_acc: 0.5391 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 34/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4180 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
      "Epoch 00034: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4180 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.7897 - val_accuracy: 0.5309 - val_top1_acc: 0.5309 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 35/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
      "Epoch 00035: val_accuracy did not improve from 0.57613\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4165 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.7593 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 36/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4153 - accuracy: 0.7994 - top1_acc: 0.7994 - top5_acc: 1.0000\n",
      "Epoch 00036: val_accuracy improved from 0.57613 to 0.58025, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.4153 - accuracy: 0.7994 - top1_acc: 0.7994 - top5_acc: 1.0000 - val_loss: 0.7498 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 37/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4172 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
      "Epoch 00037: val_accuracy did not improve from 0.58025\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4172 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.7429 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 38/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4241 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
      "Epoch 00038: val_accuracy improved from 0.58025 to 0.60494, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4241 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.7420 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 39/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4132 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
      "Epoch 00039: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4132 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.7824 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 40/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4212 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
      "Epoch 00040: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4212 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.7775 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 41/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4015 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
      "Epoch 00041: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4015 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.7802 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 42/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4230 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
      "Epoch 00042: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4230 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.7686 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 43/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4070 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
      "Epoch 00043: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00043: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4070 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.7759 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 44/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4361 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
      "Epoch 00044: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4361 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7949 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 45/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3897 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
      "Epoch 00045: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.3897 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8084 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 46/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8260 - top1_acc: 0.8260 - top5_acc: 1.0000\n",
      "Epoch 00046: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8260 - top1_acc: 0.8260 - top5_acc: 1.0000 - val_loss: 0.7577 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 47/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4213 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
      "Epoch 00047: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4213 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7974 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 48/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4049 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
      "Epoch 00048: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00048: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4049 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.7643 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 49/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4162 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
      "Epoch 00049: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4162 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.7960 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 50/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4137 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
      "Epoch 00050: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4137 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8056 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 51/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4371 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
      "Epoch 00051: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4371 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7826 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 52/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4073 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
      "Epoch 00052: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4073 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7567 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 53/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4333 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
      "Epoch 00053: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00053: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4333 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.7879 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 54/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4300 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
      "Epoch 00054: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4300 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7981 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 55/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4194 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
      "Epoch 00055: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4194 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7958 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 56/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4072 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
      "Epoch 00056: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.4072 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.8397 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 57/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4341 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000\n",
      "Epoch 00057: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4341 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000 - val_loss: 0.8151 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 58/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4187 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
      "Epoch 00058: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00058: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4187 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.7572 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 59/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8352 - top1_acc: 0.8352 - top5_acc: 1.0000\n",
      "Epoch 00059: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3963 - accuracy: 0.8352 - top1_acc: 0.8352 - top5_acc: 1.0000 - val_loss: 0.7702 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 60/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4447 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000\n",
      "Epoch 00060: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4447 - accuracy: 0.7799 - top1_acc: 0.7799 - top5_acc: 1.0000 - val_loss: 0.7811 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 61/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4151 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
      "Epoch 00061: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4151 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8052 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 62/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
      "Epoch 00062: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4145 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.7830 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 63/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4266 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000\n",
      "Epoch 00063: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00063: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4266 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000 - val_loss: 0.7984 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 64/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4071 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
      "Epoch 00064: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4071 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.8233 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 65/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3957 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
      "Epoch 00065: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.3957 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.8013 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 66/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4045 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000\n",
      "Epoch 00066: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4045 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000 - val_loss: 0.8545 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 67/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4061 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
      "Epoch 00067: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4061 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.8196 - val_accuracy: 0.5473 - val_top1_acc: 0.5473 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 68/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4111 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
      "Epoch 00068: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00068: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4111 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7886 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 69/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4065 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
      "Epoch 00069: val_accuracy did not improve from 0.60494\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4065 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.8384 - val_accuracy: 0.5432 - val_top1_acc: 0.5432 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 70/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4313 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000\n",
      "Epoch 00070: val_accuracy improved from 0.60494 to 0.61728, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4313 - accuracy: 0.7984 - top1_acc: 0.7984 - top5_acc: 1.0000 - val_loss: 0.7754 - val_accuracy: 0.6173 - val_top1_acc: 0.6173 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 71/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4128 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
      "Epoch 00071: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4128 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.7718 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 72/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4139 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
      "Epoch 00072: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4139 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8216 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 73/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4000 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
      "Epoch 00073: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4000 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.8411 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 74/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4125 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
      "Epoch 00074: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4125 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.8011 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 75/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3860 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000\n",
      "Epoch 00075: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00075: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3860 - accuracy: 0.8301 - top1_acc: 0.8301 - top5_acc: 1.0000 - val_loss: 0.8244 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 76/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4269 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000\n",
      "Epoch 00076: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4269 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000 - val_loss: 0.8367 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 77/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
      "Epoch 00077: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8050 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 78/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
      "Epoch 00078: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4055 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8203 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 79/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4268 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
      "Epoch 00079: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4268 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8736 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 80/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4295 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
      "Epoch 00080: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00080: val_accuracy did not improve from 0.61728\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4295 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8296 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 81/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4161 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
      "Epoch 00081: val_accuracy improved from 0.61728 to 0.64198, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4161 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.7427 - val_accuracy: 0.6420 - val_top1_acc: 0.6420 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 82/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8291 - top1_acc: 0.8291 - top5_acc: 1.0000\n",
      "Epoch 00082: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4032 - accuracy: 0.8291 - top1_acc: 0.8291 - top5_acc: 1.0000 - val_loss: 0.7947 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 83/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4326 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000\n",
      "Epoch 00083: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4326 - accuracy: 0.7973 - top1_acc: 0.7973 - top5_acc: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 84/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4256 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000\n",
      "Epoch 00084: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4256 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000 - val_loss: 0.8390 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 85/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4141 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
      "Epoch 00085: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4141 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8190 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 86/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4214 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
      "Epoch 00086: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00086: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4214 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8395 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 87/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4311 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000\n",
      "Epoch 00087: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4311 - accuracy: 0.7902 - top1_acc: 0.7902 - top5_acc: 1.0000 - val_loss: 0.8246 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 88/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4257 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
      "Epoch 00088: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4257 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.8820 - val_accuracy: 0.5514 - val_top1_acc: 0.5514 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 89/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
      "Epoch 00089: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.3992 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8756 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 90/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4209 - accuracy: 0.8025 - top1_acc: 0.8025 - top5_acc: 1.0000\n",
      "Epoch 00090: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4209 - accuracy: 0.8025 - top1_acc: 0.8025 - top5_acc: 1.0000 - val_loss: 0.8621 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 91/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4325 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
      "Epoch 00091: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00091: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4325 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7532 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 92/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4083 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
      "Epoch 00092: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4083 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7819 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 93/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4089 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
      "Epoch 00093: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4089 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8260 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 94/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4044 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
      "Epoch 00094: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.4044 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8332 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 95/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4271 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000\n",
      "Epoch 00095: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4271 - accuracy: 0.8004 - top1_acc: 0.8004 - top5_acc: 1.0000 - val_loss: 0.8011 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 96/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4049 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
      "Epoch 00096: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00096: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4049 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8113 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 97/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4142 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
      "Epoch 00097: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4142 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8392 - val_accuracy: 0.5597 - val_top1_acc: 0.5597 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 98/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3947 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
      "Epoch 00098: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3947 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.8612 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 99/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4289 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
      "Epoch 00099: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4289 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.8127 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 100/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3977 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
      "Epoch 00100: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3977 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8109 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 101/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4250 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
      "Epoch 00101: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00101: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4250 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7745 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 102/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4169 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
      "Epoch 00102: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4169 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7935 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 103/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4098 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000\n",
      "Epoch 00103: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4098 - accuracy: 0.8117 - top1_acc: 0.8117 - top5_acc: 1.0000 - val_loss: 0.8652 - val_accuracy: 0.5267 - val_top1_acc: 0.5267 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 104/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4068 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
      "Epoch 00104: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4068 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8326 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 105/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
      "Epoch 00105: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4032 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8283 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 106/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4017 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
      "Epoch 00106: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00106: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4017 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8378 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 107/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3853 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000\n",
      "Epoch 00107: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3853 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000 - val_loss: 0.7966 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 108/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4340 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000\n",
      "Epoch 00108: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4340 - accuracy: 0.7871 - top1_acc: 0.7871 - top5_acc: 1.0000 - val_loss: 0.8355 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 109/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3952 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
      "Epoch 00109: val_accuracy did not improve from 0.64198\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3952 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.7637 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 110/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4181 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000\n",
      "Epoch 00110: val_accuracy improved from 0.64198 to 0.65432, saving model to landslide_classifier_efficientnet.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4181 - accuracy: 0.8096 - top1_acc: 0.8096 - top5_acc: 1.0000 - val_loss: 0.7650 - val_accuracy: 0.6543 - val_top1_acc: 0.6543 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 111/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4153 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000\n",
      "Epoch 00111: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4153 - accuracy: 0.8158 - top1_acc: 0.8158 - top5_acc: 1.0000 - val_loss: 0.8310 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 112/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
      "Epoch 00112: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.3963 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8353 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 113/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4266 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
      "Epoch 00113: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4266 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8709 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 114/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3953 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
      "Epoch 00114: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.3953 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.8202 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 115/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4040 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
      "Epoch 00115: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00115: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4040 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8301 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 116/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3972 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
      "Epoch 00116: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3972 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8246 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 117/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4072 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
      "Epoch 00117: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4072 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8309 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 118/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4291 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000\n",
      "Epoch 00118: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4291 - accuracy: 0.7932 - top1_acc: 0.7932 - top5_acc: 1.0000 - val_loss: 0.8027 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 119/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
      "Epoch 00119: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4145 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.7976 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 120/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4201 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000\n",
      "Epoch 00120: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00120: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4201 - accuracy: 0.8066 - top1_acc: 0.8066 - top5_acc: 1.0000 - val_loss: 0.8541 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 121/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3885 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000\n",
      "Epoch 00121: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3885 - accuracy: 0.8332 - top1_acc: 0.8332 - top5_acc: 1.0000 - val_loss: 0.8040 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 122/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4096 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
      "Epoch 00122: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4096 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8665 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 123/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
      "Epoch 00123: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8233 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 124/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4238 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000\n",
      "Epoch 00124: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4238 - accuracy: 0.8014 - top1_acc: 0.8014 - top5_acc: 1.0000 - val_loss: 0.8254 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 125/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4124 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000\n",
      "Epoch 00125: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00125: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4124 - accuracy: 0.8311 - top1_acc: 0.8311 - top5_acc: 1.0000 - val_loss: 0.8165 - val_accuracy: 0.5556 - val_top1_acc: 0.5556 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 126/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
      "Epoch 00126: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8247 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 127/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4161 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
      "Epoch 00127: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4161 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.8241 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 128/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.8342 - top1_acc: 0.8342 - top5_acc: 1.0000\n",
      "Epoch 00128: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3992 - accuracy: 0.8342 - top1_acc: 0.8342 - top5_acc: 1.0000 - val_loss: 0.8063 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 129/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3994 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
      "Epoch 00129: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3994 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7940 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 130/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4076 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
      "Epoch 00130: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00130: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4076 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8176 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 131/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3999 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
      "Epoch 00131: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.3999 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8128 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 132/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4064 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
      "Epoch 00132: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4064 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.7827 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 133/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4264 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
      "Epoch 00133: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4264 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8162 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 134/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4045 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
      "Epoch 00134: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4045 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.8722 - val_accuracy: 0.5926 - val_top1_acc: 0.5926 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 135/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
      "Epoch 00135: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00135: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4227 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.7998 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 136/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4397 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000\n",
      "Epoch 00136: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4397 - accuracy: 0.8045 - top1_acc: 0.8045 - top5_acc: 1.0000 - val_loss: 0.8207 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 137/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4199 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000\n",
      "Epoch 00137: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4199 - accuracy: 0.8127 - top1_acc: 0.8127 - top5_acc: 1.0000 - val_loss: 0.8432 - val_accuracy: 0.5720 - val_top1_acc: 0.5720 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 138/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4279 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000\n",
      "Epoch 00138: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4279 - accuracy: 0.7953 - top1_acc: 0.7953 - top5_acc: 1.0000 - val_loss: 0.7595 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 139/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4121 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000\n",
      "Epoch 00139: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4121 - accuracy: 0.8168 - top1_acc: 0.8168 - top5_acc: 1.0000 - val_loss: 0.8093 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 140/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4046 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000\n",
      "Epoch 00140: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00140: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4046 - accuracy: 0.8199 - top1_acc: 0.8199 - top5_acc: 1.0000 - val_loss: 0.8322 - val_accuracy: 0.5967 - val_top1_acc: 0.5967 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 141/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000\n",
      "Epoch 00141: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4204 - accuracy: 0.8086 - top1_acc: 0.8086 - top5_acc: 1.0000 - val_loss: 0.7741 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 142/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4293 - accuracy: 0.8035 - top1_acc: 0.8035 - top5_acc: 1.0000\n",
      "Epoch 00142: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4293 - accuracy: 0.8035 - top1_acc: 0.8035 - top5_acc: 1.0000 - val_loss: 0.8359 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 143/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4171 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000\n",
      "Epoch 00143: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4171 - accuracy: 0.8137 - top1_acc: 0.8137 - top5_acc: 1.0000 - val_loss: 0.8525 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 144/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4122 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
      "Epoch 00144: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4122 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8431 - val_accuracy: 0.5761 - val_top1_acc: 0.5761 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 145/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4032 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000\n",
      "Epoch 00145: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00145: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4032 - accuracy: 0.8219 - top1_acc: 0.8219 - top5_acc: 1.0000 - val_loss: 0.8754 - val_accuracy: 0.5350 - val_top1_acc: 0.5350 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 146/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3939 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000\n",
      "Epoch 00146: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3939 - accuracy: 0.8373 - top1_acc: 0.8373 - top5_acc: 1.0000 - val_loss: 0.7797 - val_accuracy: 0.5885 - val_top1_acc: 0.5885 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 147/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4003 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000\n",
      "Epoch 00147: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4003 - accuracy: 0.8147 - top1_acc: 0.8147 - top5_acc: 1.0000 - val_loss: 0.8439 - val_accuracy: 0.6132 - val_top1_acc: 0.6132 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 148/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4031 - accuracy: 0.8362 - top1_acc: 0.8362 - top5_acc: 1.0000\n",
      "Epoch 00148: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4031 - accuracy: 0.8362 - top1_acc: 0.8362 - top5_acc: 1.0000 - val_loss: 0.8480 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 149/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3908 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
      "Epoch 00149: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.3908 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8352 - val_accuracy: 0.5679 - val_top1_acc: 0.5679 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 150/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4011 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000\n",
      "Epoch 00150: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00150: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4011 - accuracy: 0.8280 - top1_acc: 0.8280 - top5_acc: 1.0000 - val_loss: 0.8256 - val_accuracy: 0.5802 - val_top1_acc: 0.5802 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 151/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000\n",
      "Epoch 00151: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4091 - accuracy: 0.8076 - top1_acc: 0.8076 - top5_acc: 1.0000 - val_loss: 0.8496 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 152/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3963 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
      "Epoch 00152: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3963 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8126 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 153/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4223 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000\n",
      "Epoch 00153: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4223 - accuracy: 0.8055 - top1_acc: 0.8055 - top5_acc: 1.0000 - val_loss: 0.8070 - val_accuracy: 0.6008 - val_top1_acc: 0.6008 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 154/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4055 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000\n",
      "Epoch 00154: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4055 - accuracy: 0.8229 - top1_acc: 0.8229 - top5_acc: 1.0000 - val_loss: 0.8222 - val_accuracy: 0.5844 - val_top1_acc: 0.5844 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 155/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3918 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000\n",
      "Epoch 00155: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00155: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3918 - accuracy: 0.8270 - top1_acc: 0.8270 - top5_acc: 1.0000 - val_loss: 0.8130 - val_accuracy: 0.6214 - val_top1_acc: 0.6214 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 156/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4041 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000\n",
      "Epoch 00156: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.4041 - accuracy: 0.8188 - top1_acc: 0.8188 - top5_acc: 1.0000 - val_loss: 0.8405 - val_accuracy: 0.6091 - val_top1_acc: 0.6091 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 157/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4024 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000\n",
      "Epoch 00157: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4024 - accuracy: 0.8106 - top1_acc: 0.8106 - top5_acc: 1.0000 - val_loss: 0.8030 - val_accuracy: 0.6049 - val_top1_acc: 0.6049 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 158/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4081 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000\n",
      "Epoch 00158: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4081 - accuracy: 0.8178 - top1_acc: 0.8178 - top5_acc: 1.0000 - val_loss: 0.7719 - val_accuracy: 0.6255 - val_top1_acc: 0.6255 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 159/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4029 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000\n",
      "Epoch 00159: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4029 - accuracy: 0.8250 - top1_acc: 0.8250 - top5_acc: 1.0000 - val_loss: 0.7738 - val_accuracy: 0.6255 - val_top1_acc: 0.6255 - val_top5_acc: 1.0000\n",
      "Learning rate:  1e-07\n",
      "Epoch 160/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3936 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000\n",
      "Epoch 00160: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "Restoring model weights from the end of the best epoch.\n",
      "\n",
      "Epoch 00160: val_accuracy did not improve from 0.65432\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.3936 - accuracy: 0.8209 - top1_acc: 0.8209 - top5_acc: 1.0000 - val_loss: 0.8669 - val_accuracy: 0.5638 - val_top1_acc: 0.5638 - val_top5_acc: 1.0000\n",
      "Epoch 00160: early stopping\n",
      "CPU times: user 1h 14min 8s, sys: 6min 51s, total: 1h 20min 59s\n",
      "Wall time: 55min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "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:  6\n",
      "./img_data/resized/Negative/3837558_landsat_8_rgb.tif\n",
      "0.6771707   :   (0, 'Negative')\n",
      "0.32282928   :   (1, 'Positive')\n"
     ]
    },
    {
     "data": {
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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": [
      "0.6771707   :   (0, 'Negative')\n",
      "0.32282928   :   (1, 'Positive')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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       0.57      0.58      0.57       122\n",
      "    Positive       0.57      0.55      0.56       121\n",
      "\n",
      "    accuracy                           0.57       243\n",
      "   macro avg       0.57      0.57      0.57       243\n",
      "weighted avg       0.57      0.57      0.57       243\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_generator.reset()\n",
    "predictions = model.predict_generator(test_generator, steps=len(test_generator))\n",
    "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 0x7f966c6f5690>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x5760 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix')\n",
    "cm = confusion_matrix(test_generator.classes, y)\n",
    "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "functools.partial(<function top_k_categorical_accuracy at 0x7fac570aa200>, k=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(top1_acc)\n",
    "plt.title('Top accuracy')\n",
    "plt.plot(params.epoch, params.history['top1_acc'], label='Top-1 accuracy')\n",
    "plt.plot(params.epoch, params.history['top5_acc'], label='Top-5 accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "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
}