train-mobilenetv2.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": [
       "['Sun Sep  6 03:44:54 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%   50C    P8    15W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   1  Quadro RTX 4000     Off  | 00000000:1E:00.0 Off |                  N/A |',\n",
       " '| 30%   43C    P8    11W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   2  Quadro RTX 4000     Off  | 00000000:1F:00.0 Off |                  N/A |',\n",
       " '| 30%   40C    P8     4W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   3  Quadro RTX 4000     Off  | 00000000:20:00.0 Off |                  N/A |',\n",
       " '| 30%   45C    P8    18W / 125W |      0MiB /  7982MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   4  GeForce GTX 108...  Off  | 00000000:21:00.0 Off |                  N/A |',\n",
       " '| 24%   34C    P5    12W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   5  GeForce GTX 108...  Off  | 00000000:22:00.0 Off |                  N/A |',\n",
       " '| 20%   28C    P8     9W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   6  GeForce GTX 108...  Off  | 00000000:23:00.0 Off |                  N/A |',\n",
       " '| 20%   32C    P8     8W / 250W |      0MiB / 11178MiB |      0%      Default |',\n",
       " '+-------------------------------+----------------------+----------------------+',\n",
       " '|   7  GeForce GTX 108...  Off  | 00000000:24:00.0 Off |                  N/A |',\n",
       " '| 20%   31C    P8     8W / 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",
    "from imutils import paths\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.models import *\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.optimizers import *\n",
    "from tensorflow.keras.utils import *\n",
    "from tensorflow.keras.callbacks import *\n",
    "from tensorflow.keras.initializers import *\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sn\n",
    "\n",
    "from PIL import Image\n",
    "import xml.etree.ElementTree as ET\n",
    "import psutil\n",
    "import random\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"py\", python_version())\n",
    "print(\"tf\", tf.__version__)\n",
    "print(\"keras\", tf.keras.__version__)\n",
    "mem = psutil.virtual_memory()\n",
    "print(\"mem\", mem.total/1024/1024)\n",
    "cpu = mp.cpu_count()\n",
    "print(\"cpu\", cpu)\n",
    "\n",
    "%system nvidia-smi\n",
    "#%system rocm-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 250\n",
    "batch_size = 150\n",
    "testsplit = .2\n",
    "targetx = 224\n",
    "targety = 224\n",
    "learning_rate = 0.0001\n",
    "classes = 2\n",
    "seed = random.randint(1, 1000)\n",
    "\n",
    "data_dir = \"../LandslideDataset/\"\n",
    "resized_dir = \"./img_data/resized/\"\n",
    "test_dir = \"./img_data/test/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crop images using provided annotations"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "%system rm -rf $resized_dir\n",
    "%system mkdir $resized_dir\n",
    "\n",
    "\n",
    "def crop_and_save_img(img_path, new_dir_path, original_ext_filter = 0):\n",
    "#     print('trongan93:', new_dir_path)\n",
    "    image = Image.open(img_path)\n",
    "    image = image.resize((targetx,targety))\n",
    "    image.save(new_dir_path)\n",
    "\n",
    "def resize_images(ext = 0):\n",
    "    imgPaths = sorted(list(paths.list_images(data_dir)))\n",
    "#     print('images len: ', len(imgPaths))\n",
    "    \n",
    "    labels_dir = os.listdir(data_dir)\n",
    "    for label_dir in labels_dir:\n",
    "        cropper_label_dir = resized_dir+label_dir\n",
    "        if not os.path.exists(cropper_label_dir):\n",
    "            os.makedirs(cropper_label_dir)\n",
    "            print(\"sucessfull make dir for label: \", cropper_label_dir)\n",
    "    \n",
    "    \n",
    "    total_images = 0\n",
    "\n",
    "    for imgPath in imgPaths:\n",
    "#         print(imgPath)\n",
    "        img_name_info = imgPath.split(os.path.sep)\n",
    "        imgLabel = img_name_info[2]\n",
    "#         print('img label: ', imgLabel)\n",
    "        imgName = img_name_info[3]\n",
    "#         print('img name: ', imgName)\n",
    "        file_ext = os.path.splitext(imgName)[1]\n",
    "#         print('img ext: ', file_ext)\n",
    "        \n",
    "        new_path = resized_dir + imgLabel + '/' + imgName\n",
    "#         print(new_path)\n",
    "        if ext == 0:\n",
    "            crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
    "        elif ext == 1:\n",
    "            # TIF file\n",
    "            if file_ext == '.TIF':\n",
    "                crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
    "            else:\n",
    "                break\n",
    "        elif ext == 2:\n",
    "            # TIF file\n",
    "            if file_ext == '.RAW':\n",
    "                crop_and_save_img(imgPath,new_path, original_ext_filter = 0)\n",
    "            else:\n",
    "                break\n",
    "                \n",
    "        total_images += 1\n",
    "        \n",
    "#         annotations_dir_list = os.listdir(annotations_dir + breed)\n",
    "#         img_list = [data_dir + breed + '/' + i for i in dir_list]\n",
    "#         os.makedirs(cropped_dir + breed)\n",
    "\n",
    "#         for file in img_list:\n",
    "#             annotation_path = annotations_dir + breed + '/' + os.path.basename(file[:-4])\n",
    "#             newpath = cropped_dir + breed + '/' + os.path.basename(file)\n",
    "#             save_cropped_img(file, annotation_path, newpath)\n",
    "#             total_images += 1\n",
    "    \n",
    "    print(\"total images resized\", total_images)\n",
    "\n",
    "resize_images()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keras image data readers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 977 images belonging to 2 classes.\n",
      "Found 243 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        shear_range=0.1,\n",
    "        zoom_range=0.1,\n",
    "        brightness_range=[0.9,1.1],\n",
    "        horizontal_flip=True,\n",
    "        validation_split=testsplit,\n",
    "        preprocessing_function=preprocess_input\n",
    ")\n",
    "\n",
    "train_generator = datagen.flow_from_directory(\n",
    "        resized_dir,\n",
    "        target_size=(targetx, targety),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical',\n",
    "        shuffle=True,\n",
    "        seed=seed,\n",
    "        subset=\"training\"\n",
    ")\n",
    "\n",
    "test_generator = datagen.flow_from_directory(\n",
    "        resized_dir,\n",
    "        target_size=(targetx, targety),\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical',\n",
    "        shuffle=False,\n",
    "        seed=seed,\n",
    "        subset=\"validation\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./img_data/resized/Positive/3838780_landsat_8_rgb.tif\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fb6c4e57890>"
      ]
     },
     "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.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",
      "Conv1_pad (ZeroPadding2D)       (None, 225, 225, 3)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 112, 112, 32) 128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 112, 112, 32) 0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 112, 112, 32) 288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 112, 112, 32) 128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 112, 112, 32) 0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 112, 112, 16) 512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 112, 112, 16) 64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 112, 112, 96) 1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 112, 112, 96) 384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 112, 112, 96) 0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 113, 113, 96) 0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 56, 56, 96)   864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 56, 56, 96)   384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 56, 56, 96)   0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 56, 56, 24)   2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 56, 56, 24)   96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 56, 56, 144)  3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 56, 56, 144)  1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 56, 56, 144)  576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 56, 56, 144)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 56, 56, 24)   3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 56, 56, 24)   96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 56, 56, 24)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 56, 56, 144)  3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 56, 56, 144)  576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 56, 56, 144)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 57, 57, 144)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 28, 28, 144)  1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 28, 28, 144)  576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 28, 28, 144)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 28, 28, 32)   4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 28, 28, 32)   128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 28, 28, 192)  6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 28, 28, 32)   6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 28, 28, 32)   128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 28, 28, 32)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 28, 28, 192)  6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 28, 28, 192)  1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 28, 28, 192)  768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 28, 28, 192)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 28, 28, 32)   6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 28, 28, 32)   128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 28, 28, 32)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 28, 28, 192)  6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 28, 28, 192)  768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 28, 28, 192)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 29, 29, 192)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 14, 14, 192)  1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 14, 14, 192)  768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 14, 14, 192)  0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 14, 14, 64)   12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 14, 14, 64)   256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 14, 14, 384)  24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 14, 14, 64)   24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 14, 14, 64)   256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 14, 14, 64)   0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 14, 14, 384)  24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 14, 14, 64)   24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 14, 14, 64)   256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 14, 14, 64)   0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 14, 14, 384)  24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 14, 14, 384)  1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 14, 14, 384)  0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 14, 14, 384)  3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 14, 14, 384)  1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 14, 14, 384)  0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 14, 14, 64)   24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 14, 14, 64)   256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 14, 14, 64)   0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 14, 14, 384)  24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 14, 14, 384)  1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 14, 14, 384)  0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 14, 14, 384)  3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 14, 14, 384)  1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 14, 14, 384)  0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 14, 14, 96)   36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 14, 14, 96)   384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 14, 14, 576)  55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 14, 14, 96)   55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 14, 14, 96)   384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 14, 14, 96)   0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 14, 14, 576)  55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 14, 14, 576)  5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 14, 14, 576)  2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 14, 14, 576)  0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 14, 14, 96)   55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 14, 14, 96)   384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 14, 14, 96)   0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 14, 14, 576)  55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 14, 14, 576)  2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 14, 14, 576)  0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 15, 15, 576)  0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 7, 7, 576)    5184        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 7, 7, 576)    2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 7, 7, 576)    0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 7, 7, 160)    92160       block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 7, 7, 160)    640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 7, 7, 960)    153600      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 7, 7, 160)    153600      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 7, 7, 160)    640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 7, 7, 160)    0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 7, 7, 960)    153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 7, 7, 160)    153600      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 7, 7, 160)    640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 7, 7, 160)    0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 7, 7, 960)    153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 7, 7, 960)    3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 7, 7, 960)    0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 7, 7, 960)    8640        block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 7, 7, 960)    3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 7, 7, 960)    0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 7, 7, 320)    307200      block_16_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, 7, 7, 320)    1280        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 7, 7, 1280)   409600      block_16_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalization)  (None, 7, 7, 1280)   5120        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 7, 7, 1280)   0           Conv_1_bn[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 1280)         0           out_relu[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: 3,910,466\n",
      "Trainable params: 3,871,234\n",
      "Non-trainable params: 39,232\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\", \"top_k_categorical_accuracy\", tf.keras.metrics.MeanIoU(num_classes=2)])\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: 164 all-reduces with algorithm = nccl, num_packs = 1\n",
      "INFO:tensorflow:batch_all_reduce: 164 all-reduces with algorithm = nccl, num_packs = 1\n",
      "1/7 [===>..........................] - ETA: 0s - loss: 0.9579 - accuracy: 0.5200 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500WARNING:tensorflow:From /home/jupyter_user/anaconda3/envs/viplab-gpu/lib/python3.7/site-packages/tensorflow/python/ops/summary_ops_v2.py:1277: stop (from tensorflow.python.eager.profiler) is deprecated and will be removed after 2020-07-01.\n",
      "Instructions for updating:\n",
      "use `tf.profiler.experimental.stop` instead.\n",
      "2/7 [=======>......................] - ETA: 2s - loss: 0.9027 - accuracy: 0.5067 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.2517s vs `on_train_batch_end` time: 0.5751s). Check your callbacks.\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.9008 - accuracy: 0.5087 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.53086, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 25s 4s/step - loss: 0.9008 - accuracy: 0.5087 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7172 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 2/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.7596 - accuracy: 0.6018 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00002: val_accuracy improved from 0.53086 to 0.55556, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.7596 - accuracy: 0.6018 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7029 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 3/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.6723 - accuracy: 0.6325 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00003: val_accuracy did not improve from 0.55556\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.6723 - accuracy: 0.6325 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7010 - val_accuracy: 0.5185 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 4/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.6027 - accuracy: 0.6909 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00004: val_accuracy did not improve from 0.55556\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.6027 - accuracy: 0.6909 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7228 - val_accuracy: 0.5144 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 5/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.5557 - accuracy: 0.7083 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00005: val_accuracy improved from 0.55556 to 0.56379, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.5557 - accuracy: 0.7083 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7036 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 6/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.5393 - accuracy: 0.7329 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00006: val_accuracy did not improve from 0.56379\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.5393 - accuracy: 0.7329 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7225 - val_accuracy: 0.4979 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 7/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4988 - accuracy: 0.7574 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00007: val_accuracy improved from 0.56379 to 0.58025, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4988 - accuracy: 0.7574 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7127 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 8/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4578 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00008: val_accuracy did not improve from 0.58025\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.4578 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7574 - val_accuracy: 0.5021 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 9/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4561 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00009: val_accuracy improved from 0.58025 to 0.60082, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.4561 - accuracy: 0.7902 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7078 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 10/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.4322 - accuracy: 0.8004 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00010: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.4322 - accuracy: 0.8004 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7696 - val_accuracy: 0.5350 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 11/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3810 - accuracy: 0.8229 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00011: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3810 - accuracy: 0.8229 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7463 - val_accuracy: 0.5391 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 12/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3898 - accuracy: 0.8332 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00012: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3898 - accuracy: 0.8332 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7329 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 13/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3554 - accuracy: 0.8567 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00013: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3554 - accuracy: 0.8567 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7269 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-04\n",
      "Epoch 14/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3270 - accuracy: 0.8608 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 3.1622775802825264e-05.\n",
      "\n",
      "Epoch 00014: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3270 - accuracy: 0.8608 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7890 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 15/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3290 - accuracy: 0.8557 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00015: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3290 - accuracy: 0.8557 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7621 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 16/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2933 - accuracy: 0.8782 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00016: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2933 - accuracy: 0.8782 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7800 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 17/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.3225 - accuracy: 0.8741 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00017: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.3225 - accuracy: 0.8741 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7712 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 18/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2935 - accuracy: 0.8762 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00018: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2935 - accuracy: 0.8762 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7659 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-05\n",
      "Epoch 19/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2720 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 9.999999259090306e-06.\n",
      "\n",
      "Epoch 00019: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2720 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7624 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 20/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2723 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00020: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2723 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7938 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 21/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2831 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00021: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2831 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8037 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 22/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2909 - accuracy: 0.8772 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00022: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2909 - accuracy: 0.8772 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7662 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 23/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2655 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00023: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2655 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7987 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-06\n",
      "Epoch 24/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2466 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 3.162277292675049e-06.\n",
      "\n",
      "Epoch 00024: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2466 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7649 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 25/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2553 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00025: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2553 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8101 - val_accuracy: 0.5473 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 26/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2549 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00026: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2549 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8154 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 27/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2751 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00027: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2751 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8256 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 28/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2848 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00028: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2848 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7880 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622774e-06\n",
      "Epoch 29/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00029: ReduceLROnPlateau reducing learning rate to 9.999999115286567e-07.\n",
      "\n",
      "Epoch 00029: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2691 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8155 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 30/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00030: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2677 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8631 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 31/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2532 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00031: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.2532 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8359 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 32/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2851 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00032: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2851 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8274 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 33/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2681 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00033: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2681 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8248 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  9.999999e-07\n",
      "Epoch 34/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2566 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00034: ReduceLROnPlateau reducing learning rate to 3.1622772926750485e-07.\n",
      "\n",
      "Epoch 00034: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2566 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8946 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 35/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2848 - accuracy: 0.8792 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00035: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2848 - accuracy: 0.8792 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8544 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 36/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2602 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00036: val_accuracy did not improve from 0.60082\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.2602 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9250 - val_accuracy: 0.5267 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 37/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2454 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00037: val_accuracy improved from 0.60082 to 0.61317, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2454 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8472 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 38/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2681 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00038: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2681 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8657 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 39/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2700 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00039: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2700 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8675 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 40/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2498 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00040: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2498 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9167 - val_accuracy: 0.5309 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 41/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2834 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00041: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 20s 3s/step - loss: 0.2834 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8890 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  3.1622773e-07\n",
      "Epoch 42/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2431 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00042: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00042: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2431 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8516 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 43/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2744 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00043: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2744 - accuracy: 0.8823 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9049 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 44/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2485 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00044: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2485 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9200 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 45/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2621 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00045: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2621 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8810 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 46/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2686 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00046: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2686 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9243 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 47/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2483 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00047: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00047: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2483 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8165 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 48/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2606 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00048: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2606 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8416 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 49/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2521 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00049: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2521 - accuracy: 0.9089 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8861 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 50/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2644 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00050: val_accuracy did not improve from 0.61317\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2644 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8572 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 51/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2575 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00051: val_accuracy improved from 0.61317 to 0.62551, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2575 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9291 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 52/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2404 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00052: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2404 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8885 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 53/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2611 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00053: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2611 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8284 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 54/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2821 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00054: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2821 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8625 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 55/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00055: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2465 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8588 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 56/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2387 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00056: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00056: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2387 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8780 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 57/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00057: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2561 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8342 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 58/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2373 - accuracy: 0.9181 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00058: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2373 - accuracy: 0.9181 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8639 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 59/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2502 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00059: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2502 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9016 - val_accuracy: 0.5679 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 60/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2665 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00060: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2665 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8504 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 61/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2498 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00061: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00061: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2498 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8339 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 62/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2538 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00062: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2538 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8285 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 63/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00063: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2561 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8794 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 64/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2461 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00064: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2461 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8995 - val_accuracy: 0.5473 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 65/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2630 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00065: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2630 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9266 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 66/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2652 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00066: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00066: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2652 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9469 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 67/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2535 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00067: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2535 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8645 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 68/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2710 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00068: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2710 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8442 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 69/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2524 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00069: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2524 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8830 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 70/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2496 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00070: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2496 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8736 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 71/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2547 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00071: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00071: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2547 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9161 - val_accuracy: 0.5556 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 72/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2770 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00072: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.2770 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8405 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 73/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2588 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00073: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2588 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8699 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 74/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2712 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00074: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2712 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9141 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 75/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2478 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00075: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2478 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9195 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 76/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2534 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00076: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00076: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2534 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8758 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 77/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2490 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00077: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2490 - accuracy: 0.9028 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8574 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 78/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2579 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00078: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2579 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8573 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 79/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2362 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00079: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2362 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8933 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 80/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2506 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00080: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2506 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8688 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 81/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2462 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00081: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00081: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2462 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9496 - val_accuracy: 0.5802 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 82/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2467 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00082: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2467 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9060 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 83/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2609 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00083: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2609 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8878 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 84/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2333 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00084: val_accuracy did not improve from 0.62551\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2333 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8473 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 85/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2428 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00085: val_accuracy improved from 0.62551 to 0.67490, saving model to landslide_classifier.h5\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.2428 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.7651 - val_accuracy: 0.6749 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 86/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2787 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00086: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2787 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8720 - val_accuracy: 0.5514 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 87/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2600 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00087: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2600 - accuracy: 0.9099 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9121 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 88/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2557 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00088: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2557 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8430 - val_accuracy: 0.6584 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 89/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2465 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00089: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2465 - accuracy: 0.9150 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9231 - val_accuracy: 0.5638 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 90/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2845 - accuracy: 0.8813 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00090: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00090: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2845 - accuracy: 0.8813 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8547 - val_accuracy: 0.5761 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 91/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2529 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00091: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.2529 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8629 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 92/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2735 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00092: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2735 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8381 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 93/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2427 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00093: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2427 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8898 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 94/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2581 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00094: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2581 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8540 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 95/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2364 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00095: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00095: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2364 - accuracy: 0.9110 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8577 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 96/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2671 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00096: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2671 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8913 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 97/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2411 - accuracy: 0.9130 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00097: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2411 - accuracy: 0.9130 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8336 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 98/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2513 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00098: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2513 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8305 - val_accuracy: 0.6379 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 99/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2472 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00099: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2472 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8513 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 100/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2588 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00100: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00100: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2588 - accuracy: 0.8997 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8374 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 101/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2677 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00101: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2677 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8531 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 102/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2635 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00102: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2635 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8723 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 103/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2688 - accuracy: 0.8936 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00103: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 19s 3s/step - loss: 0.2688 - accuracy: 0.8936 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8420 - val_accuracy: 0.6543 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 104/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2489 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00104: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2489 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9124 - val_accuracy: 0.5885 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 105/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2649 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00105: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00105: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2649 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9089 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 106/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2654 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00106: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2654 - accuracy: 0.8976 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8723 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 107/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2536 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00107: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2536 - accuracy: 0.8966 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9203 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 108/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2511 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00108: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2511 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8540 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 109/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2493 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00109: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2493 - accuracy: 0.8956 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8497 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 110/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2351 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00110: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00110: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2351 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8849 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 111/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2592 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00111: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2592 - accuracy: 0.9038 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8466 - val_accuracy: 0.5926 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 112/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2705 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00112: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2705 - accuracy: 0.8895 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9399 - val_accuracy: 0.5967 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 113/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2710 - accuracy: 0.8864 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00113: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2710 - accuracy: 0.8864 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8628 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 114/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2526 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00114: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2526 - accuracy: 0.9007 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9157 - val_accuracy: 0.5432 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 115/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2657 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00115: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00115: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2657 - accuracy: 0.8987 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8460 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 116/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2513 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00116: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2513 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9235 - val_accuracy: 0.5597 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 117/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2696 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00117: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2696 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8443 - val_accuracy: 0.5720 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 118/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2493 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00118: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2493 - accuracy: 0.9079 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8471 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 119/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2434 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00119: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2434 - accuracy: 0.9120 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8549 - val_accuracy: 0.6132 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 120/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2296 - accuracy: 0.9222 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00120: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00120: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2296 - accuracy: 0.9222 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8200 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 121/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2687 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00121: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2687 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9070 - val_accuracy: 0.5844 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 122/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2371 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00122: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2371 - accuracy: 0.9058 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8041 - val_accuracy: 0.6379 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 123/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2795 - accuracy: 0.8802 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00123: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2795 - accuracy: 0.8802 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8686 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 124/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2578 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00124: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2578 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8294 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 125/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00125: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00125: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2691 - accuracy: 0.9017 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8685 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 126/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2678 - accuracy: 0.9069 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00126: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2678 - accuracy: 0.9069 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9145 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 127/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2786 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00127: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2786 - accuracy: 0.8884 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8757 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 128/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2633 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00128: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2633 - accuracy: 0.8946 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8155 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 129/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2695 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00129: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2695 - accuracy: 0.8874 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8042 - val_accuracy: 0.6296 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 130/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.9161 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00130: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "\n",
      "Epoch 00130: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2287 - accuracy: 0.9161 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8985 - val_accuracy: 0.6008 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 131/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2657 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00131: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 17s 2s/step - loss: 0.2657 - accuracy: 0.8915 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.9389 - val_accuracy: 0.6214 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 132/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2565 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00132: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2565 - accuracy: 0.9048 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8627 - val_accuracy: 0.6255 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 133/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2487 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00133: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 18s 3s/step - loss: 0.2487 - accuracy: 0.9140 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8624 - val_accuracy: 0.6049 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 134/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2576 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00134: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 16s 2s/step - loss: 0.2576 - accuracy: 0.8905 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8436 - val_accuracy: 0.6173 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Learning rate:  1e-07\n",
      "Epoch 135/250\n",
      "7/7 [==============================] - ETA: 0s - loss: 0.2692 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500\n",
      "Epoch 00135: ReduceLROnPlateau reducing learning rate to 1e-07.\n",
      "Restoring model weights from the end of the best epoch.\n",
      "\n",
      "Epoch 00135: val_accuracy did not improve from 0.67490\n",
      "7/7 [==============================] - 20s 3s/step - loss: 0.2692 - accuracy: 0.8925 - top_k_categorical_accuracy: 1.0000 - mean_io_u: 0.2500 - val_loss: 0.8920 - val_accuracy: 0.6091 - val_top_k_categorical_accuracy: 1.0000 - val_mean_io_u: 0.2500\n",
      "Epoch 00135: early stopping\n",
      "CPU times: user 57min 39s, sys: 5min 37s, total: 1h 3min 17s\n",
      "Wall time: 46min 24s\n"
     ]
    }
   ],
   "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": 14,
   "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.plot(params.epoch, params.history['top_k_categorical_accuracy'], label='Top-5 accuracy')\n",
    "plt.plot(params.epoch, params.history['mean_io_u'], label='Mean IoU') #tf.keras.metrics.Mean IoU\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict with test number:  23\n",
      "./img_data/resized/Negative/3837676_sentinel_2_rgb.tif\n",
      "0.8859837   :   (1, 'Positive')\n",
      "0.11401627   :   (0, 'Negative')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8859837   :   (1, 'Positive')\n",
      "0.11401627   :   (0, 'Negative')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# path for landslide \n",
    "#./data/reized/WithLandslide/25_94_Large_cropped_6.TIF\n",
    "# ./data/reized/WithLandslide/44_-123_Large_cropped_4.TIF\n",
    "# ./imgtest/27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20130915_20170502_01_T1.TIF\n",
    "# ./imgtest/27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20140918_20170419_01_T1.TIF\n",
    "img_test = Image.open(test_dir + \"27.770733_85.868467_VERY_LARGE_cropped_LC08_L1TP_141041_20140918_20170419_01_T1.TIF\").resize((targetx, targety))\n",
    "plt.imshow(img_test,origin='lower')\n",
    "probabilities = model.predict(preprocess_input(np.expand_dims(img, axis=0)))\n",
    "breed_list = tuple(zip(test_generator.class_indices.values(), test_generator.class_indices.keys()))\n",
    "\n",
    "for i in probabilities[0].argsort()[-5:][::-1]: \n",
    "    print(probabilities[0][i], \"  :  \" , breed_list[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.60      0.60      0.60       122\n",
      "    Positive       0.60      0.60      0.60       121\n",
      "\n",
      "    accuracy                           0.60       243\n",
      "   macro avg       0.60      0.60      0.60       243\n",
      "weighted avg       0.60      0.60      0.60       243\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_generator.reset()\n",
    "predictions = model.predict_generator(test_generator, steps=len(test_generator))\n",
    "y = np.argmax(predictions, axis=1)\n",
    "\n",
    "print('Classification Report')\n",
    "cr = classification_report(y_true=test_generator.classes, y_pred=y, target_names=test_generator.class_indices)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1a6f23e50>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x5760 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Confusion Matrix')\n",
    "cm = confusion_matrix(test_generator.classes, y)\n",
    "df = pd.DataFrame(cm, columns=test_generator.class_indices)\n",
    "plt.figure(figsize=(80,80))\n",
    "sn.heatmap(df, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: \n",
    "## Transfer learning with Keras and MobileNet V2\n",
    "#### https://www.kaggle.com/devang/transfer-learning-with-keras-and-mobilenet-v2/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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