image-scene-detection.ipynb

{
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     "exception": false,
     "start_time": "2021-02-27T11:13:18.699724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__notebook__.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "ls"
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  {
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   "metadata": {
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "--2021-02-27 11:13:20--  http://data.vision.ee.ethz.ch/ihnatova/camera_scene_detection_train.zip\r\n",
      "Resolving data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)... 129.132.52.178, 2001:67c:10ec:36c2::178\r\n",
      "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.178|:80... connected.\r\n",
      "HTTP request sent, awaiting response... 302 Found\r\n",
      "Location: https://data.vision.ee.ethz.ch/ihnatova/camera_scene_detection_train.zip [following]\r\n",
      "--2021-02-27 11:13:20--  https://data.vision.ee.ethz.ch/ihnatova/camera_scene_detection_train.zip\r\n",
      "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.178|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 333074849 (318M) [application/zip]\r\n",
      "Saving to: ‘camera_scene_detection_train.zip’\r\n",
      "\r\n",
      "camera_scene_detect 100%[===================>] 317.64M  26.9MB/s    in 13s     \r\n",
      "\r\n",
      "2021-02-27 11:13:34 (24.8 MB/s) - ‘camera_scene_detection_train.zip’ saved [333074849/333074849]\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!wget http://data.vision.ee.ethz.ch/ihnatova/camera_scene_detection_train.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "iopub.status.idle": "2021-02-27T11:13:39.434122Z",
     "shell.execute_reply": "2021-02-27T11:13:39.433457Z"
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     "duration": 5.20205,
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     "exception": false,
     "start_time": "2021-02-27T11:13:34.232242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  camera_scene_detection_train.zip\r\n",
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     ]
    }
   ],
   "source": [
    "!unzip  camera_scene_detection_train.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:39.609354Z",
     "iopub.status.busy": "2021-02-27T11:13:39.608454Z",
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     "shell.execute_reply": "2021-02-27T11:13:46.581601Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as matimg\n",
    "import seaborn as sns\n",
    "import os, random,cv2, json, itertools\n",
    "\n",
    "from IPython.display import SVG\n",
    "from tensorflow.keras.utils import plot_model, model_to_dot\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tqdm import tqdm\n",
    "\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import (Add, Input, Conv2D, Dropout, Activation, BatchNormalization, \n",
    "                                    MaxPooling2D, ZeroPadding2D, AveragePooling2D, Flatten, Dense)\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint, Callback\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.initializers import *\n",
    "\n",
    "# from tensorflow.keras.applications.mobilenet import MobileNet\n",
    "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\n",
    "# from tensorflow.keras.applications.mobilenet import preprocess_input\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import img_to_array, load_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:46.754978Z",
     "iopub.status.busy": "2021-02-27T11:13:46.753917Z",
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     "shell.execute_reply": "2021-02-27T11:13:46.758522Z"
    },
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     "duration": 0.093904,
     "end_time": "2021-02-27T11:13:46.759276",
     "exception": false,
     "start_time": "2021-02-27T11:13:46.665372",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['20_Flower', '27_Backlight', '3_Kids', '14_Architecture', '7_Food', '18_Greenery', '29_QR_images', '19_Autumn_leaves', '15_Sunset_Sunrise', '24_Candle_light', '2_Group_portrait', '17_Cloudy_Sky', '28_Text_Documents', '6_Macro', '30_Computer_Screens', '12_Landscape', '11_Snow', '4_Dog', '25_Neon_lights', '22_Stage_concert', '9_Mountain', '10_Waterfall', '21_Night_shot', '16_Blue_Sky', '8_Beach', '13_Underwater', '5_Cat', '26_Indoor', '1_Portrait', '23_Fireworks']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir(\"./training\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:46.934214Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dir = \"./training/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:47.115731Z",
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     "shell.execute_reply": "2021-02-27T11:13:47.117642Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# def load_unique():    \n",
    "#     size_img = 576,384 \n",
    "#     images_for_plot = []\n",
    "#     labels_for_plot = []\n",
    "#     for folder in os.listdir(train_dir):\n",
    "#         for file in os.listdir(train_dir + '/' + folder):\n",
    "#             filepath = train_dir + '/' + folder + '/' + file\n",
    "#             image = matimg.imread(filepath)\n",
    "#             images_for_plot.append(image)\n",
    "#             labels_for_plot.append(folder)\n",
    "#             break\n",
    "#     return images_for_plot, labels_for_plot\n",
    "\n",
    "# images_for_plot, labels_for_plot = load_unique()\n",
    "# print(\"unique_labels = \", labels_for_plot)\n",
    "\n",
    "# fig = plt.figure(figsize = (15,15))\n",
    "# def plot_images(fig, image, label, row, col, index):\n",
    "#     fig.add_subplot(row, col, index)\n",
    "#     plt.axis('off')\n",
    "#     plt.imshow(image)\n",
    "#     plt.title(label)\n",
    "#     return\n",
    "\n",
    "# image_index = 0\n",
    "# row = 5\n",
    "# col = 6\n",
    "# for i in range(1,(row*col)):\n",
    "#     plot_images(fig, images_for_plot[image_index], labels_for_plot[image_index], row, col, i)\n",
    "#     image_index = image_index + 1\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:47.294717Z",
     "iopub.status.busy": "2021-02-27T11:13:47.293745Z",
     "iopub.status.idle": "2021-02-27T11:13:47.296959Z",
     "shell.execute_reply": "2021-02-27T11:13:47.296418Z"
    },
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     "duration": 0.095197,
     "end_time": "2021-02-27T11:13:47.297104",
     "exception": false,
     "start_time": "2021-02-27T11:13:47.201907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# labels_dict = {\n",
    "#     '1_Portrait':0,\n",
    "#     '2_Group_portrait':1,\n",
    "#     '3_Kids':2,\n",
    "#     '4_Dog':3,\n",
    "#     '5_Cat':4,\n",
    "#     '6_Macro':5,\n",
    "#     '7_Food':6,\n",
    "#     '8_Beach':7,\n",
    "#     '9_Mountain':8,\n",
    "#     '10_Waterfall':9,\n",
    "#     '11_Snow':10,\n",
    "#     '12_Landscape':11,\n",
    "#     '13_Underwater':12,\n",
    "#     '14_Architecture':13,\n",
    "#     '15_Sunset_Sunrise':14,\n",
    "#     '16_Blue_Sky':15,\n",
    "#     '17_Cloudy_Sky':16,\n",
    "#     '18_Greenery':17,\n",
    "#     '19_Autumn_leaves':18,\n",
    "#     '20_Flower':19,\n",
    "#     '21_Night_shot':20,\n",
    "#     '22_Stage_concert':21,\n",
    "#     '23_Fireworks':22,\n",
    "#     '24_Candle_light':23,\n",
    "#     '25_Neon_lights':24,\n",
    "#     '26_Indoor':25,\n",
    "#     '27_Backlight':26,\n",
    "#     '28_Text_Documents':27,\n",
    "#     '29_QR_images':28,\n",
    "#     '30_Computer_Screens':29\n",
    "# }\n",
    "\n",
    "\n",
    "# def load_data():\n",
    "#     \"\"\"\n",
    "#     Loads data and preprocess. Returns train and test data along with labels.\n",
    "#     \"\"\"\n",
    "#     images = []\n",
    "#     labels = []\n",
    "#     size = 384,576\n",
    "#     print(\"LOADING DATA FROM : \",end = \"\")\n",
    "#     for folder in os.listdir(train_dir):\n",
    "#         print(folder, end = ' | ')\n",
    "#         for image in os.listdir(train_dir + \"/\" + folder):\n",
    "#             temp_img = cv2.imread(train_dir + '/' + folder + '/' + image)\n",
    "#             temp_img = cv2.resize(temp_img, size)\n",
    "#             images.append(temp_img)\n",
    "#             labels.append(labels_dict[folder])\n",
    "    \n",
    "#     images = np.array(images)\n",
    "#     images = images.astype('float32')/255.0\n",
    "    \n",
    "#     labels = keras.utils.to_categorical(labels)\n",
    "    \n",
    "#     X_train, X_test, Y_train, Y_test = train_test_split(images, labels, test_size = 0.08)\n",
    "    \n",
    "#     print()\n",
    "#     print('Loaded', len(X_train),'images for training,','Train data shape =',X_train.shape)\n",
    "#     print('Loaded', len(X_test),'images for testing','Test data shape =',X_test.shape)\n",
    "    \n",
    "#     return X_train, X_test, Y_train, Y_test\n",
    "\n",
    "# X_train, X_test, y_train, y_test = load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:47.476751Z",
     "iopub.status.busy": "2021-02-27T11:13:47.475751Z",
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    },
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     "duration": 0.097976,
     "end_time": "2021-02-27T11:13:47.479019",
     "exception": false,
     "start_time": "2021-02-27T11:13:47.381043",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class PlotLearning(Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.i = 0\n",
    "        self.x = []\n",
    "        self.losses = []\n",
    "        self.val_losses = []\n",
    "        self.acc = []\n",
    "        self.val_acc = []\n",
    "        self.fig = plt.figure()\n",
    "        \n",
    "        self.logs = []\n",
    "        \n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        self.logs.append(logs)\n",
    "        self.x.append(self.i)\n",
    "        self.losses.append(logs.get('loss'))\n",
    "        self.val_losses.append(logs.get('val_loss'))\n",
    "        self.acc.append(logs.get('acc'))\n",
    "        self.val_acc.append(logs.get('val_accuracy'))\n",
    "        self.i += 1\n",
    "        f, (ax1, ax2) = plt.subplots(1, 2, sharex=True)\n",
    "        \n",
    "        clear_output(wait=True)\n",
    "        \n",
    "        ax1.set_yscale('Log')\n",
    "        ax1.plot(self.x, self.losses, label=\"loss\")\n",
    "        ax1.plot(self.x, self.val_losses, label=\"val_loss\")\n",
    "        ax1.legend()\n",
    "        \n",
    "        ax2.plot(self.x, self.acc, label=\"acc\")\n",
    "        ax2.plot(self.x, self.val_acc, label=\"val_acc\")\n",
    "        ax2.legend()\n",
    "        \n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "plot = PlotLearning()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:47.651555Z",
     "iopub.status.busy": "2021-02-27T11:13:47.650481Z",
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     "shell.execute_reply": "2021-02-27T11:13:48.321988Z"
    },
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     "end_time": "2021-02-27T11:13:48.322940",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 9020 images belonging to 30 classes.\n",
      "Found 877 images belonging to 30 classes.\n"
     ]
    }
   ],
   "source": [
    "target_size = (224, 224)\n",
    "target_dims = (224, 224, 3) # add channel for RGB\n",
    "n_classes = 30\n",
    "val_frac = 0.09\n",
    "batch_size = 128\n",
    "\n",
    "data_augmentor = ImageDataGenerator(\n",
    "    validation_split=val_frac,\n",
    "    rotation_range=0.2,\n",
    "    zoom_range=0.4,\n",
    "    channel_shift_range=0.2,\n",
    "    fill_mode='nearest',\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=False, \n",
    "    rescale=1./255,\n",
    "    data_format='channels_last', \n",
    ")\n",
    "\n",
    "train_generator = data_augmentor.flow_from_directory(train_dir, target_size=target_size, batch_size=batch_size, shuffle=True, subset=\"training\")\n",
    "val_generator = data_augmentor.flow_from_directory(train_dir, target_size=target_size, batch_size=batch_size, subset=\"validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:48.494155Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
      "9412608/9406464 [==============================] - 0s 0us/step\n",
      "Model: \"mobilenetv2_1.00_224\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         input_1[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",
      "Total params: 2,257,984\n",
      "Trainable params: 0\n",
      "Non-trainable params: 2,257,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_1 = MobileNetV2(weights=\"imagenet\",include_top=False,input_shape=(224,224,3))\n",
    "\n",
    "for layer in model_1.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:53.398761Z",
     "iopub.status.busy": "2021-02-27T11:13:53.397522Z",
     "iopub.status.idle": "2021-02-27T11:13:53.479438Z",
     "shell.execute_reply": "2021-02-27T11:13:53.478887Z"
    },
    "papermill": {
     "duration": 0.173482,
     "end_time": "2021-02-27T11:13:53.479589",
     "exception": false,
     "start_time": "2021-02-27T11:13:53.306107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 112, 112, 32) 864         input_1[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",
      "average_pooling2d (AveragePooli (None, 1, 1, 1280)   0           out_relu[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 1280)         0           average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          327936      flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 256)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 30)           7710        dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 2,593,630\n",
      "Trainable params: 335,646\n",
      "Non-trainable params: 2,257,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "headModel = model_1.output\n",
    "\n",
    "headModel = AveragePooling2D(pool_size=(7,7))(headModel)\n",
    "headModel = Flatten(name=\"flatten\")(headModel)\n",
    "\n",
    "headModel = Dense(256,activation=\"relu\")(headModel)\n",
    "\n",
    "headModel = Dropout(0.5)(headModel)\n",
    "\n",
    "headModel = Dense(n_classes, activation=\"softmax\")(headModel)\n",
    "\n",
    "model_2 = Model(inputs=model_1.input, outputs=headModel)\n",
    "\n",
    "model_2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:53.678453Z",
     "iopub.status.busy": "2021-02-27T11:13:53.677507Z",
     "iopub.status.idle": "2021-02-27T11:13:53.685106Z",
     "shell.execute_reply": "2021-02-27T11:13:53.684577Z"
    },
    "papermill": {
     "duration": 0.108165,
     "end_time": "2021-02-27T11:13:53.685243",
     "exception": false,
     "start_time": "2021-02-27T11:13:53.577078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "opt_2 = Adam(lr=1e-4)\n",
    "model_2.compile(loss=\"categorical_crossentropy\",optimizer=opt_2,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T11:13:53.855433Z",
     "iopub.status.busy": "2021-02-27T11:13:53.854385Z",
     "iopub.status.idle": "2021-02-27T11:13:57.123112Z",
     "shell.execute_reply": "2021-02-27T11:13:57.123619Z"
    },
    "papermill": {
     "duration": 3.356653,
     "end_time": "2021-02-27T11:13:57.123786",
     "exception": false,
     "start_time": "2021-02-27T11:13:53.767133",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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   "source": [
    "plot_model(model_2,to_file='MobileNetV2_transfer_model.png',show_shapes=True,show_layer_names=True)\n",
    "SVG(model_to_dot(model_2).create(prog='dot',format='svg'))"
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    "epochs = 125\n",
    "batchSize = 128"
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   "source": [
    "MobileNet_checkpoint = ModelCheckpoint(\"model_weight.h5\", monitor='val_accuracy',verbose=1,save_best_only=True,mode='max')\n",
    "MobileNet_logs = TensorBoard(\"MobileNet-transfer-logs\")"
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   "source": [
    "import tensorflow as tf\n",
    "\n",
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "    if(logs.get('accuracy')>0.950):\n",
    "      print(\"\\nReached 97.0% accuracy so cancelling training!\")\n",
    "      self.model.stop_training = True\n",
    "        \n",
    "callback = myCallback()"
   ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/125\n",
      "Epoch 2/125\n",
      "Epoch 3/125\n",
      "Epoch 4/125\n",
      "Epoch 5/125\n",
      "Epoch 6/125\n",
      "Epoch 7/125\n",
      "Epoch 8/125\n",
      "Epoch 9/125\n",
      "Epoch 10/125\n",
      "Epoch 11/125\n",
      "Epoch 12/125\n",
      "Epoch 13/125\n",
      "Epoch 14/125\n",
      "Epoch 15/125\n",
      "Epoch 16/125\n",
      "Epoch 17/125\n",
      "Epoch 18/125\n",
      "Epoch 19/125\n",
      "Epoch 20/125\n",
      "Epoch 21/125\n",
      "Epoch 22/125\n",
      "Epoch 23/125\n",
      "Epoch 24/125\n",
      "Epoch 25/125\n",
      "Epoch 26/125\n",
      "Epoch 27/125\n",
      "Epoch 28/125\n",
      "Epoch 29/125\n",
      "Epoch 30/125\n",
      "Epoch 31/125\n",
      "Epoch 32/125\n",
      "Epoch 33/125\n",
      "Epoch 34/125\n",
      "Epoch 35/125\n",
      "Epoch 36/125\n",
      "Epoch 37/125\n",
      "Epoch 38/125\n",
      "Epoch 39/125\n",
      "Epoch 40/125\n",
      "Epoch 41/125\n",
      "Epoch 42/125\n",
      "Epoch 43/125\n",
      "Epoch 44/125\n",
      "Epoch 45/125\n",
      "Epoch 46/125\n",
      "Epoch 47/125\n",
      "Epoch 48/125\n",
      "Epoch 49/125\n",
      "Epoch 50/125\n",
      "Epoch 51/125\n",
      "Epoch 52/125\n",
      "Epoch 53/125\n",
      "Epoch 54/125\n",
      "Epoch 55/125\n",
      "Epoch 56/125\n",
      "Epoch 57/125\n",
      "Epoch 58/125\n",
      "Epoch 59/125\n",
      "Epoch 60/125\n",
      "Epoch 61/125\n",
      "Epoch 62/125\n",
      "Epoch 63/125\n",
      "Epoch 64/125\n",
      "Epoch 65/125\n",
      "Epoch 66/125\n",
      "Epoch 67/125\n",
      "Epoch 68/125\n",
      "Epoch 69/125\n",
      "Epoch 70/125\n",
      "Epoch 71/125\n",
      "Epoch 72/125\n",
      "Epoch 73/125\n",
      "Epoch 74/125\n",
      "Epoch 75/125\n",
      "Epoch 76/125\n",
      "Epoch 77/125\n",
      "Epoch 78/125\n",
      "Epoch 79/125\n",
      "Epoch 80/125\n",
      "Epoch 81/125\n",
      "Epoch 82/125\n",
      "Epoch 83/125\n",
      "Epoch 84/125\n",
      "Epoch 85/125\n",
      "Epoch 86/125\n",
      "Epoch 87/125\n",
      "Epoch 88/125\n",
      "Epoch 89/125\n",
      "\n",
      "Reached 97.0% accuracy so cancelling training!\n"
     ]
    }
   ],
   "source": [
    "history = model_2.fit(train_generator,\n",
    "                      epochs=epochs,\n",
    "                      verbose=4,\n",
    "                      batch_size=128,\n",
    "                      validation_data=val_generator,\n",
    "                      callbacks =[callback]\n",
    "                     )"
   ]
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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.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "plt.title('accuracy plot - train vs test')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.legend(['training loss', 'validation loss'], loc = 'upper right')\n",
    "plt.title('loss plot - training vs validation')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-27T14:49:04.661951Z",
     "iopub.status.busy": "2021-02-27T14:49:04.661332Z",
     "iopub.status.idle": "2021-02-27T14:49:04.666291Z",
     "shell.execute_reply": "2021-02-27T14:49:04.666884Z"
    },
    "papermill": {
     "duration": 0.138421,
     "end_time": "2021-02-27T14:49:04.667048",
     "exception": false,
     "start_time": "2021-02-27T14:49:04.528627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-596723284980>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'model.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "model.save('model.h5')"
   ]
  }
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