notebooks/8_classes_EfficientNetV2S_96x96_bs_32.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "c3SqkReH4WQe"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score, confusion_matrix, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "notebook_name = '8_classes_EfficientNetV2S_96x96_bs_32'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "zy1ftuOe4lFK"
   },
   "outputs": [],
   "source": [
    "train_dir = '../data/images/train'\n",
    "val_dir = '../data/images/val'\n",
    "test_dir = '../data/images/test'\n",
    "\n",
    "train_df = pd.read_csv('../data/majority_train.csv')\n",
    "val_df = pd.read_csv('../data/majority_val.csv')\n",
    "test_df = pd.read_csv('../data/majority_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fer0000001.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fer0000002.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fer0000004.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fer0000005.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fer0000006.png</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25040</th>\n",
       "      <td>fer0028633.png</td>\n",
       "      <td>fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25041</th>\n",
       "      <td>fer0028634.png</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25042</th>\n",
       "      <td>fer0028635.png</td>\n",
       "      <td>sadness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25043</th>\n",
       "      <td>fer0028636.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25044</th>\n",
       "      <td>fer0028637.png</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25045 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             filename    class\n",
       "0      fer0000001.png  neutral\n",
       "1      fer0000002.png  neutral\n",
       "2      fer0000004.png  neutral\n",
       "3      fer0000005.png  neutral\n",
       "4      fer0000006.png  sadness\n",
       "...               ...      ...\n",
       "25040  fer0028633.png     fear\n",
       "25041  fer0028634.png    anger\n",
       "25042  fer0028635.png  sadness\n",
       "25043  fer0028636.png  neutral\n",
       "25044  fer0028637.png  neutral\n",
       "\n",
       "[25045 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_jL-3C814lqo",
    "outputId": "5e9ce65b-bb3d-4864-9ede-f93edc545b4a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 25045 validated image filenames belonging to 8 classes.\n",
      "Found 3191 validated image filenames belonging to 8 classes.\n",
      "Found 3137 validated image filenames belonging to 8 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.08,\n",
    "    height_shift_range=0.08,\n",
    "    zoom_range=0.05,\n",
    "    shear_range=np.rad2deg(np.arctan(0.05)),  # used to set the value by fraction instead of an angle\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='reflect',\n",
    ")\n",
    "\n",
    "test_datagen = tf.keras.preprocessing.image.ImageDataGenerator()\n",
    "\n",
    "target_size = (96, 96)\n",
    "interpolation = 'bilinear'\n",
    "color_mode = 'grayscale'\n",
    "batch_size = 32\n",
    "\n",
    "train_generator = train_datagen.flow_from_dataframe(\n",
    "    dataframe=train_df,\n",
    "    directory=train_dir,\n",
    "    target_size=target_size,\n",
    "    interpolation=interpolation,\n",
    "    color_mode=color_mode,\n",
    "    batch_size=batch_size,\n",
    ")\n",
    "\n",
    "val_generator = test_datagen.flow_from_dataframe(\n",
    "    dataframe=val_df,\n",
    "    directory=val_dir,\n",
    "    target_size=target_size,\n",
    "    interpolation=interpolation,\n",
    "    color_mode=color_mode,\n",
    "    batch_size=batch_size,\n",
    ")\n",
    "\n",
    "test_generator = test_datagen.flow_from_dataframe(\n",
    "    dataframe=test_df,\n",
    "    directory=test_dir,\n",
    "    target_size=target_size,\n",
    "    interpolation=interpolation,\n",
    "    color_mode=color_mode,\n",
    "    batch_size=batch_size,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = list(train_generator.class_indices.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['anger',\n",
       " 'contempt',\n",
       " 'disgust',\n",
       " 'fear',\n",
       " 'happiness',\n",
       " 'neutral',\n",
       " 'sadness',\n",
       " 'surprise']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = next(train_generator)\n",
    "\n",
    "plt.figure(figsize=[20, 20])\n",
    "\n",
    "for i in range(5):\n",
    "    plt.subplot(1, 5, i + 1)\n",
    "    plt.title(f'{data[1][i]} = {labels[np.argmax(data[1][i])]}')\n",
    "    plt.imshow(data[0][i], cmap='gray')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "nz51JAUD7vrT"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.applications.efficientnet_v2.EfficientNetV2S(\n",
    "    input_shape=(96, 96, 1),\n",
    "    weights=None,\n",
    "    classes=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"efficientnetv2-s\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 96, 96, 1)]  0           []                               \n",
      "                                                                                                  \n",
      " rescaling (Rescaling)          (None, 96, 96, 1)    0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " stem_conv (Conv2D)             (None, 48, 48, 24)   216         ['rescaling[0][0]']              \n",
      "                                                                                                  \n",
      " stem_bn (BatchNormalization)   (None, 48, 48, 24)   96          ['stem_conv[0][0]']              \n",
      "                                                                                                  \n",
      " stem_activation (Activation)   (None, 48, 48, 24)   0           ['stem_bn[0][0]']                \n",
      "                                                                                                  \n",
      " block1a_project_conv (Conv2D)  (None, 48, 48, 24)   5184        ['stem_activation[0][0]']        \n",
      "                                                                                                  \n",
      " block1a_project_bn (BatchNorma  (None, 48, 48, 24)  96          ['block1a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block1a_project_activation (Ac  (None, 48, 48, 24)  0           ['block1a_project_bn[0][0]']     \n",
      " tivation)                                                                                        \n",
      "                                                                                                  \n",
      " block1a_add (Add)              (None, 48, 48, 24)   0           ['block1a_project_activation[0][0\n",
      "                                                                 ]',                              \n",
      "                                                                  'stem_activation[0][0]']        \n",
      "                                                                                                  \n",
      " block1b_project_conv (Conv2D)  (None, 48, 48, 24)   5184        ['block1a_add[0][0]']            \n",
      "                                                                                                  \n",
      " block1b_project_bn (BatchNorma  (None, 48, 48, 24)  96          ['block1b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block1b_project_activation (Ac  (None, 48, 48, 24)  0           ['block1b_project_bn[0][0]']     \n",
      " tivation)                                                                                        \n",
      "                                                                                                  \n",
      " block1b_add (Add)              (None, 48, 48, 24)   0           ['block1b_project_activation[0][0\n",
      "                                                                 ]',                              \n",
      "                                                                  'block1a_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2a_expand_conv (Conv2D)   (None, 24, 24, 96)   20736       ['block1b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2a_expand_bn (BatchNormal  (None, 24, 24, 96)  384         ['block2a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2a_expand_activation (Act  (None, 24, 24, 96)  0           ['block2a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2a_project_conv (Conv2D)  (None, 24, 24, 48)   4608        ['block2a_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block2a_project_bn (BatchNorma  (None, 24, 24, 48)  192         ['block2a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2b_expand_conv (Conv2D)   (None, 24, 24, 192)  82944       ['block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_expand_bn (BatchNormal  (None, 24, 24, 192)  768        ['block2b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2b_expand_activation (Act  (None, 24, 24, 192)  0          ['block2b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2b_project_conv (Conv2D)  (None, 24, 24, 48)   9216        ['block2b_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block2b_project_bn (BatchNorma  (None, 24, 24, 48)  192         ['block2b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2b_add (Add)              (None, 24, 24, 48)   0           ['block2b_project_bn[0][0]',     \n",
      "                                                                  'block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_expand_conv (Conv2D)   (None, 24, 24, 192)  82944       ['block2b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2c_expand_bn (BatchNormal  (None, 24, 24, 192)  768        ['block2c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2c_expand_activation (Act  (None, 24, 24, 192)  0          ['block2c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2c_project_conv (Conv2D)  (None, 24, 24, 48)   9216        ['block2c_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block2c_project_bn (BatchNorma  (None, 24, 24, 48)  192         ['block2c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2c_add (Add)              (None, 24, 24, 48)   0           ['block2c_project_bn[0][0]',     \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                  'block2b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2d_expand_conv (Conv2D)   (None, 24, 24, 192)  82944       ['block2c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2d_expand_bn (BatchNormal  (None, 24, 24, 192)  768        ['block2d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2d_expand_activation (Act  (None, 24, 24, 192)  0          ['block2d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2d_project_conv (Conv2D)  (None, 24, 24, 48)   9216        ['block2d_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block2d_project_bn (BatchNorma  (None, 24, 24, 48)  192         ['block2d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2d_add (Add)              (None, 24, 24, 48)   0           ['block2d_project_bn[0][0]',     \n",
      "                                                                  'block2c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3a_expand_conv (Conv2D)   (None, 12, 12, 192)  82944       ['block2d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3a_expand_bn (BatchNormal  (None, 12, 12, 192)  768        ['block3a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3a_expand_activation (Act  (None, 12, 12, 192)  0          ['block3a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3a_project_conv (Conv2D)  (None, 12, 12, 64)   12288       ['block3a_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block3a_project_bn (BatchNorma  (None, 12, 12, 64)  256         ['block3a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_expand_conv (Conv2D)   (None, 12, 12, 256)  147456      ['block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_expand_bn (BatchNormal  (None, 12, 12, 256)  1024       ['block3b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_expand_activation (Act  (None, 12, 12, 256)  0          ['block3b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_project_conv (Conv2D)  (None, 12, 12, 64)   16384       ['block3b_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block3b_project_bn (BatchNorma  (None, 12, 12, 64)  256         ['block3b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_add (Add)              (None, 12, 12, 64)   0           ['block3b_project_bn[0][0]',     \n",
      "                                                                  'block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_expand_conv (Conv2D)   (None, 12, 12, 256)  147456      ['block3b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3c_expand_bn (BatchNormal  (None, 12, 12, 256)  1024       ['block3c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3c_expand_activation (Act  (None, 12, 12, 256)  0          ['block3c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3c_project_conv (Conv2D)  (None, 12, 12, 64)   16384       ['block3c_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block3c_project_bn (BatchNorma  (None, 12, 12, 64)  256         ['block3c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3c_add (Add)              (None, 12, 12, 64)   0           ['block3c_project_bn[0][0]',     \n",
      "                                                                  'block3b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3d_expand_conv (Conv2D)   (None, 12, 12, 256)  147456      ['block3c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3d_expand_bn (BatchNormal  (None, 12, 12, 256)  1024       ['block3d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3d_expand_activation (Act  (None, 12, 12, 256)  0          ['block3d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3d_project_conv (Conv2D)  (None, 12, 12, 64)   16384       ['block3d_expand_activation[0][0]\n",
      "                                                                 ']                               \n",
      "                                                                                                  \n",
      " block3d_project_bn (BatchNorma  (None, 12, 12, 64)  256         ['block3d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3d_add (Add)              (None, 12, 12, 64)   0           ['block3d_project_bn[0][0]',     \n",
      "                                                                  'block3c_add[0][0]']            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block4a_expand_conv (Conv2D)   (None, 12, 12, 256)  16384       ['block3d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4a_expand_bn (BatchNormal  (None, 12, 12, 256)  1024       ['block4a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_expand_activation (Act  (None, 12, 12, 256)  0          ['block4a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_dwconv2 (DepthwiseConv  (None, 6, 6, 256)   2304        ['block4a_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block4a_bn (BatchNormalization  (None, 6, 6, 256)   1024        ['block4a_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_activation (Activation  (None, 6, 6, 256)   0           ['block4a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_se_squeeze (GlobalAver  (None, 256)         0           ['block4a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4a_se_reshape (Reshape)   (None, 1, 1, 256)    0           ['block4a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_reduce (Conv2D)     (None, 1, 1, 16)     4112        ['block4a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_expand (Conv2D)     (None, 1, 1, 256)    4352        ['block4a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_se_excite (Multiply)   (None, 6, 6, 256)    0           ['block4a_activation[0][0]',     \n",
      "                                                                  'block4a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_conv (Conv2D)  (None, 6, 6, 128)    32768       ['block4a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_bn (BatchNorma  (None, 6, 6, 128)   512         ['block4a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_expand_conv (Conv2D)   (None, 6, 6, 512)    65536       ['block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_expand_bn (BatchNormal  (None, 6, 6, 512)   2048        ['block4b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_expand_activation (Act  (None, 6, 6, 512)   0           ['block4b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_dwconv2 (DepthwiseConv  (None, 6, 6, 512)   4608        ['block4b_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block4b_bn (BatchNormalization  (None, 6, 6, 512)   2048        ['block4b_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_activation (Activation  (None, 6, 6, 512)   0           ['block4b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_se_squeeze (GlobalAver  (None, 512)         0           ['block4b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4b_se_reshape (Reshape)   (None, 1, 1, 512)    0           ['block4b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_reduce (Conv2D)     (None, 1, 1, 32)     16416       ['block4b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_expand (Conv2D)     (None, 1, 1, 512)    16896       ['block4b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_se_excite (Multiply)   (None, 6, 6, 512)    0           ['block4b_activation[0][0]',     \n",
      "                                                                  'block4b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_conv (Conv2D)  (None, 6, 6, 128)    65536       ['block4b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_bn (BatchNorma  (None, 6, 6, 128)   512         ['block4b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_add (Add)              (None, 6, 6, 128)    0           ['block4b_project_bn[0][0]',     \n",
      "                                                                  'block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_expand_conv (Conv2D)   (None, 6, 6, 512)    65536       ['block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4c_expand_bn (BatchNormal  (None, 6, 6, 512)   2048        ['block4c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_expand_activation (Act  (None, 6, 6, 512)   0           ['block4c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_dwconv2 (DepthwiseConv  (None, 6, 6, 512)   4608        ['block4c_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block4c_bn (BatchNormalization  (None, 6, 6, 512)   2048        ['block4c_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_activation (Activation  (None, 6, 6, 512)   0           ['block4c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_se_squeeze (GlobalAver  (None, 512)         0           ['block4c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4c_se_reshape (Reshape)   (None, 1, 1, 512)    0           ['block4c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_reduce (Conv2D)     (None, 1, 1, 32)     16416       ['block4c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_expand (Conv2D)     (None, 1, 1, 512)    16896       ['block4c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_se_excite (Multiply)   (None, 6, 6, 512)    0           ['block4c_activation[0][0]',     \n",
      "                                                                  'block4c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_conv (Conv2D)  (None, 6, 6, 128)    65536       ['block4c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_bn (BatchNorma  (None, 6, 6, 128)   512         ['block4c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4c_add (Add)              (None, 6, 6, 128)    0           ['block4c_project_bn[0][0]',     \n",
      "                                                                  'block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4d_expand_conv (Conv2D)   (None, 6, 6, 512)    65536       ['block4c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4d_expand_bn (BatchNormal  (None, 6, 6, 512)   2048        ['block4d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4d_expand_activation (Act  (None, 6, 6, 512)   0           ['block4d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4d_dwconv2 (DepthwiseConv  (None, 6, 6, 512)   4608        ['block4d_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block4d_bn (BatchNormalization  (None, 6, 6, 512)   2048        ['block4d_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4d_activation (Activation  (None, 6, 6, 512)   0           ['block4d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4d_se_squeeze (GlobalAver  (None, 512)         0           ['block4d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4d_se_reshape (Reshape)   (None, 1, 1, 512)    0           ['block4d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_se_reduce (Conv2D)     (None, 1, 1, 32)     16416       ['block4d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_se_expand (Conv2D)     (None, 1, 1, 512)    16896       ['block4d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_se_excite (Multiply)   (None, 6, 6, 512)    0           ['block4d_activation[0][0]',     \n",
      "                                                                  'block4d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_project_conv (Conv2D)  (None, 6, 6, 128)    65536       ['block4d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_project_bn (BatchNorma  (None, 6, 6, 128)   512         ['block4d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4d_add (Add)              (None, 6, 6, 128)    0           ['block4d_project_bn[0][0]',     \n",
      "                                                                  'block4c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4e_expand_conv (Conv2D)   (None, 6, 6, 512)    65536       ['block4d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4e_expand_bn (BatchNormal  (None, 6, 6, 512)   2048        ['block4e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4e_expand_activation (Act  (None, 6, 6, 512)   0           ['block4e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4e_dwconv2 (DepthwiseConv  (None, 6, 6, 512)   4608        ['block4e_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block4e_bn (BatchNormalization  (None, 6, 6, 512)   2048        ['block4e_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4e_activation (Activation  (None, 6, 6, 512)   0           ['block4e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4e_se_squeeze (GlobalAver  (None, 512)         0           ['block4e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block4e_se_reshape (Reshape)   (None, 1, 1, 512)    0           ['block4e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_se_reduce (Conv2D)     (None, 1, 1, 32)     16416       ['block4e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_se_expand (Conv2D)     (None, 1, 1, 512)    16896       ['block4e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_se_excite (Multiply)   (None, 6, 6, 512)    0           ['block4e_activation[0][0]',     \n",
      "                                                                  'block4e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_project_conv (Conv2D)  (None, 6, 6, 128)    65536       ['block4e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_project_bn (BatchNorma  (None, 6, 6, 128)   512         ['block4e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4e_add (Add)              (None, 6, 6, 128)    0           ['block4e_project_bn[0][0]',     \n",
      "                                                                  'block4d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4f_expand_conv (Conv2D)   (None, 6, 6, 512)    65536       ['block4e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4f_expand_bn (BatchNormal  (None, 6, 6, 512)   2048        ['block4f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4f_expand_activation (Act  (None, 6, 6, 512)   0           ['block4f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4f_dwconv2 (DepthwiseConv  (None, 6, 6, 512)   4608        ['block4f_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block4f_bn (BatchNormalization  (None, 6, 6, 512)   2048        ['block4f_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4f_activation (Activation  (None, 6, 6, 512)   0           ['block4f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4f_se_squeeze (GlobalAver  (None, 512)         0           ['block4f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4f_se_reshape (Reshape)   (None, 1, 1, 512)    0           ['block4f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_se_reduce (Conv2D)     (None, 1, 1, 32)     16416       ['block4f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_se_expand (Conv2D)     (None, 1, 1, 512)    16896       ['block4f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_se_excite (Multiply)   (None, 6, 6, 512)    0           ['block4f_activation[0][0]',     \n",
      "                                                                  'block4f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_project_conv (Conv2D)  (None, 6, 6, 128)    65536       ['block4f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_project_bn (BatchNorma  (None, 6, 6, 128)   512         ['block4f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4f_add (Add)              (None, 6, 6, 128)    0           ['block4f_project_bn[0][0]',     \n",
      "                                                                  'block4e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_conv (Conv2D)   (None, 6, 6, 768)    98304       ['block4f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_bn (BatchNormal  (None, 6, 6, 768)   3072        ['block5a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_expand_activation (Act  (None, 6, 6, 768)   0           ['block5a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_dwconv2 (DepthwiseConv  (None, 6, 6, 768)   6912        ['block5a_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5a_bn (BatchNormalization  (None, 6, 6, 768)   3072        ['block5a_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_activation (Activation  (None, 6, 6, 768)   0           ['block5a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_se_squeeze (GlobalAver  (None, 768)         0           ['block5a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5a_se_reshape (Reshape)   (None, 1, 1, 768)    0           ['block5a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_reduce (Conv2D)     (None, 1, 1, 32)     24608       ['block5a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_expand (Conv2D)     (None, 1, 1, 768)    25344       ['block5a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_se_excite (Multiply)   (None, 6, 6, 768)    0           ['block5a_activation[0][0]',     \n",
      "                                                                  'block5a_se_expand[0][0]']      \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block5a_project_conv (Conv2D)  (None, 6, 6, 160)    122880      ['block5a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5b_expand_activation (Act  (None, 6, 6, 960)   0           ['block5b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5b_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5b_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5b_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5b_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5b_activation (Activation  (None, 6, 6, 960)   0           ['block5b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5b_se_squeeze (GlobalAver  (None, 960)         0           ['block5b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5b_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5b_activation[0][0]',     \n",
      "                                                                  'block5b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_add (Add)              (None, 6, 6, 160)    0           ['block5b_project_bn[0][0]',     \n",
      "                                                                  'block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5c_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5c_expand_activation (Act  (None, 6, 6, 960)   0           ['block5c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5c_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5c_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5c_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5c_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5c_activation (Activation  (None, 6, 6, 960)   0           ['block5c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5c_se_squeeze (GlobalAver  (None, 960)         0           ['block5c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5c_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5c_activation[0][0]',     \n",
      "                                                                  'block5c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5c_add (Add)              (None, 6, 6, 160)    0           ['block5c_project_bn[0][0]',     \n",
      "                                                                  'block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5d_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5d_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block5d_expand_activation (Act  (None, 6, 6, 960)   0           ['block5d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5d_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5d_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5d_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5d_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5d_activation (Activation  (None, 6, 6, 960)   0           ['block5d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5d_se_squeeze (GlobalAver  (None, 960)         0           ['block5d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5d_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5d_activation[0][0]',     \n",
      "                                                                  'block5d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5d_add (Add)              (None, 6, 6, 160)    0           ['block5d_project_bn[0][0]',     \n",
      "                                                                  'block5c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5e_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5e_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5e_expand_activation (Act  (None, 6, 6, 960)   0           ['block5e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5e_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5e_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5e_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5e_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5e_activation (Activation  (None, 6, 6, 960)   0           ['block5e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5e_se_squeeze (GlobalAver  (None, 960)         0           ['block5e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5e_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5e_activation[0][0]',     \n",
      "                                                                  'block5e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5e_add (Add)              (None, 6, 6, 160)    0           ['block5e_project_bn[0][0]',     \n",
      "                                                                  'block5d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5f_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5f_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5f_expand_activation (Act  (None, 6, 6, 960)   0           ['block5f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5f_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5f_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5f_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5f_dwconv2[0][0]']        \n",
      " )                                                                                                \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block5f_activation (Activation  (None, 6, 6, 960)   0           ['block5f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5f_se_squeeze (GlobalAver  (None, 960)         0           ['block5f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5f_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5f_activation[0][0]',     \n",
      "                                                                  'block5f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5f_add (Add)              (None, 6, 6, 160)    0           ['block5f_project_bn[0][0]',     \n",
      "                                                                  'block5e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5g_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5g_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5g_expand_activation (Act  (None, 6, 6, 960)   0           ['block5g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5g_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5g_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5g_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5g_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5g_activation (Activation  (None, 6, 6, 960)   0           ['block5g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5g_se_squeeze (GlobalAver  (None, 960)         0           ['block5g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5g_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5g_activation[0][0]',     \n",
      "                                                                  'block5g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5g_add (Add)              (None, 6, 6, 160)    0           ['block5g_project_bn[0][0]',     \n",
      "                                                                  'block5f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5h_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5h_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5h_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5h_expand_activation (Act  (None, 6, 6, 960)   0           ['block5h_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5h_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5h_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5h_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5h_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5h_activation (Activation  (None, 6, 6, 960)   0           ['block5h_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5h_se_squeeze (GlobalAver  (None, 960)         0           ['block5h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5h_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block5h_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5h_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5h_activation[0][0]',     \n",
      "                                                                  'block5h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5h_add (Add)              (None, 6, 6, 160)    0           ['block5h_project_bn[0][0]',     \n",
      "                                                                  'block5g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5i_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5i_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block5i_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5i_expand_activation (Act  (None, 6, 6, 960)   0           ['block5i_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5i_dwconv2 (DepthwiseConv  (None, 6, 6, 960)   8640        ['block5i_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block5i_bn (BatchNormalization  (None, 6, 6, 960)   3840        ['block5i_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5i_activation (Activation  (None, 6, 6, 960)   0           ['block5i_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5i_se_squeeze (GlobalAver  (None, 960)         0           ['block5i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5i_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_se_excite (Multiply)   (None, 6, 6, 960)    0           ['block5i_activation[0][0]',     \n",
      "                                                                  'block5i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_project_conv (Conv2D)  (None, 6, 6, 160)    153600      ['block5i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_project_bn (BatchNorma  (None, 6, 6, 160)   640         ['block5i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5i_add (Add)              (None, 6, 6, 160)    0           ['block5i_project_bn[0][0]',     \n",
      "                                                                  'block5h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_conv (Conv2D)   (None, 6, 6, 960)    153600      ['block5i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_bn (BatchNormal  (None, 6, 6, 960)   3840        ['block6a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6a_expand_activation (Act  (None, 6, 6, 960)   0           ['block6a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6a_dwconv2 (DepthwiseConv  (None, 3, 3, 960)   8640        ['block6a_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6a_bn (BatchNormalization  (None, 3, 3, 960)   3840        ['block6a_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6a_activation (Activation  (None, 3, 3, 960)   0           ['block6a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6a_se_squeeze (GlobalAver  (None, 960)         0           ['block6a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6a_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block6a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block6a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block6a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_se_excite (Multiply)   (None, 3, 3, 960)    0           ['block6a_activation[0][0]',     \n",
      "                                                                  'block6a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_conv (Conv2D)  (None, 3, 3, 256)    245760      ['block6a_se_excite[0][0]']      \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block6a_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6b_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6b_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6b_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6b_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6b_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6b_activation (Activation  (None, 3, 3, 1536)  0           ['block6b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6b_se_squeeze (GlobalAver  (None, 1536)        0           ['block6b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6b_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6b_activation[0][0]',     \n",
      "                                                                  'block6b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_add (Add)              (None, 3, 3, 256)    0           ['block6b_project_bn[0][0]',     \n",
      "                                                                  'block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6c_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6c_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6c_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6c_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6c_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6c_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6c_activation (Activation  (None, 3, 3, 1536)  0           ['block6c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6c_se_squeeze (GlobalAver  (None, 1536)        0           ['block6c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6c_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6c_activation[0][0]',     \n",
      "                                                                  'block6c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6c_add (Add)              (None, 3, 3, 256)    0           ['block6c_project_bn[0][0]',     \n",
      "                                                                  'block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6d_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6d_expand_bn[0][0]']      \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6d_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6d_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6d_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6d_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6d_activation (Activation  (None, 3, 3, 1536)  0           ['block6d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6d_se_squeeze (GlobalAver  (None, 1536)        0           ['block6d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6d_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6d_activation[0][0]',     \n",
      "                                                                  'block6d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6d_add (Add)              (None, 3, 3, 256)    0           ['block6d_project_bn[0][0]',     \n",
      "                                                                  'block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6e_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6e_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6e_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6e_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6e_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6e_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6e_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6e_activation (Activation  (None, 3, 3, 1536)  0           ['block6e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6e_se_squeeze (GlobalAver  (None, 1536)        0           ['block6e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6e_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6e_activation[0][0]',     \n",
      "                                                                  'block6e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6e_add (Add)              (None, 3, 3, 256)    0           ['block6e_project_bn[0][0]',     \n",
      "                                                                  'block6d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6f_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6f_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6f_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6f_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6f_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6f_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6f_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6f_activation (Activation  (None, 3, 3, 1536)  0           ['block6f_bn[0][0]']             \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6f_se_squeeze (GlobalAver  (None, 1536)        0           ['block6f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6f_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6f_activation[0][0]',     \n",
      "                                                                  'block6f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6f_add (Add)              (None, 3, 3, 256)    0           ['block6f_project_bn[0][0]',     \n",
      "                                                                  'block6e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6g_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6g_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6g_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6g_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6g_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6g_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6g_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6g_activation (Activation  (None, 3, 3, 1536)  0           ['block6g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6g_se_squeeze (GlobalAver  (None, 1536)        0           ['block6g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6g_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6g_activation[0][0]',     \n",
      "                                                                  'block6g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6g_add (Add)              (None, 3, 3, 256)    0           ['block6g_project_bn[0][0]',     \n",
      "                                                                  'block6f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6h_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6h_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6h_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6h_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6h_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6h_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6h_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6h_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6h_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6h_activation (Activation  (None, 3, 3, 1536)  0           ['block6h_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6h_se_squeeze (GlobalAver  (None, 1536)        0           ['block6h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6h_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6h_se_reshape[0][0]']     \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block6h_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6h_activation[0][0]',     \n",
      "                                                                  'block6h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6h_add (Add)              (None, 3, 3, 256)    0           ['block6h_project_bn[0][0]',     \n",
      "                                                                  'block6g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6i_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6i_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6i_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6i_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6i_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6i_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6i_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6i_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6i_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6i_activation (Activation  (None, 3, 3, 1536)  0           ['block6i_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6i_se_squeeze (GlobalAver  (None, 1536)        0           ['block6i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6i_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6i_activation[0][0]',     \n",
      "                                                                  'block6i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6i_add (Add)              (None, 3, 3, 256)    0           ['block6i_project_bn[0][0]',     \n",
      "                                                                  'block6h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6j_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6j_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6j_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6j_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6j_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6j_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6j_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6j_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6j_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6j_activation (Activation  (None, 3, 3, 1536)  0           ['block6j_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6j_se_squeeze (GlobalAver  (None, 1536)        0           ['block6j_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6j_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6j_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6j_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6j_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6j_activation[0][0]',     \n",
      "                                                                  'block6j_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6j_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6j_project_conv[0][0]']   \n",
      " lization)                                                                                        \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " block6j_add (Add)              (None, 3, 3, 256)    0           ['block6j_project_bn[0][0]',     \n",
      "                                                                  'block6i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6k_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6k_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6k_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6k_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6k_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6k_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6k_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6k_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6k_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6k_activation (Activation  (None, 3, 3, 1536)  0           ['block6k_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6k_se_squeeze (GlobalAver  (None, 1536)        0           ['block6k_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6k_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6k_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6k_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6k_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6k_activation[0][0]',     \n",
      "                                                                  'block6k_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6k_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6k_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6k_add (Add)              (None, 3, 3, 256)    0           ['block6k_project_bn[0][0]',     \n",
      "                                                                  'block6j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6l_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6k_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6l_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6l_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6l_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6l_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6l_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6l_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6l_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6l_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6l_activation (Activation  (None, 3, 3, 1536)  0           ['block6l_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6l_se_squeeze (GlobalAver  (None, 1536)        0           ['block6l_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6l_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6l_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6l_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6l_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6l_activation[0][0]',     \n",
      "                                                                  'block6l_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6l_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6l_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6l_add (Add)              (None, 3, 3, 256)    0           ['block6l_project_bn[0][0]',     \n",
      "                                                                  'block6k_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6m_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6l_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6m_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6m_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block6m_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6m_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6m_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6m_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6m_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6m_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6m_activation (Activation  (None, 3, 3, 1536)  0           ['block6m_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6m_se_squeeze (GlobalAver  (None, 1536)        0           ['block6m_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6m_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6m_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6m_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6m_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6m_activation[0][0]',     \n",
      "                                                                  'block6m_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6m_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6m_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6m_add (Add)              (None, 3, 3, 256)    0           ['block6m_project_bn[0][0]',     \n",
      "                                                                  'block6l_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6n_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6m_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6n_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6n_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6n_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6n_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6n_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6n_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6n_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6n_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6n_activation (Activation  (None, 3, 3, 1536)  0           ['block6n_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6n_se_squeeze (GlobalAver  (None, 1536)        0           ['block6n_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6n_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6n_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6n_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6n_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6n_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6n_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6n_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6n_activation[0][0]',     \n",
      "                                                                  'block6n_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6n_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6n_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6n_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6n_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6n_add (Add)              (None, 3, 3, 256)    0           ['block6n_project_bn[0][0]',     \n",
      "                                                                  'block6m_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6o_expand_conv (Conv2D)   (None, 3, 3, 1536)   393216      ['block6n_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6o_expand_bn (BatchNormal  (None, 3, 3, 1536)  6144        ['block6o_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6o_expand_activation (Act  (None, 3, 3, 1536)  0           ['block6o_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6o_dwconv2 (DepthwiseConv  (None, 3, 3, 1536)  13824       ['block6o_expand_activation[0][0]\n",
      " 2D)                                                             ']                               \n",
      "                                                                                                  \n",
      " block6o_bn (BatchNormalization  (None, 3, 3, 1536)  6144        ['block6o_dwconv2[0][0]']        \n",
      " )                                                                                                \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " block6o_activation (Activation  (None, 3, 3, 1536)  0           ['block6o_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6o_se_squeeze (GlobalAver  (None, 1536)        0           ['block6o_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6o_se_reshape (Reshape)   (None, 1, 1, 1536)   0           ['block6o_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6o_se_reduce (Conv2D)     (None, 1, 1, 64)     98368       ['block6o_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6o_se_expand (Conv2D)     (None, 1, 1, 1536)   99840       ['block6o_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6o_se_excite (Multiply)   (None, 3, 3, 1536)   0           ['block6o_activation[0][0]',     \n",
      "                                                                  'block6o_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6o_project_conv (Conv2D)  (None, 3, 3, 256)    393216      ['block6o_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6o_project_bn (BatchNorma  (None, 3, 3, 256)   1024        ['block6o_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6o_add (Add)              (None, 3, 3, 256)    0           ['block6o_project_bn[0][0]',     \n",
      "                                                                  'block6n_add[0][0]']            \n",
      "                                                                                                  \n",
      " top_conv (Conv2D)              (None, 3, 3, 1280)   327680      ['block6o_add[0][0]']            \n",
      "                                                                                                  \n",
      " top_bn (BatchNormalization)    (None, 3, 3, 1280)   5120        ['top_conv[0][0]']               \n",
      "                                                                                                  \n",
      " top_activation (Activation)    (None, 3, 3, 1280)   0           ['top_bn[0][0]']                 \n",
      "                                                                                                  \n",
      " avg_pool (GlobalAveragePooling  (None, 1280)        0           ['top_activation[0][0]']         \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " top_dropout (Dropout)          (None, 1280)         0           ['avg_pool[0][0]']               \n",
      "                                                                                                  \n",
      " predictions (Dense)            (None, 8)            10248       ['top_dropout[0][0]']            \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 20,341,176\n",
      "Trainable params: 20,187,304\n",
      "Non-trainable params: 153,872\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "VAksXWJv7yZ3"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.01),\n",
    "    loss=tf.keras.losses.CategoricalCrossentropy(),\n",
    "    metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()],\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_callback = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    factor=0.1,\n",
    "    patience=10,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop_callback = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    min_delta=0,\n",
    "    patience=20,\n",
    "    restore_best_weights=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_loss_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/min_loss.h5',\n",
    "    monitor='val_loss',\n",
    "    mode='min',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_accuracy_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/max_accuracy.h5',\n",
    "    monitor='val_accuracy',\n",
    "    mode='max',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_precision_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/max_precision.h5',\n",
    "    monitor='val_precision',\n",
    "    mode='max',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_recall_callback = tf.keras.callbacks.ModelCheckpoint(\n",
    "    f'../models/{notebook_name}/max_recall.h5',\n",
    "    monitor='val_recall',\n",
    "    mode='max',\n",
    "    save_best_only=True,\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "_yx8z1758eIJ",
    "outputId": "86b9d417-18b6-40c0-f7ad-494ac6f9a5aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "\n",
      "Epoch 1: val_loss improved from inf to 1.68592, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 1: val_accuracy improved from -inf to 0.38170, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 1: val_precision improved from -inf to 0.44939, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 1: val_recall improved from -inf to 0.23096, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 190s - loss: 1.9051 - accuracy: 0.3236 - precision: 0.3305 - recall: 0.0544 - val_loss: 1.6859 - val_accuracy: 0.3817 - val_precision: 0.4494 - val_recall: 0.2310 - lr: 0.0100 - 190s/epoch - 242ms/step\n",
      "Epoch 2/300\n",
      "\n",
      "Epoch 2: val_loss improved from 1.68592 to 1.46460, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 2: val_accuracy improved from 0.38170 to 0.47352, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 2: val_precision improved from 0.44939 to 0.52844, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 2: val_recall improved from 0.23096 to 0.34065, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 1.4918 - accuracy: 0.4306 - precision: 0.5526 - recall: 0.1480 - val_loss: 1.4646 - val_accuracy: 0.4735 - val_precision: 0.5284 - val_recall: 0.3406 - lr: 0.0100 - 169s/epoch - 216ms/step\n",
      "Epoch 3/300\n",
      "\n",
      "Epoch 3: val_loss improved from 1.46460 to 1.18515, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 3: val_accuracy improved from 0.47352 to 0.58477, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 3: val_precision improved from 0.52844 to 0.70892, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 3: val_recall improved from 0.34065 to 0.41366, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 1.3302 - accuracy: 0.5317 - precision: 0.6433 - recall: 0.3444 - val_loss: 1.1852 - val_accuracy: 0.5848 - val_precision: 0.7089 - val_recall: 0.4137 - lr: 0.0100 - 169s/epoch - 215ms/step\n",
      "Epoch 4/300\n",
      "\n",
      "Epoch 4: val_loss improved from 1.18515 to 1.09385, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 4: val_accuracy improved from 0.58477 to 0.62864, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 4: val_precision improved from 0.70892 to 0.71019, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 4: val_recall improved from 0.41366 to 0.53526, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 1.2065 - accuracy: 0.5782 - precision: 0.6922 - recall: 0.4234 - val_loss: 1.0939 - val_accuracy: 0.6286 - val_precision: 0.7102 - val_recall: 0.5353 - lr: 0.0100 - 169s/epoch - 216ms/step\n",
      "Epoch 5/300\n",
      "\n",
      "Epoch 5: val_loss improved from 1.09385 to 1.06227, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 5: val_accuracy did not improve from 0.62864\n",
      "\n",
      "Epoch 5: val_precision improved from 0.71019 to 0.75501, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 5: val_recall did not improve from 0.53526\n",
      "783/783 - 168s - loss: 1.1010 - accuracy: 0.6145 - precision: 0.7219 - recall: 0.4859 - val_loss: 1.0623 - val_accuracy: 0.6221 - val_precision: 0.7550 - val_recall: 0.4723 - lr: 0.0100 - 168s/epoch - 214ms/step\n",
      "Epoch 6/300\n",
      "\n",
      "Epoch 6: val_loss improved from 1.06227 to 0.96077, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 6: val_accuracy improved from 0.62864 to 0.66813, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 6: val_precision improved from 0.75501 to 0.76730, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 6: val_recall improved from 0.53526 to 0.56628, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 1.0014 - accuracy: 0.6472 - precision: 0.7411 - recall: 0.5399 - val_loss: 0.9608 - val_accuracy: 0.6681 - val_precision: 0.7673 - val_recall: 0.5663 - lr: 0.0100 - 169s/epoch - 216ms/step\n",
      "Epoch 7/300\n",
      "\n",
      "Epoch 7: val_loss improved from 0.96077 to 0.83255, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 7: val_accuracy improved from 0.66813 to 0.71012, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 7: val_precision improved from 0.76730 to 0.78058, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 7: val_recall improved from 0.56628 to 0.62990, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 0.9256 - accuracy: 0.6754 - precision: 0.7608 - recall: 0.5806 - val_loss: 0.8325 - val_accuracy: 0.7101 - val_precision: 0.7806 - val_recall: 0.6299 - lr: 0.0100 - 169s/epoch - 216ms/step\n",
      "Epoch 8/300\n",
      "\n",
      "Epoch 8: val_loss improved from 0.83255 to 0.79007, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 8: val_accuracy improved from 0.71012 to 0.71294, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 8: val_precision improved from 0.78058 to 0.79189, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 8: val_recall improved from 0.62990 to 0.64275, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 0.8702 - accuracy: 0.6941 - precision: 0.7672 - recall: 0.6094 - val_loss: 0.7901 - val_accuracy: 0.7129 - val_precision: 0.7919 - val_recall: 0.6427 - lr: 0.0100 - 169s/epoch - 216ms/step\n",
      "Epoch 9/300\n",
      "\n",
      "Epoch 9: val_loss did not improve from 0.79007\n",
      "\n",
      "Epoch 9: val_accuracy improved from 0.71294 to 0.73958, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 9: val_precision did not improve from 0.79189\n",
      "\n",
      "Epoch 9: val_recall improved from 0.64275 to 0.71764, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 167s - loss: 0.8248 - accuracy: 0.7082 - precision: 0.7787 - recall: 0.6360 - val_loss: 0.7988 - val_accuracy: 0.7396 - val_precision: 0.7664 - val_recall: 0.7176 - lr: 0.0100 - 167s/epoch - 214ms/step\n",
      "Epoch 10/300\n",
      "\n",
      "Epoch 10: val_loss did not improve from 0.79007\n",
      "\n",
      "Epoch 10: val_accuracy did not improve from 0.73958\n",
      "\n",
      "Epoch 10: val_precision did not improve from 0.79189\n",
      "\n",
      "Epoch 10: val_recall did not improve from 0.71764\n",
      "783/783 - 166s - loss: 0.7924 - accuracy: 0.7187 - precision: 0.7861 - recall: 0.6486 - val_loss: 0.8077 - val_accuracy: 0.7092 - val_precision: 0.7590 - val_recall: 0.6572 - lr: 0.0100 - 166s/epoch - 211ms/step\n",
      "Epoch 11/300\n",
      "\n",
      "Epoch 11: val_loss improved from 0.79007 to 0.77600, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 11: val_accuracy did not improve from 0.73958\n",
      "\n",
      "Epoch 11: val_precision did not improve from 0.79189\n",
      "\n",
      "Epoch 11: val_recall did not improve from 0.71764\n",
      "783/783 - 166s - loss: 0.7667 - accuracy: 0.7304 - precision: 0.7888 - recall: 0.6630 - val_loss: 0.7760 - val_accuracy: 0.7292 - val_precision: 0.7898 - val_recall: 0.6725 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 12/300\n",
      "\n",
      "Epoch 12: val_loss improved from 0.77600 to 0.76992, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 12: val_accuracy did not improve from 0.73958\n",
      "\n",
      "Epoch 12: val_precision did not improve from 0.79189\n",
      "\n",
      "Epoch 12: val_recall did not improve from 0.71764\n",
      "783/783 - 166s - loss: 0.7318 - accuracy: 0.7422 - precision: 0.7976 - recall: 0.6814 - val_loss: 0.7699 - val_accuracy: 0.7311 - val_precision: 0.7917 - val_recall: 0.6647 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 13/300\n",
      "\n",
      "Epoch 13: val_loss improved from 0.76992 to 0.66079, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 13: val_accuracy improved from 0.73958 to 0.76966, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 13: val_precision improved from 0.79189 to 0.81825, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 13: val_recall improved from 0.71764 to 0.72798, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 0.7126 - accuracy: 0.7490 - precision: 0.8017 - recall: 0.6936 - val_loss: 0.6608 - val_accuracy: 0.7697 - val_precision: 0.8182 - val_recall: 0.7280 - lr: 0.0100 - 169s/epoch - 216ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 14/300\n",
      "\n",
      "Epoch 14: val_loss did not improve from 0.66079\n",
      "\n",
      "Epoch 14: val_accuracy did not improve from 0.76966\n",
      "\n",
      "Epoch 14: val_precision did not improve from 0.81825\n",
      "\n",
      "Epoch 14: val_recall did not improve from 0.72798\n",
      "783/783 - 166s - loss: 0.6994 - accuracy: 0.7532 - precision: 0.8057 - recall: 0.7015 - val_loss: 0.6846 - val_accuracy: 0.7603 - val_precision: 0.8026 - val_recall: 0.7111 - lr: 0.0100 - 166s/epoch - 211ms/step\n",
      "Epoch 15/300\n",
      "\n",
      "Epoch 15: val_loss did not improve from 0.66079\n",
      "\n",
      "Epoch 15: val_accuracy did not improve from 0.76966\n",
      "\n",
      "Epoch 15: val_precision did not improve from 0.81825\n",
      "\n",
      "Epoch 15: val_recall did not improve from 0.72798\n",
      "783/783 - 166s - loss: 0.6735 - accuracy: 0.7635 - precision: 0.8104 - recall: 0.7137 - val_loss: 0.6663 - val_accuracy: 0.7647 - val_precision: 0.8007 - val_recall: 0.7277 - lr: 0.0100 - 166s/epoch - 211ms/step\n",
      "Epoch 16/300\n",
      "\n",
      "Epoch 16: val_loss did not improve from 0.66079\n",
      "\n",
      "Epoch 16: val_accuracy did not improve from 0.76966\n",
      "\n",
      "Epoch 16: val_precision did not improve from 0.81825\n",
      "\n",
      "Epoch 16: val_recall did not improve from 0.72798\n",
      "783/783 - 166s - loss: 0.6597 - accuracy: 0.7664 - precision: 0.8128 - recall: 0.7199 - val_loss: 0.8186 - val_accuracy: 0.7449 - val_precision: 0.7760 - val_recall: 0.7101 - lr: 0.0100 - 166s/epoch - 211ms/step\n",
      "Epoch 17/300\n",
      "\n",
      "Epoch 17: val_loss improved from 0.66079 to 0.62133, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 17: val_accuracy improved from 0.76966 to 0.77844, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 17: val_precision improved from 0.81825 to 0.82140, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 17: val_recall improved from 0.72798 to 0.73363, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 0.6498 - accuracy: 0.7723 - precision: 0.8182 - recall: 0.7255 - val_loss: 0.6213 - val_accuracy: 0.7784 - val_precision: 0.8214 - val_recall: 0.7336 - lr: 0.0100 - 169s/epoch - 216ms/step\n",
      "Epoch 18/300\n",
      "\n",
      "Epoch 18: val_loss improved from 0.62133 to 0.57803, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 18: val_accuracy improved from 0.77844 to 0.80288, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 18: val_precision improved from 0.82140 to 0.84865, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 18: val_recall improved from 0.73363 to 0.76966, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 0.6298 - accuracy: 0.7781 - precision: 0.8202 - recall: 0.7351 - val_loss: 0.5780 - val_accuracy: 0.8029 - val_precision: 0.8487 - val_recall: 0.7697 - lr: 0.0100 - 169s/epoch - 216ms/step\n",
      "Epoch 19/300\n",
      "\n",
      "Epoch 19: val_loss did not improve from 0.57803\n",
      "\n",
      "Epoch 19: val_accuracy did not improve from 0.80288\n",
      "\n",
      "Epoch 19: val_precision did not improve from 0.84865\n",
      "\n",
      "Epoch 19: val_recall did not improve from 0.76966\n",
      "783/783 - 166s - loss: 0.6150 - accuracy: 0.7823 - precision: 0.8203 - recall: 0.7393 - val_loss: 0.6401 - val_accuracy: 0.7838 - val_precision: 0.8168 - val_recall: 0.7377 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 20/300\n",
      "\n",
      "Epoch 20: val_loss did not improve from 0.57803\n",
      "\n",
      "Epoch 20: val_accuracy did not improve from 0.80288\n",
      "\n",
      "Epoch 20: val_precision did not improve from 0.84865\n",
      "\n",
      "Epoch 20: val_recall did not improve from 0.76966\n",
      "783/783 - 165s - loss: 0.6058 - accuracy: 0.7848 - precision: 0.8224 - recall: 0.7459 - val_loss: 0.6112 - val_accuracy: 0.7910 - val_precision: 0.8213 - val_recall: 0.7618 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 21/300\n",
      "\n",
      "Epoch 21: val_loss improved from 0.57803 to 0.56074, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 21: val_accuracy did not improve from 0.80288\n",
      "\n",
      "Epoch 21: val_precision did not improve from 0.84865\n",
      "\n",
      "Epoch 21: val_recall did not improve from 0.76966\n",
      "783/783 - 166s - loss: 0.6041 - accuracy: 0.7882 - precision: 0.8254 - recall: 0.7476 - val_loss: 0.5607 - val_accuracy: 0.7938 - val_precision: 0.8283 - val_recall: 0.7603 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 22/300\n",
      "\n",
      "Epoch 22: val_loss improved from 0.56074 to 0.55343, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 22: val_accuracy did not improve from 0.80288\n",
      "\n",
      "Epoch 22: val_precision did not improve from 0.84865\n",
      "\n",
      "Epoch 22: val_recall did not improve from 0.76966\n",
      "783/783 - 166s - loss: 0.5900 - accuracy: 0.7940 - precision: 0.8302 - recall: 0.7538 - val_loss: 0.5534 - val_accuracy: 0.8029 - val_precision: 0.8380 - val_recall: 0.7668 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 23/300\n",
      "\n",
      "Epoch 23: val_loss improved from 0.55343 to 0.54962, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 23: val_accuracy improved from 0.80288 to 0.80351, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 23: val_precision did not improve from 0.84865\n",
      "\n",
      "Epoch 23: val_recall did not improve from 0.76966\n",
      "783/783 - 167s - loss: 0.5736 - accuracy: 0.7981 - precision: 0.8347 - recall: 0.7610 - val_loss: 0.5496 - val_accuracy: 0.8035 - val_precision: 0.8423 - val_recall: 0.7631 - lr: 0.0100 - 167s/epoch - 214ms/step\n",
      "Epoch 24/300\n",
      "\n",
      "Epoch 24: val_loss did not improve from 0.54962\n",
      "\n",
      "Epoch 24: val_accuracy did not improve from 0.80351\n",
      "\n",
      "Epoch 24: val_precision did not improve from 0.84865\n",
      "\n",
      "Epoch 24: val_recall did not improve from 0.76966\n",
      "783/783 - 165s - loss: 0.5708 - accuracy: 0.8010 - precision: 0.8358 - recall: 0.7645 - val_loss: 0.5624 - val_accuracy: 0.7929 - val_precision: 0.8230 - val_recall: 0.7650 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 25/300\n",
      "\n",
      "Epoch 25: val_loss improved from 0.54962 to 0.54811, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 25: val_accuracy improved from 0.80351 to 0.80727, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 25: val_precision did not improve from 0.84865\n",
      "\n",
      "Epoch 25: val_recall improved from 0.76966 to 0.78283, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 168s - loss: 0.5569 - accuracy: 0.8057 - precision: 0.8406 - recall: 0.7719 - val_loss: 0.5481 - val_accuracy: 0.8073 - val_precision: 0.8352 - val_recall: 0.7828 - lr: 0.0100 - 168s/epoch - 214ms/step\n",
      "Epoch 26/300\n",
      "\n",
      "Epoch 26: val_loss improved from 0.54811 to 0.53706, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 26: val_accuracy improved from 0.80727 to 0.81385, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 26: val_precision improved from 0.84865 to 0.85248, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 26: val_recall did not improve from 0.78283\n",
      "783/783 - 168s - loss: 0.5520 - accuracy: 0.8052 - precision: 0.8387 - recall: 0.7706 - val_loss: 0.5371 - val_accuracy: 0.8139 - val_precision: 0.8525 - val_recall: 0.7697 - lr: 0.0100 - 168s/epoch - 214ms/step\n",
      "Epoch 27/300\n",
      "\n",
      "Epoch 27: val_loss did not improve from 0.53706\n",
      "\n",
      "Epoch 27: val_accuracy did not improve from 0.81385\n",
      "\n",
      "Epoch 27: val_precision did not improve from 0.85248\n",
      "\n",
      "Epoch 27: val_recall did not improve from 0.78283\n",
      "783/783 - 166s - loss: 0.5426 - accuracy: 0.8078 - precision: 0.8408 - recall: 0.7762 - val_loss: 0.5652 - val_accuracy: 0.8001 - val_precision: 0.8227 - val_recall: 0.7766 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 28/300\n",
      "\n",
      "Epoch 28: val_loss did not improve from 0.53706\n",
      "\n",
      "Epoch 28: val_accuracy did not improve from 0.81385\n",
      "\n",
      "Epoch 28: val_precision did not improve from 0.85248\n",
      "\n",
      "Epoch 28: val_recall improved from 0.78283 to 0.78909, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 166s - loss: 0.5322 - accuracy: 0.8131 - precision: 0.8440 - recall: 0.7819 - val_loss: 0.5767 - val_accuracy: 0.8079 - val_precision: 0.8286 - val_recall: 0.7891 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 29/300\n",
      "\n",
      "Epoch 29: val_loss improved from 0.53706 to 0.52131, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 29: val_accuracy improved from 0.81385 to 0.82012, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 29: val_precision improved from 0.85248 to 0.85990, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 29: val_recall did not improve from 0.78909\n",
      "783/783 - 168s - loss: 0.5286 - accuracy: 0.8164 - precision: 0.8451 - recall: 0.7851 - val_loss: 0.5213 - val_accuracy: 0.8201 - val_precision: 0.8599 - val_recall: 0.7809 - lr: 0.0100 - 168s/epoch - 215ms/step\n",
      "Epoch 30/300\n",
      "\n",
      "Epoch 30: val_loss did not improve from 0.52131\n",
      "\n",
      "Epoch 30: val_accuracy did not improve from 0.82012\n",
      "\n",
      "Epoch 30: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 30: val_recall did not improve from 0.78909\n",
      "783/783 - 165s - loss: 0.5161 - accuracy: 0.8165 - precision: 0.8479 - recall: 0.7866 - val_loss: 0.6857 - val_accuracy: 0.7628 - val_precision: 0.7905 - val_recall: 0.7330 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 31/300\n",
      "\n",
      "Epoch 31: val_loss did not improve from 0.52131\n",
      "\n",
      "Epoch 31: val_accuracy did not improve from 0.82012\n",
      "\n",
      "Epoch 31: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 31: val_recall did not improve from 0.78909\n",
      "783/783 - 165s - loss: 0.5082 - accuracy: 0.8179 - precision: 0.8454 - recall: 0.7892 - val_loss: 0.5903 - val_accuracy: 0.7982 - val_precision: 0.8241 - val_recall: 0.7753 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 32/300\n",
      "\n",
      "Epoch 32: val_loss improved from 0.52131 to 0.50964, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 32: val_accuracy improved from 0.82012 to 0.83453, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 32: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 32: val_recall improved from 0.78909 to 0.81228, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 168s - loss: 0.4997 - accuracy: 0.8216 - precision: 0.8501 - recall: 0.7948 - val_loss: 0.5096 - val_accuracy: 0.8345 - val_precision: 0.8512 - val_recall: 0.8123 - lr: 0.0100 - 168s/epoch - 214ms/step\n",
      "Epoch 33/300\n",
      "\n",
      "Epoch 33: val_loss did not improve from 0.50964\n",
      "\n",
      "Epoch 33: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 33: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 33: val_recall did not improve from 0.81228\n",
      "783/783 - 165s - loss: 0.5017 - accuracy: 0.8247 - precision: 0.8529 - recall: 0.7976 - val_loss: 0.5379 - val_accuracy: 0.8157 - val_precision: 0.8432 - val_recall: 0.7935 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 34/300\n",
      "\n",
      "Epoch 34: val_loss did not improve from 0.50964\n",
      "\n",
      "Epoch 34: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 34: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 34: val_recall did not improve from 0.81228\n",
      "783/783 - 165s - loss: 0.4959 - accuracy: 0.8260 - precision: 0.8538 - recall: 0.7967 - val_loss: 0.5107 - val_accuracy: 0.8204 - val_precision: 0.8485 - val_recall: 0.7966 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 35/300\n",
      "\n",
      "Epoch 35: val_loss did not improve from 0.50964\n",
      "\n",
      "Epoch 35: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 35: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 35: val_recall did not improve from 0.81228\n",
      "783/783 - 165s - loss: 0.4868 - accuracy: 0.8282 - precision: 0.8563 - recall: 0.8017 - val_loss: 0.5509 - val_accuracy: 0.8041 - val_precision: 0.8299 - val_recall: 0.7769 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 36/300\n",
      "\n",
      "Epoch 36: val_loss did not improve from 0.50964\n",
      "\n",
      "Epoch 36: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 36: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 36: val_recall did not improve from 0.81228\n",
      "783/783 - 165s - loss: 0.4868 - accuracy: 0.8293 - precision: 0.8576 - recall: 0.8029 - val_loss: 0.5215 - val_accuracy: 0.8223 - val_precision: 0.8491 - val_recall: 0.7972 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 37/300\n",
      "\n",
      "Epoch 37: val_loss did not improve from 0.50964\n",
      "\n",
      "Epoch 37: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 37: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 37: val_recall did not improve from 0.81228\n",
      "783/783 - 165s - loss: 0.4718 - accuracy: 0.8348 - precision: 0.8606 - recall: 0.8082 - val_loss: 0.5488 - val_accuracy: 0.8242 - val_precision: 0.8395 - val_recall: 0.8095 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 38/300\n",
      "\n",
      "Epoch 38: val_loss did not improve from 0.50964\n",
      "\n",
      "Epoch 38: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 38: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 38: val_recall did not improve from 0.81228\n",
      "783/783 - 165s - loss: 0.4731 - accuracy: 0.8353 - precision: 0.8603 - recall: 0.8093 - val_loss: 0.5220 - val_accuracy: 0.8292 - val_precision: 0.8485 - val_recall: 0.8110 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 39/300\n",
      "\n",
      "Epoch 39: val_loss did not improve from 0.50964\n",
      "\n",
      "Epoch 39: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 39: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 39: val_recall did not improve from 0.81228\n",
      "783/783 - 165s - loss: 0.4693 - accuracy: 0.8355 - precision: 0.8585 - recall: 0.8096 - val_loss: 0.5170 - val_accuracy: 0.8179 - val_precision: 0.8476 - val_recall: 0.7929 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 40/300\n",
      "\n",
      "Epoch 40: val_loss improved from 0.50964 to 0.49252, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 40: val_accuracy did not improve from 0.83453\n",
      "\n",
      "Epoch 40: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 40: val_recall did not improve from 0.81228\n",
      "783/783 - 166s - loss: 0.4658 - accuracy: 0.8361 - precision: 0.8617 - recall: 0.8113 - val_loss: 0.4925 - val_accuracy: 0.8345 - val_precision: 0.8555 - val_recall: 0.8092 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 41/300\n",
      "\n",
      "Epoch 41: val_loss improved from 0.49252 to 0.47300, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 41: val_accuracy improved from 0.83453 to 0.83892, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 41: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 41: val_recall improved from 0.81228 to 0.82169, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 168s - loss: 0.4555 - accuracy: 0.8415 - precision: 0.8635 - recall: 0.8167 - val_loss: 0.4730 - val_accuracy: 0.8389 - val_precision: 0.8557 - val_recall: 0.8217 - lr: 0.0100 - 168s/epoch - 214ms/step\n",
      "Epoch 42/300\n",
      "\n",
      "Epoch 42: val_loss did not improve from 0.47300\n",
      "\n",
      "Epoch 42: val_accuracy improved from 0.83892 to 0.83924, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 42: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 42: val_recall did not improve from 0.82169\n",
      "783/783 - 166s - loss: 0.4545 - accuracy: 0.8406 - precision: 0.8642 - recall: 0.8167 - val_loss: 0.4807 - val_accuracy: 0.8392 - val_precision: 0.8566 - val_recall: 0.8145 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 43/300\n",
      "\n",
      "Epoch 43: val_loss did not improve from 0.47300\n",
      "\n",
      "Epoch 43: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 43: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 43: val_recall did not improve from 0.82169\n",
      "783/783 - 165s - loss: 0.4448 - accuracy: 0.8424 - precision: 0.8664 - recall: 0.8211 - val_loss: 0.4871 - val_accuracy: 0.8301 - val_precision: 0.8497 - val_recall: 0.8148 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 44/300\n",
      "\n",
      "Epoch 44: val_loss improved from 0.47300 to 0.45555, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 44: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 44: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 44: val_recall did not improve from 0.82169\n",
      "783/783 - 166s - loss: 0.4439 - accuracy: 0.8438 - precision: 0.8666 - recall: 0.8205 - val_loss: 0.4556 - val_accuracy: 0.8374 - val_precision: 0.8578 - val_recall: 0.8204 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 45/300\n",
      "\n",
      "Epoch 45: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 45: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 45: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 45: val_recall did not improve from 0.82169\n",
      "783/783 - 166s - loss: 0.4391 - accuracy: 0.8458 - precision: 0.8682 - recall: 0.8230 - val_loss: 0.4844 - val_accuracy: 0.8289 - val_precision: 0.8552 - val_recall: 0.8107 - lr: 0.0100 - 166s/epoch - 211ms/step\n",
      "Epoch 46/300\n",
      "\n",
      "Epoch 46: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 46: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 46: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 46: val_recall improved from 0.82169 to 0.82827, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 166s - loss: 0.4336 - accuracy: 0.8482 - precision: 0.8687 - recall: 0.8272 - val_loss: 0.5452 - val_accuracy: 0.8361 - val_precision: 0.8520 - val_recall: 0.8283 - lr: 0.0100 - 166s/epoch - 212ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 47/300\n",
      "\n",
      "Epoch 47: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 47: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 47: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 47: val_recall did not improve from 0.82827\n",
      "783/783 - 165s - loss: 0.4264 - accuracy: 0.8500 - precision: 0.8717 - recall: 0.8291 - val_loss: 0.4757 - val_accuracy: 0.8367 - val_precision: 0.8557 - val_recall: 0.8229 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 48/300\n",
      "\n",
      "Epoch 48: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 48: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 48: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 48: val_recall did not improve from 0.82827\n",
      "783/783 - 165s - loss: 0.4209 - accuracy: 0.8500 - precision: 0.8704 - recall: 0.8296 - val_loss: 0.5059 - val_accuracy: 0.8308 - val_precision: 0.8510 - val_recall: 0.8126 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 49/300\n",
      "\n",
      "Epoch 49: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 49: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 49: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 49: val_recall did not improve from 0.82827\n",
      "783/783 - 165s - loss: 0.4168 - accuracy: 0.8542 - precision: 0.8745 - recall: 0.8337 - val_loss: 0.5032 - val_accuracy: 0.8289 - val_precision: 0.8488 - val_recall: 0.8073 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 50/300\n",
      "\n",
      "Epoch 50: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 50: val_accuracy did not improve from 0.83924\n",
      "\n",
      "Epoch 50: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 50: val_recall did not improve from 0.82827\n",
      "783/783 - 165s - loss: 0.4154 - accuracy: 0.8536 - precision: 0.8735 - recall: 0.8334 - val_loss: 0.5055 - val_accuracy: 0.8292 - val_precision: 0.8588 - val_recall: 0.8007 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 51/300\n",
      "\n",
      "Epoch 51: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 51: val_accuracy improved from 0.83924 to 0.84174, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 51: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 51: val_recall improved from 0.82827 to 0.83265, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 167s - loss: 0.4070 - accuracy: 0.8533 - precision: 0.8740 - recall: 0.8353 - val_loss: 0.4998 - val_accuracy: 0.8417 - val_precision: 0.8585 - val_recall: 0.8327 - lr: 0.0100 - 167s/epoch - 213ms/step\n",
      "Epoch 52/300\n",
      "\n",
      "Epoch 52: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 52: val_accuracy did not improve from 0.84174\n",
      "\n",
      "Epoch 52: val_precision did not improve from 0.85990\n",
      "\n",
      "Epoch 52: val_recall did not improve from 0.83265\n",
      "783/783 - 165s - loss: 0.4011 - accuracy: 0.8584 - precision: 0.8782 - recall: 0.8396 - val_loss: 0.4713 - val_accuracy: 0.8345 - val_precision: 0.8575 - val_recall: 0.8129 - lr: 0.0100 - 165s/epoch - 211ms/step\n",
      "Epoch 53/300\n",
      "\n",
      "Epoch 53: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 53: val_accuracy did not improve from 0.84174\n",
      "\n",
      "Epoch 53: val_precision improved from 0.85990 to 0.86217, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 53: val_recall did not improve from 0.83265\n",
      "783/783 - 166s - loss: 0.4088 - accuracy: 0.8570 - precision: 0.8766 - recall: 0.8374 - val_loss: 0.4942 - val_accuracy: 0.8383 - val_precision: 0.8622 - val_recall: 0.8135 - lr: 0.0100 - 166s/epoch - 212ms/step\n",
      "Epoch 54/300\n",
      "\n",
      "Epoch 54: ReduceLROnPlateau reducing learning rate to 0.0009999999776482583.\n",
      "\n",
      "Epoch 54: val_loss did not improve from 0.45555\n",
      "\n",
      "Epoch 54: val_accuracy improved from 0.84174 to 0.84268, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 54: val_precision did not improve from 0.86217\n",
      "\n",
      "Epoch 54: val_recall improved from 0.83265 to 0.83359, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 167s - loss: 0.4024 - accuracy: 0.8580 - precision: 0.8788 - recall: 0.8383 - val_loss: 0.4660 - val_accuracy: 0.8427 - val_precision: 0.8567 - val_recall: 0.8336 - lr: 0.0100 - 167s/epoch - 213ms/step\n",
      "Epoch 55/300\n",
      "\n",
      "Epoch 55: val_loss improved from 0.45555 to 0.40952, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 55: val_accuracy improved from 0.84268 to 0.86337, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 55: val_precision improved from 0.86217 to 0.87799, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 55: val_recall improved from 0.83359 to 0.85020, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 0.3270 - accuracy: 0.8827 - precision: 0.8996 - recall: 0.8670 - val_loss: 0.4095 - val_accuracy: 0.8634 - val_precision: 0.8780 - val_recall: 0.8502 - lr: 1.0000e-03 - 169s/epoch - 215ms/step\n",
      "Epoch 56/300\n",
      "\n",
      "Epoch 56: val_loss did not improve from 0.40952\n",
      "\n",
      "Epoch 56: val_accuracy did not improve from 0.86337\n",
      "\n",
      "Epoch 56: val_precision did not improve from 0.87799\n",
      "\n",
      "Epoch 56: val_recall improved from 0.85020 to 0.85083, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 166s - loss: 0.3027 - accuracy: 0.8925 - precision: 0.9067 - recall: 0.8793 - val_loss: 0.4156 - val_accuracy: 0.8615 - val_precision: 0.8758 - val_recall: 0.8508 - lr: 1.0000e-03 - 166s/epoch - 212ms/step\n",
      "Epoch 57/300\n",
      "\n",
      "Epoch 57: val_loss did not improve from 0.40952\n",
      "\n",
      "Epoch 57: val_accuracy improved from 0.86337 to 0.86431, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 57: val_precision did not improve from 0.87799\n",
      "\n",
      "Epoch 57: val_recall improved from 0.85083 to 0.85271, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 167s - loss: 0.2998 - accuracy: 0.8924 - precision: 0.9053 - recall: 0.8802 - val_loss: 0.4140 - val_accuracy: 0.8643 - val_precision: 0.8749 - val_recall: 0.8527 - lr: 1.0000e-03 - 167s/epoch - 213ms/step\n",
      "Epoch 58/300\n",
      "\n",
      "Epoch 58: val_loss improved from 0.40952 to 0.40681, saving model to ../models/EfficientNetV2S_96x96_32\\min_loss.h5\n",
      "\n",
      "Epoch 58: val_accuracy improved from 0.86431 to 0.86587, saving model to ../models/EfficientNetV2S_96x96_32\\max_accuracy.h5\n",
      "\n",
      "Epoch 58: val_precision improved from 0.87799 to 0.87981, saving model to ../models/EfficientNetV2S_96x96_32\\max_precision.h5\n",
      "\n",
      "Epoch 58: val_recall improved from 0.85271 to 0.85334, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 169s - loss: 0.2928 - accuracy: 0.8951 - precision: 0.9074 - recall: 0.8836 - val_loss: 0.4068 - val_accuracy: 0.8659 - val_precision: 0.8798 - val_recall: 0.8533 - lr: 1.0000e-03 - 169s/epoch - 216ms/step\n",
      "Epoch 59/300\n",
      "\n",
      "Epoch 59: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 59: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 59: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 59: val_recall did not improve from 0.85334\n",
      "783/783 - 166s - loss: 0.2862 - accuracy: 0.8989 - precision: 0.9099 - recall: 0.8881 - val_loss: 0.4098 - val_accuracy: 0.8580 - val_precision: 0.8723 - val_recall: 0.8480 - lr: 1.0000e-03 - 166s/epoch - 212ms/step\n",
      "Epoch 60/300\n",
      "\n",
      "Epoch 60: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 60: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 60: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 60: val_recall did not improve from 0.85334\n",
      "783/783 - 166s - loss: 0.2804 - accuracy: 0.8975 - precision: 0.9089 - recall: 0.8864 - val_loss: 0.4143 - val_accuracy: 0.8602 - val_precision: 0.8710 - val_recall: 0.8483 - lr: 1.0000e-03 - 166s/epoch - 211ms/step\n",
      "Epoch 61/300\n",
      "\n",
      "Epoch 61: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 61: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 61: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 61: val_recall did not improve from 0.85334\n",
      "783/783 - 165s - loss: 0.2794 - accuracy: 0.8987 - precision: 0.9093 - recall: 0.8878 - val_loss: 0.4271 - val_accuracy: 0.8602 - val_precision: 0.8725 - val_recall: 0.8511 - lr: 1.0000e-03 - 165s/epoch - 211ms/step\n",
      "Epoch 62/300\n",
      "\n",
      "Epoch 62: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 62: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 62: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 62: val_recall did not improve from 0.85334\n",
      "783/783 - 165s - loss: 0.2762 - accuracy: 0.8999 - precision: 0.9117 - recall: 0.8889 - val_loss: 0.4114 - val_accuracy: 0.8599 - val_precision: 0.8748 - val_recall: 0.8521 - lr: 1.0000e-03 - 165s/epoch - 211ms/step\n",
      "Epoch 63/300\n",
      "\n",
      "Epoch 63: val_loss did not improve from 0.40681\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 63: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 63: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 63: val_recall did not improve from 0.85334\n",
      "783/783 - 165s - loss: 0.2754 - accuracy: 0.8997 - precision: 0.9111 - recall: 0.8901 - val_loss: 0.4233 - val_accuracy: 0.8571 - val_precision: 0.8717 - val_recall: 0.8477 - lr: 1.0000e-03 - 165s/epoch - 211ms/step\n",
      "Epoch 64/300\n",
      "\n",
      "Epoch 64: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 64: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 64: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 64: val_recall did not improve from 0.85334\n",
      "783/783 - 171s - loss: 0.2721 - accuracy: 0.9019 - precision: 0.9129 - recall: 0.8918 - val_loss: 0.4172 - val_accuracy: 0.8565 - val_precision: 0.8699 - val_recall: 0.8464 - lr: 1.0000e-03 - 171s/epoch - 218ms/step\n",
      "Epoch 65/300\n",
      "\n",
      "Epoch 65: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 65: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 65: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 65: val_recall improved from 0.85334 to 0.85396, saving model to ../models/EfficientNetV2S_96x96_32\\max_recall.h5\n",
      "783/783 - 166s - loss: 0.2705 - accuracy: 0.9031 - precision: 0.9127 - recall: 0.8942 - val_loss: 0.4109 - val_accuracy: 0.8621 - val_precision: 0.8723 - val_recall: 0.8540 - lr: 1.0000e-03 - 166s/epoch - 212ms/step\n",
      "Epoch 66/300\n",
      "\n",
      "Epoch 66: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 66: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 66: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 66: val_recall did not improve from 0.85396\n",
      "783/783 - 166s - loss: 0.2653 - accuracy: 0.9026 - precision: 0.9132 - recall: 0.8931 - val_loss: 0.4236 - val_accuracy: 0.8577 - val_precision: 0.8693 - val_recall: 0.8480 - lr: 1.0000e-03 - 166s/epoch - 211ms/step\n",
      "Epoch 67/300\n",
      "\n",
      "Epoch 67: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 67: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 67: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 67: val_recall did not improve from 0.85396\n",
      "783/783 - 165s - loss: 0.2666 - accuracy: 0.9021 - precision: 0.9120 - recall: 0.8924 - val_loss: 0.4297 - val_accuracy: 0.8546 - val_precision: 0.8669 - val_recall: 0.8471 - lr: 1.0000e-03 - 165s/epoch - 211ms/step\n",
      "Epoch 68/300\n",
      "\n",
      "Epoch 68: ReduceLROnPlateau reducing learning rate to 9.999999310821295e-05.\n",
      "\n",
      "Epoch 68: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 68: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 68: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 68: val_recall did not improve from 0.85396\n",
      "783/783 - 165s - loss: 0.2622 - accuracy: 0.9036 - precision: 0.9144 - recall: 0.8944 - val_loss: 0.4236 - val_accuracy: 0.8546 - val_precision: 0.8686 - val_recall: 0.8452 - lr: 1.0000e-03 - 165s/epoch - 211ms/step\n",
      "Epoch 69/300\n",
      "\n",
      "Epoch 69: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 69: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 69: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 69: val_recall did not improve from 0.85396\n",
      "783/783 - 165s - loss: 0.2484 - accuracy: 0.9115 - precision: 0.9220 - recall: 0.9012 - val_loss: 0.4271 - val_accuracy: 0.8568 - val_precision: 0.8693 - val_recall: 0.8480 - lr: 1.0000e-04 - 165s/epoch - 211ms/step\n",
      "Epoch 70/300\n",
      "\n",
      "Epoch 70: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 70: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 70: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 70: val_recall did not improve from 0.85396\n",
      "783/783 - 166s - loss: 0.2516 - accuracy: 0.9081 - precision: 0.9173 - recall: 0.8991 - val_loss: 0.4211 - val_accuracy: 0.8593 - val_precision: 0.8700 - val_recall: 0.8496 - lr: 1.0000e-04 - 166s/epoch - 211ms/step\n",
      "Epoch 71/300\n",
      "\n",
      "Epoch 71: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 71: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 71: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 71: val_recall did not improve from 0.85396\n",
      "783/783 - 165s - loss: 0.2539 - accuracy: 0.9082 - precision: 0.9187 - recall: 0.9001 - val_loss: 0.4229 - val_accuracy: 0.8590 - val_precision: 0.8694 - val_recall: 0.8515 - lr: 1.0000e-04 - 165s/epoch - 211ms/step\n",
      "Epoch 72/300\n",
      "\n",
      "Epoch 72: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 72: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 72: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 72: val_recall did not improve from 0.85396\n",
      "783/783 - 165s - loss: 0.2537 - accuracy: 0.9078 - precision: 0.9168 - recall: 0.8993 - val_loss: 0.4267 - val_accuracy: 0.8565 - val_precision: 0.8662 - val_recall: 0.8477 - lr: 1.0000e-04 - 165s/epoch - 211ms/step\n",
      "Epoch 73/300\n",
      "\n",
      "Epoch 73: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 73: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 73: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 73: val_recall did not improve from 0.85396\n",
      "783/783 - 165s - loss: 0.2523 - accuracy: 0.9087 - precision: 0.9178 - recall: 0.9002 - val_loss: 0.4198 - val_accuracy: 0.8593 - val_precision: 0.8682 - val_recall: 0.8502 - lr: 1.0000e-04 - 165s/epoch - 211ms/step\n",
      "Epoch 74/300\n",
      "\n",
      "Epoch 74: val_loss did not improve from 0.40681\n",
      "\n",
      "Epoch 74: val_accuracy did not improve from 0.86587\n",
      "\n",
      "Epoch 74: val_precision did not improve from 0.87981\n",
      "\n",
      "Epoch 74: val_recall did not improve from 0.85396\n",
      "783/783 - 166s - loss: 0.2503 - accuracy: 0.9082 - precision: 0.9181 - recall: 0.8995 - val_loss: 0.4202 - val_accuracy: 0.8596 - val_precision: 0.8688 - val_recall: 0.8511 - lr: 1.0000e-04 - 166s/epoch - 212ms/step\n",
      "Epoch 75/300\n"
     ]
    }
   ],
   "source": [
    "fit_history = model.fit(\n",
    "    train_generator,\n",
    "    epochs=300,\n",
    "    validation_data=val_generator,\n",
    "    callbacks=[\n",
    "        lr_callback,\n",
    "        early_stop_callback,\n",
    "        min_loss_callback,\n",
    "        max_accuracy_callback,\n",
    "        max_precision_callback,\n",
    "        max_recall_callback,\n",
    "    ],\n",
    "    verbose=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_graphics(fit_history, metric_name):\n",
    "    history = fit_history.history\n",
    "    \n",
    "    metric = history[metric_name]\n",
    "    val_metric = history[f'val_{metric_name}']\n",
    "    \n",
    "    epochs = range(1, len(metric) + 1)\n",
    "\n",
    "    plt.plot(epochs, metric, 'b', label=f'Training {metric_name}')\n",
    "    plt.plot(epochs, val_metric, 'r', label=f'Validation {metric_name}')\n",
    "    \n",
    "    plt.title(f'Training and validation {metric_name}')\n",
    "    plt.xlabel('epochs')\n",
    "    plt.ylabel(f'{metric_name}')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "draw_graphics(fit_history, 'loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_graphics(fit_history, 'accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_graphics(fit_history, 'precision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "draw_graphics(fit_history, 'recall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "\n",
    "for image_name, emotion in test_df.values:\n",
    "    image = tf.keras.preprocessing.image.load_img(\n",
    "        os.path.join(test_dir, image_name),\n",
    "        target_size=target_size,\n",
    "        interpolation=interpolation,\n",
    "        color_mode=color_mode,\n",
    "    )\n",
    "    \n",
    "    x_test.append(tf.keras.preprocessing.image.img_to_array(image))\n",
    "    y_test.append(labels.index(emotion))\n",
    "        \n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_confusion_matrix(y_true, y_pred, labels):\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred, normalize='true')\n",
    "    display = ConfusionMatrixDisplay(cm, display_labels=labels)\n",
    "\n",
    "    _, ax = plt.subplots(figsize=(10, 10))\n",
    "    display.plot(cmap=plt.cm.Blues, ax=ax)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_metrics(y_true, y_pred):\n",
    "    y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "    precision = precision_score(y_true, y_pred, average='macro', zero_division=0)\n",
    "    recall = recall_score(y_true, y_pred, average='macro', zero_division=0)\n",
    "\n",
    "    print(f'Accuracy: {accuracy * 100:.2f}%')\n",
    "    print(f'Precision: {precision * 100:.2f}%')\n",
    "    print(f'Recall: {recall * 100:.2f}%')\n",
    "    print(f'F1 score: {2 * (precision * recall) / (precision + recall) * 100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 85.56%\n",
      "Precision: 68.16%\n",
      "Recall: 63.06%\n",
      "F1 score: 65.51%\n"
     ]
    }
   ],
   "source": [
    "min_loss_model = tf.keras.models.load_model(f'../models/{notebook_name}/min_loss.h5')\n",
    "y_pred = min_loss_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 85.56%\n",
      "Precision: 68.16%\n",
      "Recall: 63.06%\n",
      "F1 score: 65.51%\n"
     ]
    }
   ],
   "source": [
    "max_accuracy_model = tf.keras.models.load_model(f'../models/{notebook_name}/max_accuracy.h5')\n",
    "y_pred = max_accuracy_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 85.56%\n",
      "Precision: 68.16%\n",
      "Recall: 63.06%\n",
      "F1 score: 65.51%\n"
     ]
    }
   ],
   "source": [
    "max_precision_model = tf.keras.models.load_model(f'../models/{notebook_name}/max_precision.h5')\n",
    "y_pred = max_precision_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 85.81%\n",
      "Precision: 75.12%\n",
      "Recall: 64.22%\n",
      "F1 score: 69.25%\n"
     ]
    }
   ],
   "source": [
    "# best\n",
    "max_recall_model = tf.keras.models.load_model(f'../models/{notebook_name}/max_recall.h5')\n",
    "y_pred = max_recall_model.predict(x_test)\n",
    "\n",
    "draw_confusion_matrix(y_test, y_pred, labels)\n",
    "compute_metrics(y_test, y_pred)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "celltoolbar": "Raw Cell Format",
  "colab": {
   "collapsed_sections": [],
   "name": "mobilenet_v3_augmented_data.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}