3. Valence-Arousal model  tuning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "db981aa3",
   "metadata": {},
   "source": [
    "# Libraries import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19e7c62e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "\n",
    "import random\n",
    "import cv2\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras_vggface.utils import preprocess_input as preprocess_input_vggface\n",
    "from keras_vggface.vggface import VGGFace\n",
    "from livelossplot.tf_keras import PlotLossesCallback\n",
    "from keras_vggface import utils\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f608bc4",
   "metadata": {},
   "source": [
    "# Data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4572b1a",
   "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>image_path</th>\n",
       "      <th>emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./_datasets/train_dataset/anger/0.jpg</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./_datasets/train_dataset/anger/1.jpg</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./_datasets/train_dataset/anger/10.jpg</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./_datasets/train_dataset/anger/100.jpg</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./_datasets/train_dataset/anger/1000.jpg</td>\n",
       "      <td>anger</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 image_path emotion\n",
       "0     ./_datasets/train_dataset/anger/0.jpg   anger\n",
       "1     ./_datasets/train_dataset/anger/1.jpg   anger\n",
       "2    ./_datasets/train_dataset/anger/10.jpg   anger\n",
       "3   ./_datasets/train_dataset/anger/100.jpg   anger\n",
       "4  ./_datasets/train_dataset/anger/1000.jpg   anger"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load csv with images\n",
    "df = pd.read_csv('./_datasets/train.csv', index_col = 0)\n",
    "# edit path to file location\n",
    "df['image_path'] = df['image_path'].apply(lambda x: str.replace(x, 'train', '_datasets/train_dataset'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c38fe40b",
   "metadata": {},
   "source": [
    "# Valence-arousal intro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d00a05",
   "metadata": {},
   "source": [
    "<center><img src=\"img/valence-arousal.jpg\" width=\"600\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0c4573",
   "metadata": {},
   "source": [
    "While our dataset doesn't have any information about Valence-Arousal parametres of given fotos, I shall use random values close to given emotions.\n",
    "Typical 2D valence-arousal emotion space [Russell 1980] shown above. I will use numeric values of given emotions according to https://www.mdpi.com/1660-4601/17/22/8534/htm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea048f25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fisrt number - valence, second - arousal\n",
    "VA_emotions = {\n",
    "    'anger':    [ 1.5, 6.72],   # гнев\n",
    "    'contempt': [ 1.5, 3.75],   # презрение\n",
    "    'disgust':  [ 3.5,  2.5],   # отвращение\n",
    "    'fear':     [ 2.0,  6.2],   # страх\n",
    "    'happy':    [ 6.6,  4.2],   # радость\n",
    "    'neutral':  [ 4.0,  4.0],   # безразличие\n",
    "    'sad':      [ 1.5,  1.5],   # грусть\n",
    "    'surprise': [ 6.0,  6.0],   # удивление\n",
    "    'uncertain':[ 3.0,  4.0]    # неопределенность\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac53e586",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# drawing points of given emotions\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "for emotion, (v, a) in VA_emotions.items():\n",
    "    plt.scatter(v, a, label=emotion, s=150)\n",
    "    plt.text(v-0.1, a+0.03, emotion, size=12)\n",
    "plt.xlim(1,7)\n",
    "plt.ylim(1,7)\n",
    "plt.vlines(4, 1, 7, linestyle='dashed', lw=1)\n",
    "plt.hlines(4, 1, 7, linestyle='dashed', lw=1)\n",
    "plt.xlabel('Valence', size=14)\n",
    "plt.ylabel('Arousal', size=14)\n",
    "plt.title('Emotions in valence-arousal coords', size=17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "583018d3",
   "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>image_path</th>\n",
       "      <th>emotion</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Arousal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./_datasets/train_dataset/anger/0.jpg</td>\n",
       "      <td>anger</td>\n",
       "      <td>1.380460</td>\n",
       "      <td>6.813815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./_datasets/train_dataset/anger/1.jpg</td>\n",
       "      <td>anger</td>\n",
       "      <td>1.683875</td>\n",
       "      <td>6.795412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./_datasets/train_dataset/anger/10.jpg</td>\n",
       "      <td>anger</td>\n",
       "      <td>1.609044</td>\n",
       "      <td>6.912541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./_datasets/train_dataset/anger/100.jpg</td>\n",
       "      <td>anger</td>\n",
       "      <td>1.333618</td>\n",
       "      <td>6.570348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./_datasets/train_dataset/anger/1000.jpg</td>\n",
       "      <td>anger</td>\n",
       "      <td>1.450941</td>\n",
       "      <td>6.660688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50041</th>\n",
       "      <td>./_datasets/train_dataset/uncertain/995.jpg</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>2.811215</td>\n",
       "      <td>3.806269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50042</th>\n",
       "      <td>./_datasets/train_dataset/uncertain/996.jpg</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>2.960640</td>\n",
       "      <td>3.999018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50043</th>\n",
       "      <td>./_datasets/train_dataset/uncertain/997.jpg</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>2.797010</td>\n",
       "      <td>4.015861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50044</th>\n",
       "      <td>./_datasets/train_dataset/uncertain/998.jpg</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>3.040114</td>\n",
       "      <td>4.046429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50045</th>\n",
       "      <td>./_datasets/train_dataset/uncertain/999.jpg</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>2.833141</td>\n",
       "      <td>4.057445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50046 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        image_path    emotion   Valence  \\\n",
       "0            ./_datasets/train_dataset/anger/0.jpg      anger  1.380460   \n",
       "1            ./_datasets/train_dataset/anger/1.jpg      anger  1.683875   \n",
       "2           ./_datasets/train_dataset/anger/10.jpg      anger  1.609044   \n",
       "3          ./_datasets/train_dataset/anger/100.jpg      anger  1.333618   \n",
       "4         ./_datasets/train_dataset/anger/1000.jpg      anger  1.450941   \n",
       "...                                            ...        ...       ...   \n",
       "50041  ./_datasets/train_dataset/uncertain/995.jpg  uncertain  2.811215   \n",
       "50042  ./_datasets/train_dataset/uncertain/996.jpg  uncertain  2.960640   \n",
       "50043  ./_datasets/train_dataset/uncertain/997.jpg  uncertain  2.797010   \n",
       "50044  ./_datasets/train_dataset/uncertain/998.jpg  uncertain  3.040114   \n",
       "50045  ./_datasets/train_dataset/uncertain/999.jpg  uncertain  2.833141   \n",
       "\n",
       "        Arousal  \n",
       "0      6.813815  \n",
       "1      6.795412  \n",
       "2      6.912541  \n",
       "3      6.570348  \n",
       "4      6.660688  \n",
       "...         ...  \n",
       "50041  3.806269  \n",
       "50042  3.999018  \n",
       "50043  4.015861  \n",
       "50044  4.046429  \n",
       "50045  4.057445  \n",
       "\n",
       "[50046 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# featuring of valence-arousal values of given foto by adding random float within (-0.22, 0.22) of base emotion value\n",
    "\n",
    "def get_valence(emotion):\n",
    "    \"\"\"\n",
    "    function return random valence value of given emotion\n",
    "    \"\"\"\n",
    "    emotion_valence = VA_emotions[emotion][0] + random.uniform(-0.22, 0.22)\n",
    "    return emotion_valence\n",
    "\n",
    "\n",
    "def get_arousal(emotion):\n",
    "    \"\"\"\n",
    "    function return random arousal value of given emotion\n",
    "    \"\"\"\n",
    "    emotion_arousal = VA_emotions[emotion][1] + random.uniform(-0.22, 0.22)\n",
    "    return emotion_arousal\n",
    "    \n",
    "df['Valence'] = df['emotion'].apply(lambda x: get_valence(x))\n",
    "df['Arousal'] = df['emotion'].apply(lambda x: get_arousal(x))\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "010b4c87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "plt.xlim(1,7)\n",
    "plt.ylim(1,7)\n",
    "plt.vlines(4, 1, 7, linestyle='dashed', lw=1)\n",
    "plt.hlines(4, 1, 7, linestyle='dashed', lw=1)\n",
    "plt.xlabel('Valence', size=14)\n",
    "plt.ylabel('Arousal', size=14)\n",
    "plt.title('Emotions in valence-arousal coords', size=17)\n",
    "ax = sns.scatterplot(x = 'Valence', y = 'Arousal', data = df, hue = 'emotion')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f65e49d",
   "metadata": {},
   "source": [
    "# DataGenerators with augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d4f9045c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = df.sample(frac=0.8,random_state=200)\n",
    "test_df = df.drop(train_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e58a767e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_input_facenet(image_):\n",
    "    \"\"\"\n",
    "    image_ -- тензор размера (1, H, W, 3)\n",
    "    \n",
    "    return: картинка, с примененным preprocess_input(..., version=2) из keras_vggface\n",
    "    \"\"\"\n",
    "    preprocessed = utils.preprocess_input(image_, version=2)\n",
    "    \n",
    "    return preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "911a5010",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 40037 validated image filenames.\n",
      "Found 10009 validated image filenames.\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 16\n",
    "IMAGE_SIZE = 224\n",
    "\n",
    "# train generator with augmentation\n",
    "train_generator = ImageDataGenerator(\n",
    "                                     rotation_range=15,\n",
    "                                     horizontal_flip=True,\n",
    "                                     fill_mode='nearest',\n",
    "                                     brightness_range=(0.5,1.5),\n",
    "                                     shear_range=0.1,\n",
    "                                     zoom_range=.1,\n",
    "                                     preprocessing_function=preprocess_input_facenet)\n",
    "        \n",
    "train_data_gen = train_generator.flow_from_dataframe(dataframe=train_df,\n",
    "                                                     x_col='image_path',\n",
    "                                                     y_col=['Valence','Arousal'],\n",
    "                                                     target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
    "                                                     batch_size=BATCH_SIZE,\n",
    "                                                     class_mode='raw',\n",
    "                                                     shuffle=True,\n",
    "                                                     interpolation='nearest',\n",
    "                                                )\n",
    "\n",
    "# validation generator without augmentation\n",
    "val_generator = ImageDataGenerator(preprocessing_function=preprocess_input_facenet)\n",
    "\n",
    "val_data_gen = val_generator.flow_from_dataframe(dataframe=test_df,\n",
    "                                                 x_col='image_path',\n",
    "                                                 y_col=['Valence','Arousal'],\n",
    "                                                 target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
    "                                                 batch_size=BATCH_SIZE,\n",
    "                                                 class_mode='raw',\n",
    "                                                 shuffle=False,\n",
    "                                                 interpolation='nearest',\n",
    "                                                )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "505e9390",
   "metadata": {},
   "source": [
    "# VGGFace load and final model set-up"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d31a38",
   "metadata": {},
   "source": [
    "I use pre-trained VGGFace model with only few last layres trainable. My final model is build on VGGFace without top-layer, one flatten layer, one hidden 512 layer, dropout and one last layer with two neurons (for each prediction value) with linear activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01c4800f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vggface_resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1/7x7_s2 (Conv2D)           (None, 112, 112, 64) 9408        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1/7x7_s2/bn (BatchNormaliza (None, 112, 112, 64) 256         conv1/7x7_s2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 112, 112, 64) 0           conv1/7x7_s2/bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 55, 55, 64)   0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_reduce (Conv2D)     (None, 55, 55, 64)   4096        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 55, 55, 64)   0           conv2_1_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 55, 55, 64)   0           conv2_1_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_proj (Conv2D)       (None, 55, 55, 256)  16384       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv2_1_1x1_proj/bn (BatchNorma (None, 55, 55, 256)  1024        conv2_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 55, 55, 256)  0           conv2_1_1x1_increase/bn[0][0]    \n",
      "                                                                 conv2_1_1x1_proj/bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 55, 55, 256)  0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 55, 55, 64)   0           conv2_2_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 55, 55, 64)   0           conv2_2_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_2_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 55, 55, 256)  0           conv2_2_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 55, 55, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_reduce (Conv2D)     (None, 55, 55, 64)   16384       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_reduce/bn (BatchNor (None, 55, 55, 64)   256         conv2_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 55, 55, 64)   0           conv2_3_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_3x3 (Conv2D)            (None, 55, 55, 64)   36864       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_3x3/bn (BatchNormalizat (None, 55, 55, 64)   256         conv2_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 55, 55, 64)   0           conv2_3_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_increase (Conv2D)   (None, 55, 55, 256)  16384       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2_3_1x1_increase/bn (BatchN (None, 55, 55, 256)  1024        conv2_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 55, 55, 256)  0           conv2_3_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 55, 55, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_reduce (Conv2D)     (None, 28, 28, 128)  32768       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 28, 28, 128)  0           conv3_1_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 28, 28, 128)  0           conv3_1_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_proj (Conv2D)       (None, 28, 28, 512)  131072      activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv3_1_1x1_proj/bn (BatchNorma (None, 28, 28, 512)  2048        conv3_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 28, 28, 512)  0           conv3_1_1x1_increase/bn[0][0]    \n",
      "                                                                 conv3_1_1x1_proj/bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 28, 28, 512)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 28, 28, 128)  0           conv3_2_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 28, 28, 128)  0           conv3_2_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_2_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           conv3_2_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 28, 28, 128)  0           conv3_3_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 28, 28, 128)  0           conv3_3_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_3_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           conv3_3_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_reduce (Conv2D)     (None, 28, 28, 128)  65536       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_reduce/bn (BatchNor (None, 28, 28, 128)  512         conv3_4_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 28, 28, 128)  0           conv3_4_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_3x3 (Conv2D)            (None, 28, 28, 128)  147456      activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_3x3/bn (BatchNormalizat (None, 28, 28, 128)  512         conv3_4_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 28, 28, 128)  0           conv3_4_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_increase (Conv2D)   (None, 28, 28, 512)  65536       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv3_4_1x1_increase/bn (BatchN (None, 28, 28, 512)  2048        conv3_4_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           conv3_4_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_reduce (Conv2D)     (None, 14, 14, 256)  131072      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 14, 14, 256)  0           conv4_1_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 14, 14, 256)  0           conv4_1_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_proj (Conv2D)       (None, 14, 14, 1024) 524288      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_1_1x1_proj/bn (BatchNorma (None, 14, 14, 1024) 4096        conv4_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 14, 14, 1024) 0           conv4_1_1x1_increase/bn[0][0]    \n",
      "                                                                 conv4_1_1x1_proj/bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 14, 14, 1024) 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 14, 14, 256)  0           conv4_2_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 256)  0           conv4_2_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_2_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           conv4_2_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 14, 14, 256)  0           conv4_3_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 256)  0           conv4_3_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_3_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           conv4_3_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_4_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 256)  0           conv4_4_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_4_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 256)  0           conv4_4_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_4_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_4_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           conv4_4_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_5_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 256)  0           conv4_5_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_5_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 256)  0           conv4_5_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_5_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_5_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           conv4_5_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_reduce (Conv2D)     (None, 14, 14, 256)  262144      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_reduce/bn (BatchNor (None, 14, 14, 256)  1024        conv4_6_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 256)  0           conv4_6_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_3x3 (Conv2D)            (None, 14, 14, 256)  589824      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_3x3/bn (BatchNormalizat (None, 14, 14, 256)  1024        conv4_6_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 256)  0           conv4_6_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_increase (Conv2D)   (None, 14, 14, 1024) 262144      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv4_6_1x1_increase/bn (BatchN (None, 14, 14, 1024) 4096        conv4_6_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           conv4_6_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_reduce (Conv2D)     (None, 7, 7, 512)    524288      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_1_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 7, 7, 512)    0           conv5_1_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_1_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 7, 7, 512)    0           conv5_1_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_proj (Conv2D)       (None, 7, 7, 2048)   2097152     activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_1_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv5_1_1x1_proj/bn (BatchNorma (None, 7, 7, 2048)   8192        conv5_1_1x1_proj[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 7, 7, 2048)   0           conv5_1_1x1_increase/bn[0][0]    \n",
      "                                                                 conv5_1_1x1_proj/bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 7, 7, 2048)   0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_2_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 7, 7, 512)    0           conv5_2_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_2_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 7, 7, 512)    0           conv5_2_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_2_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_2_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           conv5_2_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_reduce (Conv2D)     (None, 7, 7, 512)    1048576     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_reduce/bn (BatchNor (None, 7, 7, 512)    2048        conv5_3_1x1_reduce[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 7, 7, 512)    0           conv5_3_1x1_reduce/bn[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_3x3 (Conv2D)            (None, 7, 7, 512)    2359296     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_3x3/bn (BatchNormalizat (None, 7, 7, 512)    2048        conv5_3_3x3[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 7, 7, 512)    0           conv5_3_3x3/bn[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_increase (Conv2D)   (None, 7, 7, 2048)   1048576     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv5_3_1x1_increase/bn (BatchN (None, 7, 7, 2048)   8192        conv5_3_1x1_increase[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           conv5_3_1x1_increase/bn[0][0]    \n",
      "                                                                 activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (AveragePooling2D)     (None, 1, 1, 2048)   0           activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 2048)         0           avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 23,561,152\n",
      "Trainable params: 23,508,032\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Load VGGFace model with RESNet\n",
    "vggface_model = VGGFace(model='resnet50', include_top=False,\n",
    "                        input_shape=(224, 224, 3), pooling='avg')\n",
    "vggface_model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6db3e4ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vggface_resnet50 (Functional (None, 2048)              23561152  \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 2)                 4098      \n",
      "=================================================================\n",
      "Total params: 23,565,250\n",
      "Trainable params: 23,512,130\n",
      "Non-trainable params: 53,120\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# build final model\n",
    "model = tf.keras.Sequential([\n",
    "    vggface_model,\n",
    "    tf.keras.layers.Flatten(),\n",
    "#     tf.keras.layers.Dense(1024, activation = 'relu'),\n",
    "#     tf.keras.layers.Dropout(0.1),\n",
    "#     tf.keras.layers.Dense(64, activation = 'relu'),\n",
    "#     tf.keras.layers.Dropout(0.1),\n",
    "    tf.keras.layers.Dense(2)])\n",
    "\n",
    "model.build(input_shape=(None, 224, 224, 3))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48160fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate=5e-4,\n",
    "    decay_steps=100,\n",
    "    decay_rate=0.9)\n",
    "\n",
    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n",
    "              loss='mse',\n",
    "              metrics='mean_absolute_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6b5a618f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss\n",
      "\ttraining         \t (min:    2.010, max:    2.676, cur:    2.014)\n",
      "\tvalidation       \t (min:    2.053, max:    2.224, cur:    2.055)\n",
      "mean_absolute_error\n",
      "\ttraining         \t (min:    1.113, max:    1.314, cur:    1.114)\n",
      "\tvalidation       \t (min:    1.114, max:    1.193, cur:    1.114)\n",
      "\n",
      "Epoch 00010: val_mean_absolute_error improved from 1.11445 to 1.11401, saving model to ./_models/_checkpoints/best_checkpoint/val_ar\\\n",
      "2503/2503 [==============================] - 552s 221ms/step - loss: 2.0137 - mean_absolute_error: 1.1142 - val_loss: 2.0546 - val_mean_absolute_error: 1.1140\n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 10\n",
    "\n",
    "my_callbacks = [\n",
    "    PlotLossesCallback(),\n",
    "    tf.keras.callbacks.ModelCheckpoint('./_models/_checkpoints/best_checkpoint/val_ar/',\n",
    "                                       monitor='val_mean_absolute_error',\n",
    "                                       verbose=1,\n",
    "                                       save_best_only=True,\n",
    "                                       save_weights_only=True,\n",
    "                                       save_freq='epoch',\n",
    "                                       mode='min')\n",
    "]\n",
    "\n",
    "history = model.fit(\n",
    "    train_data_gen,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=val_data_gen,\n",
    "    callbacks=my_callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0de5e47a",
   "metadata": {},
   "source": [
    "# Evaluate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e0b9fb4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x2b883fc6948>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load model from best_checkpoint\n",
    "model.load_weights('./_models/_checkpoints/best_checkpoint/val_ar/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b05449c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "626/626 [==============================] - 622s 993ms/step - loss: 2.0546 - mean_absolute_error: 1.1140\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2.0546343326568604, 1.1140120029449463]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(val_data_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4546b375",
   "metadata": {},
   "source": [
    "# Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14d78ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_json = model.to_json()\n",
    "with open(\"./_models/valence_arousal_decomposition/model.json\",\"w\") as json_file:\n",
    "    json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a61e8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights(\"./_models/valence_arousal_decomposition/saved_weights\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02b534d",
   "metadata": {},
   "source": [
    "# Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9fac9a5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x2166b137f08>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import model_from_json\n",
    "\n",
    "with open(\"./_models/valence_arousal_decomposition/model.json\",\"r\") as json_file:\n",
    "    model_json = json_file.read()\n",
    "model_new = model_from_json(model_json)\n",
    "\n",
    "model_new.load_weights(\"./_models/valence_arousal_decomposition/saved_weights\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4acfe20",
   "metadata": {},
   "source": [
    "# Decode model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "43e2f4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# тестовое изображение\n",
    "img = cv2.imread('./test_foto/happy.jpg')\n",
    "img = cv2.resize(img, (224,224))\n",
    "img = np.expand_dims(img, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "80538343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.114682 , 4.1169586]], dtype=float32)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# предсказание параметров эмоции\n",
    "prediction = model(img).numpy()\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7b74dcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1.5, 6.72],\n",
       " [1.5, 3.75],\n",
       " [3.5, 2.5],\n",
       " [2.0, 6.2],\n",
       " [6.6, 4.2],\n",
       " [4.0, 4.0],\n",
       " [1.5, 1.5],\n",
       " [6.0, 6.0],\n",
       " [3.0, 4.0]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# список с векторами имеющихся 9 эмоций\n",
    "list(VA_emotions.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c08d5d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# использую KDTree для поиска ближайшего вектора из этого списка к предсказанию\n",
    "from scipy import spatial\n",
    "emotions = spatial.KDTree(list(VA_emotions.values()))\n",
    "\n",
    "emotion_number = int(emotions.query(prediction)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e60318b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'happy'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotion_name = list(VA_emotions.keys())[emotion_number]\n",
    "emotion_name"
   ]
  }
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