docs/source/user_guide/notebooks/Video-load_video_model_deep_fe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Формирование нейросетевой архитектуры модели и загрузка ее весов для получения нейросетевых признаков (видео модальность)\n",
    "\n",
    "<hr>\n",
    "\n",
    "> - `_b5.video_model_deep_fe_` - Нейросетевая модель **tf.keras.Model** для получения нейросетевых признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os  # Взаимодействие с файловой системой\n",
    "import sys # Доступ к некоторым переменным и функциям Python\n",
    "\n",
    "PATH_TO_SOURCE = os.path.abspath(os.path.dirname(globals()['_dh'][0]))\n",
    "PATH_TO_ROOT = os.path.join(PATH_TO_SOURCE, '..', '..', '..')\n",
    "\n",
    "sys.path.insert(0, os.path.abspath(PATH_TO_ROOT))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Импорт необходимых инструментов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from oceanai.modules.lab.build import Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сборка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-10 17:08:31</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">OCEANAI - персональные качества личности человека:</span>**<br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;Авторы:</span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Рюмина Елена [<u>ryumina_ev@mail.ru</u>]</span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Рюмин Дмитрий [<u>dl_03.03.1991@mail.ru</u>]</span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Карпов Алексей [<u>karpov@iias.spb.su</u>]</span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;Сопровождающие:</span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Рюмина Елена [<u>ryumina_ev@mail.ru</u>]</span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Рюмин Дмитрий [<u>dl_03.03.1991@mail.ru</u>]</span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;Версия: <u>1.0.0a5</u></span><br /><span style=\"color:#333\">&nbsp;&nbsp;&nbsp;&nbsp;Лицензия: <u>BSD License</u></span></p>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_b5 = Run(\n",
    "    lang = 'ru', # Язык\n",
    "    color_simple = '#333', # Цвет обычного текста (шестнадцатеричный код)\n",
    "    color_info = '#1776D2', # Цвет текста содержащего информацию (шестнадцатеричный код)\n",
    "    color_err = '#FF0000', # Цвет текста содержащего ошибку (шестнадцатеричный код)\n",
    "    color_true = '#008001', # Цвет текста содержащего положительную информацию (шестнадцатеричный код)\n",
    "    bold_text = True, # Жирное начертание текста\n",
    "    num_to_df_display = 30, # Количество строк для отображения в таблицах\n",
    "    text_runtime = 'Время выполнения', # Текст времени выполнения\n",
    "    metadata = True # Отображение информации о библиотеке\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Формирование нейросетевой архитектуры модели (`FI V2`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-10 17:08:31</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">Формирование нейросетевой архитектуры для получения нейросетевых признаков (видео модальность) ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#333\">**--- Время выполнения: 1.118 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_load_video_model_deep_fe = _b5.load_video_model_deep_fe(\n",
    "    show_summary = False, # Отображение сформированной нейросетевой архитектуры модели\n",
    "    out = True, # Отображение\n",
    "    runtime = True, # Подсчет времени выполнения\n",
    "    run = True # Блокировка выполнения\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Загрузка весов нейросетевой модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-10 17:08:32</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">Загрузка весов нейросетевой модели для получения нейросетевых признаков (видео модальность) ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#333\">**[</span><span style=\"color:#1776D2\">2023-12-10 17:08:36</span><span style=\"color:#333\">]</span> <span style=\"color:#333\">Загрузка файла \"<span style=\"color:#1776D2\">weights_2022-11-01_12-27-07.h5</span>\" 100.0% ...</span>** "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<span style=\"color:#333\">**--- Время выполнения: 4.042 сек. ---**</span>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Настройки ядра\n",
    "_b5.path_to_save_ = './models' # Директория для сохранения файла\n",
    "_b5.chunk_size_ = 2000000 # Размер загрузки файла из сети за 1 шаг\n",
    "\n",
    "url = _b5.weights_for_big5_['video']['fi']['fe']['sberdisk']\n",
    "\n",
    "res_load_video_model_weights_deep_fe = _b5.load_video_model_weights_deep_fe(\n",
    "    url = url, # Полный путь к файлу с весами нейросетевой модели\n",
    "    force_reload = True, # Принудительная загрузка файла с весами нейросетевой модели из сети\n",
    "    out = True, # Отображение\n",
    "    runtime = True, # Подсчет времени выполнения\n",
    "    run = True # Блокировка выполнения\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Отображение сформированной нейросетевой архитектуры модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1/7x7_s2 (Conv2D)          (None, 112, 112, 64  9408        ['input_1[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1/7x7_s2/bn (BatchNormaliz  (None, 112, 112, 64  256        ['conv1/7x7_s2[0][0]']           \n",
      " ation)                         )                                                                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 112, 112, 64  0           ['conv1/7x7_s2/bn[0][0]']        \n",
      "                                )                                                                 \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 (BatchNo  (None, 55, 55, 64)  256         ['conv2_1_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 55, 55, 64)  256         ['conv2_1_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 55, 55, 256)  1024       ['conv2_1_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv2_1_1x1_proj/bn (BatchNorm  (None, 55, 55, 256)  1024       ['conv2_1_1x1_proj[0][0]']       \n",
      " alization)                                                                                       \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 (BatchNo  (None, 55, 55, 64)  256         ['conv2_2_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 55, 55, 64)  256         ['conv2_2_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 55, 55, 256)  1024       ['conv2_2_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \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 (BatchNo  (None, 55, 55, 64)  256         ['conv2_3_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 55, 55, 64)  256         ['conv2_3_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 55, 55, 256)  1024       ['conv2_3_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \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 (BatchNo  (None, 28, 28, 128)  512        ['conv3_1_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_1_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 28, 28, 512)  2048       ['conv3_1_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv3_1_1x1_proj/bn (BatchNorm  (None, 28, 28, 512)  2048       ['conv3_1_1x1_proj[0][0]']       \n",
      " alization)                                                                                       \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 (BatchNo  (None, 28, 28, 128)  512        ['conv3_2_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_2_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 28, 28, 512)  2048       ['conv3_2_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \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 (BatchNo  (None, 28, 28, 128)  512        ['conv3_3_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_3_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 28, 28, 512)  2048       ['conv3_3_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \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 (BatchNo  (None, 28, 28, 128)  512        ['conv3_4_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 28, 28, 128)  512        ['conv3_4_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 28, 28, 512)  2048       ['conv3_4_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \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 (BatchNo  (None, 14, 14, 256)  1024       ['conv4_1_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_1_3x3[0][0]']            \n",
      " tion)                                                                                            \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",
      "                                                                                                  \n",
      " conv4_1_1x1_proj (Conv2D)      (None, 14, 14, 1024  524288      ['activation_21[0][0]']          \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_1_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_1_1x1_increase[0][0]']   \n",
      " Normalization)                 )                                                                 \n",
      "                                                                                                  \n",
      " conv4_1_1x1_proj/bn (BatchNorm  (None, 14, 14, 1024  4096       ['conv4_1_1x1_proj[0][0]']       \n",
      " alization)                     )                                                                 \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",
      "                                                                                                  \n",
      " conv4_2_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_24[0][0]']          \n",
      "                                                                                                  \n",
      " conv4_2_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_2_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_2_3x3[0][0]']            \n",
      " tion)                                                                                            \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",
      "                                                                                                  \n",
      " conv4_2_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_2_1x1_increase[0][0]']   \n",
      " Normalization)                 )                                                                 \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",
      "                                                                                                  \n",
      " conv4_3_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " conv4_3_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_3_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_3_3x3[0][0]']            \n",
      " tion)                                                                                            \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",
      "                                                                                                  \n",
      " conv4_3_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_3_1x1_increase[0][0]']   \n",
      " Normalization)                 )                                                                 \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",
      "                                                                                                  \n",
      " conv4_4_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_30[0][0]']          \n",
      "                                                                                                  \n",
      " conv4_4_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_4_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_4_3x3[0][0]']            \n",
      " tion)                                                                                            \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",
      "                                                                                                  \n",
      " conv4_4_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_4_1x1_increase[0][0]']   \n",
      " Normalization)                 )                                                                 \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",
      "                                                                                                  \n",
      " conv4_5_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_33[0][0]']          \n",
      "                                                                                                  \n",
      " conv4_5_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_5_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_5_3x3[0][0]']            \n",
      " tion)                                                                                            \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",
      "                                                                                                  \n",
      " conv4_5_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_5_1x1_increase[0][0]']   \n",
      " Normalization)                 )                                                                 \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",
      "                                                                                                  \n",
      " conv4_6_1x1_reduce (Conv2D)    (None, 14, 14, 256)  262144      ['activation_36[0][0]']          \n",
      "                                                                                                  \n",
      " conv4_6_1x1_reduce/bn (BatchNo  (None, 14, 14, 256)  1024       ['conv4_6_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 14, 14, 256)  1024       ['conv4_6_3x3[0][0]']            \n",
      " tion)                                                                                            \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",
      "                                                                                                  \n",
      " conv4_6_1x1_increase/bn (Batch  (None, 14, 14, 1024  4096       ['conv4_6_1x1_increase[0][0]']   \n",
      " Normalization)                 )                                                                 \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",
      "                                                                                                  \n",
      " conv5_1_1x1_reduce (Conv2D)    (None, 7, 7, 512)    524288      ['activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv5_1_1x1_reduce/bn (BatchNo  (None, 7, 7, 512)   2048        ['conv5_1_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 7, 7, 512)   2048        ['conv5_1_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 7, 7, 2048)  8192        ['conv5_1_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv5_1_1x1_proj/bn (BatchNorm  (None, 7, 7, 2048)  8192        ['conv5_1_1x1_proj[0][0]']       \n",
      " alization)                                                                                       \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 (BatchNo  (None, 7, 7, 512)   2048        ['conv5_2_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 7, 7, 512)   2048        ['conv5_2_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 7, 7, 2048)  8192        ['conv5_2_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \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 (BatchNo  (None, 7, 7, 512)   2048        ['conv5_3_1x1_reduce[0][0]']     \n",
      " rmalization)                                                                                     \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 (BatchNormaliza  (None, 7, 7, 512)   2048        ['conv5_3_3x3[0][0]']            \n",
      " tion)                                                                                            \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 (Batch  (None, 7, 7, 2048)  8192        ['conv5_3_1x1_increase[0][0]']   \n",
      " Normalization)                                                                                   \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 (Glob  (None, 2048)        0           ['avg_pool[0][0]']               \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " gaussian_noise (GaussianNoise)  (None, 2048)        0           ['global_average_pooling2d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " dense_x (Dense)                (None, 512)          1049088     ['gaussian_noise[0][0]']         \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 24,610,240\n",
      "Trainable params: 24,557,120\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "_b5.video_model_deep_fe_.summary()"
   ]
  }
 ],
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