Day020_Classic CNN-ResNet_InceptionV4_Inception-ResNet_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 『本次練習內容』\n",
    "#### 學習如何搭建 Residual Block\n",
    "####  學習如何搭建Inception-ResNet中的 Inception Block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 『本次練習目的』\n",
    "  #### 了解 Residual Block原理\n",
    "  #### 了解如何結合Inception 與 Residual概念"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.models import Model\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Input\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers import GlobalAveragePooling2D\n",
    "from keras import backend as K\n",
    "from keras import layers\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Concatenate\n",
    "from keras.layers import Lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Incpeiton](ResNet_Structure.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ResNetV1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Residual_block(input_tensor, kernel_size, filters, stage, block):\n",
    "    filters1, filters2, filters3 = filters\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "\n",
    "    x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)\n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2a')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters2, kernel_size,\n",
    "               padding='same', name=conv_name_base + '2b')(x)\n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2b')(x)\n",
    "   \n",
    "    x = layers.add([x, input_tensor])\n",
    "    x = Activation('relu')(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 參考ResNetV1 搭建 ResNetV2版本的Residual Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Residual_block_v2(input_tensor, kernel_size, filters, stage, block):\n",
    "    filters1, filters2, filters3 = filters\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2a')(input_tensor)\n",
    "    x = Activation('relu')(x)    \n",
    "    x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2a')(x)\n",
    "    \n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2b')(x)\n",
    "    x = Activation('relu')(x)  \n",
    "    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2b')(x)  \n",
    "\n",
    "    x = layers.add([x, input_tensor])\n",
    "\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 試試看自己設計一個先壓縮再回放的V2 Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduce=96,ouput_size=128\n",
    "def Residual_block_v2(input_tensor, kernel_size, stage, block,reduce=96,ouput_size=128):\n",
    "\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "    \n",
    "    \n",
    "    x = Conv2D(reduce, kernel_size, padding='same', name=conv_name_base + '2a')(input_tensor)\n",
    "    \n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2a')(x)\n",
    "    x = Activation('relu')(x)    \n",
    "    x = Conv2D(reduce, kernel_size, padding='same', name=conv_name_base + '2a')(x)\n",
    "    \n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2b')(x)\n",
    "    x = Activation('relu')(x)\n",
    "    \n",
    "    x = Conv2D(ouput_size, (1, 1), name=conv_name_base + '2b')(x)\n",
    "    \n",
    "    x = layers.add([x, input_tensor])\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tfa8Vx5iJbZU"
   },
   "source": [
    "## Incpetion Block-A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xbuyi79mJbZX"
   },
   "source": [
    "![Incpeiton](Inception-ResNet-A.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3GI24k24JbZa"
   },
   "source": [
    "## Incpetion Block-B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ge28e58BJbZd"
   },
   "source": [
    "![Incpeiton](Inception-ResNet-B.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aMz1WNBsJbZg"
   },
   "source": [
    "## Incpetion Block-C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "A4lY1HV6JbZj"
   },
   "source": [
    "![Incpeiton](Inception-ResNet-C.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t9IKAa_4JbZ1"
   },
   "outputs": [],
   "source": [
    "def Conv2d_bn(x,filters,kernel_size,padding='same',strides=(1, 1),normalizer=True,activation='relu',name=None):\n",
    "    if name is not None:\n",
    "        conv_name = name + '_conv'\n",
    "        bn_name = name + '_bn'\n",
    "        act_name = name + '_act'\n",
    "    else:\n",
    "        conv_name = None\n",
    "        bn_name = None\n",
    "        act_name = None\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        bn_axis = 1\n",
    "    else:\n",
    "        bn_axis = 3\n",
    "    x = Conv2D(\n",
    "            filters, kernel_size,\n",
    "            strides=strides, padding=padding,\n",
    "            use_bias=False, name=conv_name)(x)\n",
    "    if normalizer:\n",
    "        x = BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)\n",
    "    if activation:\n",
    "        x = Activation(activation, name=act_name)(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d7iVPJQkJbZ-"
   },
   "outputs": [],
   "source": [
    "def Residual_block(input_tensor, kernel_size, filters, stage, block):\n",
    "    filters1, filters2, filters3 = filters\n",
    "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
    "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
    "\n",
    "    x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)\n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2a')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters2, kernel_size,\n",
    "               padding='same', name=conv_name_base + '2b')(x)\n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2b')(x)\n",
    "    x = Activation('relu')(x)\n",
    "\n",
    "    x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)\n",
    "    x = BatchNormalization(axis=3, name=bn_name_base + '2c')(x)\n",
    "\n",
    "    x = layers.add([x, input_tensor])\n",
    "    x = Activation('relu')(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M0BEkO7VJbaI"
   },
   "source": [
    "## 參考上方Residual_block搭建 Inception-ResNet中的Inception Block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XJjxq9lWJbaN"
   },
   "outputs": [],
   "source": [
    "def inception_resnet_block(x, scale, block_type, activation='relu'):\n",
    "    '''scale: scaling factor to scale the residuals (i.e., the output of\n",
    "            passing `x` through an inception module) before adding them\n",
    "            to the shortcut branch. Let `r` be the output from the residual branch,\n",
    "            the output of this block will be `x + scale * r`.(簡單來說就是控制Residual branch的比例)'''\n",
    "    if block_type == 'Incpetion_Block-A':\n",
    "        branch_0 = Conv2d_bn(x, 32, 1)\n",
    "        branch_1 = Conv2d_bn(x, 32, 1)\n",
    "        branch_1 = Conv2d_bn(branch_1, 32, 3)\n",
    "        branch_2 = Conv2d_bn(x, 32, 1)\n",
    "        branch_2 = Conv2d_bn(branch_2, 48, 3)\n",
    "        branch_2 = Conv2d_bn(branch_2, 64, 3)\n",
    "        branches = [branch_0, branch_1, branch_2]\n",
    "    elif block_type == 'Incpetion_Block-B':\n",
    "        branch_0 = Conv2d_bn(x, 192, 1)\n",
    "        branch_1 = Conv2d_bn(x, 128, 1)\n",
    "        branch_1 = Conv2d_bn(branch_1, 160, [1, 7])\n",
    "        branch_1 = Conv2d_bn(branch_1, 192, [7, 1])\n",
    "        branches = [branch_0, branch_1]\n",
    "    elif block_type == 'Incpetion_Block-C':\n",
    "        branch_0 = Conv2d_bn(x, 192, 1)\n",
    "        branch_1 = Conv2d_bn(x, 192, 1)\n",
    "        branch_1 = Conv2d_bn(branch_1, 192, [1, 3])\n",
    "        branch_1 = Conv2d_bn(branch_1, 192, [3, 1])\n",
    "        branches = [branch_0, branch_1]\n",
    "    else:\n",
    "        raise ValueError('Unknown Inception-ResNet block type. '\n",
    "                         'Expects \"block35\", \"block17\" or \"block8\", '\n",
    "                         'but got: ' + str(block_type))\n",
    "    mixed = Concatenate(axis=3)(branches)\n",
    "    \n",
    "    '''確保輸入跟輸出深度相同'''\n",
    "    up = Conv2d_bn(mixed,K.int_shape(x)[3],1,activation=None)\n",
    "    \n",
    "    '''導入殘差結構，並給予權重'''\n",
    "    x = Lambda(lambda inputs, scale: inputs[0] + inputs[1] * scale,\n",
    "               output_shape=K.int_shape(x)[1:],\n",
    "               arguments={'scale': scale},)([x, up])\n",
    "    \n",
    "    if activation is not None:\n",
    "        x = Activation(activation)(x)\n",
    "    return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wv-ni8GpJbaU",
    "outputId": "dcf7198f-59a6-4eac-d912-565146b1ce16"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "Tensor(\"activation_7/Relu:0\", shape=(?, 224, 224, 32), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "img_input = Input(shape=(224,224,32))\n",
    "x=inception_resnet_block(img_input, 0.1, 'Incpetion_Block-A', activation='relu')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HOBuOAjOJbab"
   },
   "source": [
    "## 測試"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "T-igNjppJbad",
    "outputId": "0455b644-5456-4145-d719-3878348aa4cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"activation_14/Relu:0\", shape=(?, 224, 224, 32), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "img_input = Input(shape=(224,224,32))\n",
    "x=inception_resnet_block(img_input, 0.1, 'Incpetion_Block-A', activation='relu')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V7otKKZWJbah",
    "outputId": "bca7b98f-b8ab-4318-d884-77c8ee7c7fa8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"activation_19/Relu:0\", shape=(?, 224, 224, 32), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "img_input = Input(shape=(224,224,32))\n",
    "x=inception_resnet_block(img_input, 0.1, 'Incpetion_Block-B', activation='relu')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RjxCkTlHJbal",
    "outputId": "462b602c-2a47-4790-eff4-13c875aba275"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"activation_24/Relu:0\", shape=(?, 224, 224, 32), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "img_input = Input(shape=(224,224,32))\n",
    "x=inception_resnet_block(img_input, 0.1, 'Incpetion_Block-C', activation='relu')\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UYD4kS0BJbao"
   },
   "source": [
    "## 嘗試導入Inception Block到 Vgg_Inception中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FEe8WW2qJbap"
   },
   "outputs": [],
   "source": [
    "def VGG16_ResNet_Inception(include_top=True,input_tensor=None, input_shape=(224,224,1),\n",
    "          pooling='max',classes=1000):\n",
    " \n",
    "    img_input = Input(shape=input_shape)\n",
    "    \n",
    "    x = Conv2d_bn(img_input,64, (3, 3), activation='relu', padding='same', name='block1_conv1')\n",
    "    x = Conv2d_bn(x,64, (3, 3), activation='relu', padding='same', name='block1_conv2')\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
    "\n",
    "    # Block 2\n",
    "    x = Conv2d_bn(x,128, (3, 3), activation='relu', padding='same', name='block2_conv1')\n",
    "    x = Conv2d_bn(x,128, (3, 3), activation='relu', padding='same', name='block2_conv2')\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
    "\n",
    "    # Block 3\n",
    "    x = InceptionV1_block(x, ((64,), (96,128), (16,32), (32,)), 3, 'Block_1')\n",
    "    x = InceptionV1_block(x, ((64,), (96,128), (16,32), (32,)), 3, 'Block_2')\n",
    "    x = InceptionV1_block(x, ((64,), (96,128), (16,32), (32,)), 3, 'Block_3')\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
    "\n",
    "    # Block 4\n",
    "    x = Conv2d_bn(x,512, (3, 3), activation='relu', padding='same', name='block4_conv1')\n",
    "    x = Conv2d_bn(x,512, (3, 3), activation='relu', padding='same', name='block4_conv2')\n",
    "    x = Conv2d_bn(x,512, (3, 3), activation='relu', padding='same', name='block4_conv3')\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
    "\n",
    "    # Block 5 \n",
    "    #為什麼要加InceptionV3_block 原因?\n",
    "    x =InceptionV3_block(x, ((128,), (192,256), (32,64), (64,)), 3, 'Block_4')\n",
    "    x =InceptionV3_block(x, ((128,), (192,256), (32,64), (64,)), 3, 'Block_5')\n",
    "    x =InceptionV3_block(x, ((128,), (192,256), (32,64), (64,)), 3, 'Block_6')\n",
    "    x =MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
    "\n",
    "    if include_top:\n",
    "        # Classification block\n",
    "        x = Flatten(name='flatten')(x)\n",
    "        x = Dense(4096, activation='relu', name='fc1')(x)\n",
    "        x = Dense(4096, activation='relu', name='fc2')(x)\n",
    "        x = Dense(classes, activation='softmax', name='predictions')(x)\n",
    "    else:\n",
    "       #可以提醒學員為什麼要加avg或是max\n",
    "        if pooling == 'avg':\n",
    "            x = GlobalAveragePooling2D()(x)\n",
    "        elif pooling == 'max':\n",
    "            x = GlobalMaxPooling2D()(x)\n",
    "\n",
    "    inputs = img_input\n",
    "    # Create model.\n",
    "    model = Model(inputs, x, name='vgg16')\n",
    "\n",
    "   \n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mQufg4jFJbas"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'InceptionV1_block' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-a5251bbf8ed4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mVGG16_ResNet_Inception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude_top\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-25-ec47c4c64d8c>\u001b[0m in \u001b[0;36mVGG16_ResNet_Inception\u001b[1;34m(include_top, input_tensor, input_shape, pooling, classes)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[1;31m# Block 3\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mInceptionV1_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m96\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Block_1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mInceptionV1_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m96\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Block_2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mInceptionV1_block\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m96\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Block_3'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'InceptionV1_block' is not defined"
     ]
    }
   ],
   "source": [
    "model = VGG16_ResNet_Inception(include_top=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e3WfMLiYJbav"
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Classic CNN-ResNet、InceptionV4、Inception-ResNet作業.ipynb",
   "provenance": []
  },
  "kernelspec": {
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   "codemirror_mode": {
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