homework/Day013_池化、全連接層_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 『作業內容』\n",
    "####   依照指示，搭建Maxpooling層與全連接層"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 『目標』\n",
    "####   了解Maxpooling的原理與CNN、FC層連結的方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential  #用來啟動 NN\n",
    "from keras.layers import Conv2D  # Convolution Operation\n",
    "from keras.layers import MaxPooling2D # Pooling\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense # Fully Connected Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "43xxX0yJxiDr"
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   "outputs": [],
   "source": [
    "input_shape = (32, 32, 3)\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), padding='same',input_shape=input_shape))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2),strides=(2, 2)))  ##pooling_size=2,2 strides=2,2 \n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2),strides=(2, 2)))##pooling_size=2,2 strides=2,2 \n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(1, 1),strides=(1, 1)))##pooling_size=1,1 strides=1,1 \n",
    "\n",
    "model.add(Conv2D(10, kernel_size=(3, 3), padding='same'))\n",
    "model.add(Flatten()) ##Flatten\n",
    "#model.add(GlobalAveragePooling2D()) #關掉Flatten，使用GlobalAveragePooling2D\n",
    "\n",
    "model.add(Dense(units=28, input_shape=input_shape)) ##全連接層使用28個units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nFgU3mB-xiDt",
    "outputId": "6fb884fa-75ad-4b47-b5c6-1fc948b2d614"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 8, 8, 10)          11530     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 640)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 28)                17948     \n",
      "=================================================================\n",
      "Total params: 122,726\n",
      "Trainable params: 122,726\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_5 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 8, 128)         73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 8, 8, 10)          11530     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 8, 8, 28)          308       \n",
      "=================================================================\n",
      "Total params: 105,086\n",
      "Trainable params: 105,086\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#沒有Flatten Layer\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), padding='same', input_shape=input_shape))\n",
    "model.add(MaxPooling2D(strides=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))\n",
    "model.add(MaxPooling2D(strides=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))\n",
    "model.add(MaxPooling2D(pool_size=(1, 1), strides=(1, 1)))\n",
    "\n",
    "model.add(Conv2D(10, kernel_size=(3, 3), padding='same'))\n",
    "\n",
    "model.add(Dense(units=28, input_shape=input_shape))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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