homework/Day_096_HW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Conv2D, SeparableConv2D, Input\n",
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_image = Input((224, 224, 3))\n",
    "feature_maps = Conv2D(filters=32, kernel_size=(3,3))(input_image)\n",
    "feature_maps2 = Conv2D(filters=64, kernel_size=(3,3))(feature_maps)\n",
    "model = Model(inputs=input_image, outputs=feature_maps2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_5 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 222, 222, 32)      896       \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 220, 220, 64)      18496     \n",
      "=================================================================\n",
      "Total params: 19,392\n",
      "Trainable params: 19,392\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可以看到經過兩次 Conv2D，如果沒有設定 padding=\"SAME\"，圖就會越來越小，同時特徵圖的 channel 數與 filters 的數量一致"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_image = Input((224, 224, 3))\n",
    "feature_maps = SeparableConv2D(filters=32, kernel_size=(3,3))(input_image)\n",
    "feature_maps2 = SeparableConv2D(filters=64, kernel_size=(3,3))(feature_maps)\n",
    "model = Model(inputs=input_image, outputs=feature_maps2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_6 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "separable_conv2d_1 (Separabl (None, 222, 222, 32)      155       \n",
      "_________________________________________________________________\n",
      "separable_conv2d_2 (Separabl (None, 220, 220, 64)      2400      \n",
      "=================================================================\n",
      "Total params: 2,555\n",
      "Trainable params: 2,555\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可以看到使用 Seperable Conv2D，即使模型設置都一模一樣，但是參數量明顯減少非常多！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "請閱讀 Keras 官方範例 [mnist_cnn.py](https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py)  \n",
    "\n",
    "並回答下列問題。僅有 70 行程式碼，請確保每一行的程式碼你都能夠理解目的\n",
    "\n",
    "1. 是否有對資料做標準化 (normalization)? 如果有，在哪幾行?\n",
    "2. 使用的優化器 Optimizer 為何?\n",
    "3. 模型總共疊了幾層卷積層?\n",
    "4. 模型的參數量是多少?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ANS:\n",
    "1.第37&38行\n",
    "2.Adadelta\n",
    "3.2層\n",
    "4.1,199,882"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mnist_cnn.py\n",
    "'''Trains a simple convnet on the MNIST dataset.\n",
    "Gets to 99.25% test accuracy after 12 epochs\n",
    "(there is still a lot of margin for parameter tuning).\n",
    "16 seconds per epoch on a GRID K520 GPU.\n",
    "'''\n",
    "\n",
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\n",
    "          verbose=1,\n",
    "          validation_data=(x_test, y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  }
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