code/02_Softmax_Regression_with_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST 手寫數字辨識 - Softmax Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下載 MINST data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data/', one_hot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "im_dim = mnist.train.images.shape[1] # 影像維度\n",
    "lb_dim = mnist.train.labels.shape[1] # 類別數量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建立 Softmax 模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.zeros([im_dim, lb_dim]))    # 784 * 10\n",
    "b = tf.Variable(tf.zeros([lb_dim]))            # 10\n",
    "\n",
    "x = tf.placeholder(tf.float32, [None, im_dim]) # None代表可以是任意維度\n",
    "y = tf.nn.softmax(tf.matmul(x, W) + b)         # y = W * x + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 訓練模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_ = tf.placeholder(tf.float32, [None, lb_dim])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定義 loss function 為 cross-entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices = [1])) # reduction_indices = [1] 加總第2維度數值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "定義訓練方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "初始化參數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "啟動 Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "開始訓練"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "itera     = 1000 # 迭帶次數\n",
    "num_batch = 100  # 訓練的批次數量\n",
    "\n",
    "for step in range(itera):\n",
    "    batch_xs, batch_ys = mnist.train.next_batch(num_batch)\n",
    "    sess.run(train, feed_dict = {x: batch_xs, y_: batch_ys})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 評估訓練結果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用 tf.argmax() 找出最大數值的 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict = tf.argmax(y , 1) # 預測結果\n",
    "correct = tf.argmax(y_, 1) # 實際結果\n",
    "\n",
    "correct_predict = tf.equal(correct, predict) # 若兩者一樣為True，不一樣為False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "將布林值轉為浮點數，計算平均值。例如: [True, True, False, True] -> [1, 1, 0, 1]，平均值為0.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "輸入測試資料集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "準確率: 0.915\n"
     ]
    }
   ],
   "source": [
    "print('準確率: {:.3f}'.format(sess.run(accuracy, feed_dict = {x: mnist.test.images, y_: mnist.test.labels})))"
   ]
  }
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