极客 MNIST预测数字深入理解.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# 深入理解mnist http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_pros.html\n",
    "\n",
    "import input_data    # 自己路径下的input_data.py\n",
    "mnist = input_data.read_data_sets(\"data/MNIST_data/\", one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(\"float\", shape=[None, 784])\n",
    "y_ = tf.placeholder(\"float\", shape=[None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = tf.Variable(tf.zeros([784,10]))\n",
    "b = tf.Variable(tf.zeros([10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = tf.nn.softmax(tf.matmul(x,W) + b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(1000):\n",
    "  batch = mnist.train.next_batch(50)\n",
    "  train_step.run(feed_dict={x: batch[0], y_: batch[1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.916\n"
     ]
    }
   ],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 权重初始化\n",
    "\n",
    "def weight_variable(shape):\n",
    "  initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "  return tf.Variable(initial)\n",
    "\n",
    "def bias_variable(shape):\n",
    "  initial = tf.constant(0.1, shape=shape)\n",
    "  return tf.Variable(initial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 卷积和池化\n",
    "def conv2d(x, W):\n",
    "  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n",
    "                        strides=[1, 2, 2, 1], padding='SAME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 第一层卷积\n",
    "W_conv1 = weight_variable([5, 5, 1, 32])\n",
    "b_conv1 = bias_variable([32])\n",
    "\n",
    "x_image = tf.reshape(x, [-1,28,28,1])\n",
    "\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 第二层卷积\n",
    "W_conv2 = weight_variable([5, 5, 32, 64])\n",
    "b_conv2 = bias_variable([64])\n",
    "\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#密集练接层\n",
    "W_fc1 = weight_variable([7 * 7 * 64, 1024])\n",
    "b_fc1 = bias_variable([1024])\n",
    "\n",
    "h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Dropout\n",
    "keep_prob = tf.placeholder(\"float\")\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 输出层\n",
    "\n",
    "W_fc2 = weight_variable([1024, 10])\n",
    "b_fc2 = bias_variable([10])\n",
    "\n",
    "y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 训练和评估模型\n",
    "\n",
    "cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))\n",
    "train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for i in range(20000):\n",
    "  batch = mnist.train.next_batch(50)\n",
    "  if i%100 == 0:\n",
    "    train_accuracy = accuracy.eval(feed_dict={\n",
    "        x:batch[0], y_: batch[1], keep_prob: 1.0})\n",
    "    print( \"step %d, training accuracy %g\"%(i, train_accuracy))\n",
    "  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n",
    "\n",
    "print(\"test accuracy %g\"%accuracy.eval(feed_dict={\n",
    "    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}