Python38-Tensorflow实现Softmax Regression识别手写数字.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "#加载数据集，下载mnist数据到data文件夹下\n",
    "mnist=input_data.read_data_sets(\"data/MNIST_data/\",one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(55000, 784) (55000, 10)\n",
      "(10000, 784) (10000, 10)\n",
      "(5000, 784) (5000, 10)\n"
     ]
    }
   ],
   "source": [
    "#打印数据情况\n",
    "print(mnist.train.images.shape,mnist.train.labels.shape)#训练集55000个样本，\n",
    "print(mnist.test.images.shape,mnist.test.labels.shape)#测试集10000个样本\n",
    "print(mnist.validation.images.shape,mnist.validation.labels.shape)#验证集1000个样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#实现softmax regression\n",
    "import tensorflow as tf\n",
    "sess=tf.InteractiveSession()\n",
    "x=tf.placeholder(tf.float32,[None,784])\n",
    "W=tf.Variable(tf.zeros([784,10]))\n",
    "b=tf.Variable(tf.zeros([10]))\n",
    "y=tf.nn.softmax(tf.matmul(x,W)+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#计算交叉熵\n",
    "y_=tf.placeholder(tf.float32,[None,10])\n",
    "cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#SGD随机梯度优化\n",
    "train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#初始化并执行run方法\n",
    "tf.global_variables_initializer().run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#使用部分数据进行随机梯度下降优化\n",
    "for i in range(1000):\n",
    "    batch_xs,batch_ys=mnist.train.next_batch(100)\n",
    "    train_step.run({x:batch_xs,y_:batch_ys})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#验证分类是否正确\n",
    "correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#统计全部样本预测的准确率\n",
    "accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9212\n"
     ]
    }
   ],
   "source": [
    "#打印结果\n",
    "print(accuracy.eval({x:mnist.test.images,y_:mnist.test.labels}))"
   ]
  }
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