tensorflow/mnist2.py

# -*- coding: utf-8 -*-
"""
Created on Thu Sep  8 15:29:48 2016

@author: root
"""
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  # 下载并加载mnist数据
x = tf.placeholder(tf.float32, [None, 784])  # 输入的数据占位符
y_actual = tf.placeholder(tf.float32, shape=[None, 10])  # 输入的标签占位符


# 定义一个函数，用于初始化所有的权值 W
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


# 定义一个函数，用于初始化所有的偏置项 b
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


# 定义一个函数，用于构建卷积层
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


# 定义一个函数，用于构建池化层
def max_pool(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# 构建网络
x_image = tf.reshape(x, [-1, 28, 28, 1])  # 转换输入数据shape,以便于用于网络中
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  # 第一个卷积层
h_pool1 = max_pool(h_conv1)  # 第一个池化层

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)  # 第二个卷积层
h_pool2 = max_pool(h_conv2)  # 第二个池化层

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])  # reshape成向量
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)  # 第一个全连接层

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)  # dropout层

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_predict = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)  # softmax层

cross_entropy = -tf.reduce_sum(y_actual * tf.log(y_predict))  # 交叉熵
train_step = tf.train.GradientDescentOptimizer(1e-3).minimize(cross_entropy)  # 梯度下降法
correct_prediction = tf.equal(tf.argmax(y_predict, 1), tf.argmax(y_actual, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))  # 精确度计算
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:  # 训练100次，验证一次
        train_acc = accuracy.eval(feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 1.0})
        print('step', i, 'training accuracy', train_acc)
        train_step.run(feed_dict={x: batch[0], y_actual: batch[1], keep_prob: 0.5})

test_acc = accuracy.eval(feed_dict={x: mnist.test.images, y_actual: mnist.test.labels, keep_prob: 1.0})
print("test accuracy", test_acc)