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本文假设大家对CNN、softmax原理已经比较熟悉，着重点在于使用Tensorflow对CNN的简单实践上。所以不会对算法进行详细介绍，主要针对代码中所使用的一些函数定义与用法进行解释，并给出最终运行代码。如果对Tensorflow的一些基本操作不熟悉的话，推荐先看下极客学院的 这篇文章再回来看本文。
数据集是MNIST，一个入门级的计算机视觉数据集，它包含各种手写数字图片：
每张图片包含28X28个像素点，标签即为图片中的数字。
使用MNIST数据集进行训练，识别图片中的手写数字（0到9共10类）。
使用一个简单的CNN网络结构如下，括号里边表示tensor经过本层后的输出shape：
具体的参数看后边的代码注释。
在撸代码前，先对几个会用到的主要函数中的主要参数进行说明。
随机产生一个形状为shape的服从截断正态分布（均值为mean，标准差为stddev）的tensor。截断的方法根据官方API的定义为，如果单次随机生成的值偏离均值2倍标准差之外，就丢弃并重新随机生成一个新的数。
[batch, in_height, in_width, in_channels]的tensor：[filter_height, filter_width, in_channels, out_channels]的tensor：[1, stride, stride, 1]。[1, 2, 2, 1]，即用一个2*2的窗口做pooling。这里不对dropout的算法进行描述，如果不知道自行百度。
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#coding:utf-8import tensorflow as tfimport MNIST_data.input_data as input_dataimport time"""
权重初始化初始化为一个接近0的很小的正数"""def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev = 0.1)
return tf.Variable(initial)def bias_variable(shape):
initial = tf.constant(0.1, shape = shape)
return tf.Variable(initial)"""卷积和池化，
使用卷积步长为1（stride size）,0边距（padding size）池化用简单传统的2x2大小的模板做max pooling"""def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding = 'SAME')
# tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)
# x(input) : [batch, in_height, in_width, in_channels]
# W(filter) : [filter_height, filter_width, in_channels, out_channels]
# strides : The stride of the sliding window for each dimension of input.
# For the most common case of the same horizontal and vertices strides,
strides = [1, stride, stride, 1]def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1], padding = 'SAME')
# tf.nn.max_pool(value, ksize, strides, padding, data_format='NHWC', name=None)
# x(value) : [batch, height, width, channels]
# ksize(pool大小) : A list of ints that has length >= 4.
The size of the window for each dimension of the input tensor.
# strides(pool滑动大小) : A list of ints that has length >= 4.
The stride of the sliding window for each dimension of the input tensor.start = time.clock()
#计算开始时间mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#MNIST数据输入"""第一层 卷积层x_image(batch, 28, 28, 1) -> h_pool1(batch, 14, 14, 32)"""x = tf
.placeholder(tf.float32,[None, 784])x_image = tf.reshape(x, [-1, 28, 28, 1])
#最后一维代表通道数目，如果是rgb则为3 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)# x_image -> [batch, in_height, in_width, in_channels]
# [batch, 28, 28, 1]# W_conv1 -> [filter_height, filter_width, in_channels, out_channels]
# [5, 5, 1, 32]# output -> [batch, out_height, out_width, out_channels]#
[batch, 28, 28, 32]h_pool1 = max_pool_2x2(h_conv1)# h_conv1 -> [batch, in_height, in_weight, in_channels]
# [batch, 28, 28, 32]# output -> [batch, out_height, out_weight, out_channels]
# [batch, 14, 14, 32]"""第二层 卷积层h_pool1(batch, 14, 14, 32) -> h_pool2(batch, 7, 7, 64)"""
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)# h_pool1 -> [batch, 14, 14, 32]# W_conv2 -> [5, 5, 32, 64]# output ->
[batch, 14, 14, 64]h_pool2 = max_pool_2x2(h_conv2)# h_conv2 -> [batch, 14, 14, 64]# output ->
[batch, 7, 7, 64]"""第三层 全连接层h_pool2(batch, 7, 7, 64) -> h_fc1(1, 1024)""
"W_fc1 = weight_variable([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])h_fc1 = tf.nn.relu(tf
.matmul(h_pool2_flat, W_fc1) + b_fc1)"""Dropouth_fc1 -> h_fc1_drop, 训练中启用，测试中关闭"""
keep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)""
"第四层 Softmax输出层"""W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv = tf.nn
.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)"""训练和评估模型ADAM优化器来做梯度最速下降,
feed_dict中加入参数keep_prob控制dropout比例"""y_ = tf.placeholder("float",
[None, 10])cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
#计算交叉熵train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
#使用adam优化器来以0.0001的学习率来进行微调correct_prediction = tf.equal(tf.argmax(y_conv,1), tf
.argmax(y_,1)) #判断预测标签和实际标签是否匹配accuracy = tf.reduce_mean(tf.cast(correct_prediction,
"float"))sess = tf.Session() #启动创建的模型sess.run(tf.initialize_all_variables())
#旧版本#sess.run(tf.global_variables_initializer()) #初始化变量for i in range(5000):
#开始训练模型，循环训练5000次 batch = mnist.train.next_batch(50) #batch大小设置为50
if i % 100 == 0: train_accuracy = accuracy.eval（session = sess,
feed_dict = {x:batch[0], y_:batch[1], keep_prob:1.0})
print("step %d, train_accuracy %g" %(i, train_accuracy))
train_step.run(session = sess, feed_dict = {x:batch[0], y_:batch[1],
keep_prob:0.5}) #神经元输出保持不变的概率 keep_prob 为0.5print("test accuracy %g" %accuracy.eval
（session = sess, feed_dict = {x:mnist.test.images, y_:mnist.test.labels,
keep_prob:1.0})) #神经元输出保持不变的概率 keep_prob 为 1，即不变，一直保持输出end = time.clock()
#计算程序结束时间print("running time is %g s") % (end-start)1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.
20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.
55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.
90.91.92.93.94.95.96.97.98.99.100.101.102.103.104.105.106.107.108.109.110.111.112.113.114.115.116.117.118.
119.120.121.122.123.124.125.126.127.128.129.130.131.132.133.134.135.136.
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