用tensorflow搭建CNN的方法

#encoding=utf-8 import tensorflow as tf  import numpy as np  from tensorflow.examples.tutorials.mnist import input_data  mnist = input_data.read_data_sets('MNIST_data', one_hot=True)  def weight_variable(shape):   initial = tf.truncated_normal(shape,stddev=0.1) #截断正态分布，此函数原型为尺寸、均值、标准差   return tf.Variable(initial) def bias_variable(shape):   initial = tf.constant(0.1,shape=shape)   return tf.Variable(initial) def conv2d(x,W):   return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') # strides第0位和第3为一定为1，剩下的是卷积的横向和纵向步长 def max_pool_2x2(x):   return tf.nn.max_pool(x,ksize = [1,2,2,1],strides=[1,2,2,1],padding='SAME')# 参数同上，ksize是池化块的大小  x = tf.placeholder("float", shape=[None, 784]) y_ = tf.placeholder("float", shape=[None, 10])  # 图像转化为一个四维张量，第一个参数代表样本数量，-1表示不定，第二三参数代表图像尺寸，最后一个参数代表图像通道数 x_image = tf.reshape(x,[-1,28,28,1])  # 第一层卷积加池化 w_conv1 = weight_variable([5,5,1,32]) # 第一二参数值得卷积核尺寸大小，即patch，第三个参数是图像通道数，第四个参数是卷积核的数目，代表会出现多少个卷积特征 b_conv1 = bias_variable([32])  h_conv1 = tf.nn.relu(conv2d(x_image,w_conv1)+b_conv1) h_pool1 = max_pool_2x2(h_conv1)  # 第二层卷积加池化  w_conv2 = weight_variable([5,5,32,64]) # 多通道卷积，卷积出64个特征 b_conv2 = bias_variable([64])  h_conv2 = tf.nn.relu(conv2d(h_pool1,w_conv2)+b_conv2) h_pool2 = max_pool_2x2(h_conv2)  # 原图像尺寸28*28，第一轮图像缩小为14*14，共有32张，第二轮后图像缩小为7*7，共有64张  w_fc1 = weight_variable([7*7*64,1024]) b_fc1 = bias_variable([1024])  h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64]) # 展开，第一个参数为样本数量，-1未知 f_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,w_fc1)+b_fc1)  # dropout操作，减少过拟合 keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(f_fc1,keep_prob)  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)  cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) # 定义交叉熵为loss函数 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # 调用优化器优化 correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))  sess = tf.InteractiveSession() sess.run(tf.initialize_all_variables()) for i in range(2000):  batch = mnist.train.next_batch(50)  if i%100 == 0:   train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})   print "step %d, training accuracy %g"%(i, train_accuracy)  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})  print "test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images[0:500], y_: mnist.test.labels[0:500], keep_prob: 1.0})