TensorFlow（五）CNN

import tensorflow as tf  from tensorflow.examples.tutorials.mnist import input_data  mnist=input_data.read_data_sets('MNIST_data',one_hot=True)    def compute_accuracy(v_xs,v_ys):      global prediction      y_pre=sess.run(prediction,feed_dict={xs:v_xs})      correct_prediction=tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))      accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))      result=sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys,keep_prob:1})      return result    def weight_variable(shape):    initial=tf.truncated_normal(shape,stddev=0.1)    return tf.Variable(initial)def bias_variable(shape):    inital=tf.constant(0.1,shape=shape)    return tf.Variable(inital)def conv2d(x,W):    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')def max_pool_2x2(x):    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')xs=tf.placeholder(tf.float32,[None,784])#28*28  ys=tf.placeholder(tf.float32,[None,10])  keep_prob=tf.placeholder(tf.float32)x_image=tf.reshape(xs,[-1,28,28,1])#把xs的形状换成28*28的，-1为例子，1为黑白W_conv1=weight_variable([5,5,1,32]) #path5x5，in size 1;out size 32b_convl=bias_variable([32])h_convl=tf.nn.relu(conv2d(x_image,W_conv1)+b_convl) #output size 28x28x32h_pool1=max_pool_2x2(h_convl)                       #output size 14x14x32W_conv2=weight_variable([5,5,32,64]) #path5x5，in size 32;out size 64b_conv2=bias_variable([64])h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2) #output size 14x14x64h_pool2=max_pool_2x2(h_conv2)                       #output size 7x7x64W_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)h_fc1_drop=tf.nn.dropout(h_fc1,keep_prob=0.5)W_fc2=weight_variable([1024,10])b_fc2=bias_variable([10])prediction=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2) cross_entropy=tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))           train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)      sess=tf.Session()      sess.run(tf.initialize_all_variables())  for i in range(1000):      batch_xs,batch_ys=mnist.train.next_batch(100)      sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})      if i%50==0:          print(compute_accuracy(mnist.test.images,mnist.test.labels))