利用TensorFlow实现CNN

import tensorflow as tffrom tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets mnist = read_data_sets("G://MNIST_data/", one_hot=True)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)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')x = tf.placeholder(tf.float32, [None, 784])y = tf.placeholder(tf.float32, [None, 10])W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])x_image = tf.reshape(x, [-1,28,28,1])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)W_conv2 = weight_variable([5, 5, 32, 64])b_conv2 = bias_variable([64])tf.summary.histogram('h1', h_conv1)h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)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)keep_prob = tf.placeholder(tf.float32)h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_conv))tf.summary.histogram(name = 'loss',values=cross_entropy)train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))merged = tf.summary.merge_all()#将图形、训练过程等数据合并在一起with tf.Session() as sess:    sess.run(tf.global_variables_initializer())         writer = tf.summary.FileWriter('log', sess.graph)    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))        summary, _ = sess.run([merged, train_step],feed_dict={x: batch[0], y: batch[1], keep_prob: 0.5})        writer.add_summary(summary, i)    print("test accuracy %g"%accuracy.eval(feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0}))writer.close()