TensorFlow mnist多层卷积网络官方示例完整代码

根据TensorFlow中文社区的内容写的完整的代码，具体的讲解后续再添加

import tensorflow as tf  from tensorflow.examples.tutorials.mnist import input_data#导入Minst数据集  mnist = input_data.read_data_sets("temp",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("float", shape=[None, 784])y_ = tf.placeholder("float", shape=[None, 10])# 第一层卷积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)#第二层卷积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_pool2 = max_pool_2x2(h_conv2)#密集连接层W_fc1 = weight_variable([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)#dropoutkeep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)# 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cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))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,"float"))sess = tf.Session()sess.run(tf.global_variables_initializer())for i in range(2000):    batch = mnist.train.next_batch(50)    if i%100 == 0:        train_accuracy = sess.run(accuracy,feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})        print("step %d, training accuracy %g"%(i, train_accuracy))    sess.run(train_step,feed_dict={x:batch[0], y_: batch[1], keep_prob: 0.5})print("test accuracy %g"%sess.run(accuracy,feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))