import tensorflow as tf# 输入数据from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("E:\\MNIST_data", one_hot=True)# 定义网络的超参数learning_rate = 0.001training_iters = 200000batch_size = 128display_step = 5# 定义网络的参数# 输入的维度 (img shape: 28*28)n_input = 784 # 标记的维度 (0-9 digits)n_classes = 10 # Dropout的概率，输出的可能性dropout = 0.75 # 输入占位符x = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_classes])#dropout (keep probability)keep_prob = tf.placeholder(tf.float32) # 定义卷积操作def conv2d(name,x, W, b, strides=1):    # Conv2D wrapper, with bias and relu activation    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')    x = tf.nn.bias_add(x, b)    # 使用relu激活函数    return tf.nn.relu(x,name=name)  # 定义池化层操作def maxpool2d(name,x, k=2):    # MaxPool2D wrapper    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME',name=name)# 规范化操作def norm(name, l_input, lsize=4):    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0,beta=0.75, name=name)# 定义所有的网络参数weights = {    'wc1': tf.Variable(tf.random_normal([11, 11, 1, 96])),    'wc2': tf.Variable(tf.random_normal([5, 5, 96, 256])),    'wc3': tf.Variable(tf.random_normal([3, 3, 256, 384])),    'wc4': tf.Variable(tf.random_normal([3, 3, 384, 384])),    'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])),    'wd1': tf.Variable(tf.random_normal([4*4*256, 4096])),    'wd2': tf.Variable(tf.random_normal([4096, 1024])),    'out': tf.Variable(tf.random_normal([1024, n_classes]))}biases = {    'bc1': tf.Variable(tf.random_normal([96])),    'bc2': tf.Variable(tf.random_normal([256])),    'bc3': tf.Variable(tf.random_normal([384])),    'bc4': tf.Variable(tf.random_normal([384])),    'bc5': tf.Variable(tf.random_normal([256])),    'bd1': tf.Variable(tf.random_normal([4096])),    'bd2': tf.Variable(tf.random_normal([1024])),    'out': tf.Variable(tf.random_normal([n_classes]))}# 定义整个网络def alex_net(x, weights, biases, dropout):    # 向量转为矩阵 Reshape input picture    x = tf.reshape(x, shape=[-1, 28, 28, 1])    # 第一层卷积    # 卷积    conv1 = conv2d('conv1', x, weights['wc1'], biases['bc1'])    # 下采样    pool1 = maxpool2d('pool1', conv1, k=2)    # 规范化    norm1 = norm('norm1', pool1, lsize=4)    # 第二层卷积    # 卷积    conv2 = conv2d('conv2', norm1, weights['wc2'], biases['bc2'])    # 最大池化（向下采样）    pool2 = maxpool2d('pool2', conv2, k=2)    # 规范化    norm2 = norm('norm2', pool2, lsize=4)    # 第三层卷积    # 卷积    conv3 = conv2d('conv3', norm2, weights['wc3'], biases['bc3'])    # 规范化    norm3 = norm('norm3', conv3, lsize=4)    # 第四层卷积    conv4 = conv2d('conv4', norm3, weights['wc4'], biases['bc4'])    # 第五层卷积    conv5 = conv2d('conv5', conv4, weights['wc5'], biases['bc5'])    # 最大池化（向下采样）    pool5 = maxpool2d('pool5', conv5, k=2)    # 规范化    norm5 = norm('norm5', pool5, lsize=4)    # 全连接层1    fc1 = tf.reshape(norm5, [-1, weights['wd1'].get_shape().as_list()[0]])    fc1 =tf.add(tf.matmul(fc1, weights['wd1']),biases['bd1'])    fc1 = tf.nn.relu(fc1)    # dropout    fc1=tf.nn.dropout(fc1,dropout)    # 全连接层2    fc2 = tf.reshape(fc1, [-1, weights['wd2'].get_shape().as_list()[0]])    fc2 =tf.add(tf.matmul(fc2, weights['wd2']),biases['bd2'])    fc2 = tf.nn.relu(fc2)    # dropout    fc2=tf.nn.dropout(fc2,dropout)    # 输出层    out = tf.add(tf.matmul(fc2, weights['out']) ,biases['out'])    return out# 构建模型pred = alex_net(x, weights, biases, keep_prob)# 定义损失函数和优化器cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# 评估函数correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# 初始化变量init = tf.global_variables_initializer()# 开启一个训练with tf.Session() as sess:    sess.run(init)    step = 1    # 开始训练，直到达到training_iters，即200000    while step * batch_size < training_iters:        #获取批量数据        batch_x, batch_y = mnist.train.next_batch(batch_size)        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})        if step % display_step == 0:            # 计算损失值和准确度，输出            loss,acc = sess.run([cost,accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})            print ("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))        step += 1    print ("Optimization Finished!")    # 计算测试集的精确度    print ("Testing Accuracy:",sess.run(accuracy, feed_dict={x: mnist.test.images[:256],y: mnist.test.labels[:256],keep_prob: 1.}))