# 定义参数n_hidden_1 = 256    #第一层神经元n_hidden_2 = 128    #第二层神经元n_input = 784       #输入大小，28*28的一个灰度图，彩图没有什么意义n_classes = 10      #结果是要得到一个几分类的任务# 输入和输出x = tf.placeholder("float", [None, n_input])y = tf.placeholder("float", [None, n_classes])    # 权重和偏置参数stddev = 0.1weights = {    ‘w1‘: tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)),    ‘w2‘: tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)),    ‘out‘: tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev))}biases = {    ‘b1‘: tf.Variable(tf.random_normal([n_hidden_1])),    ‘b2‘: tf.Variable(tf.random_normal([n_hidden_2])),    ‘out‘: tf.Variable(tf.random_normal([n_classes]))}print ("NETWORK READY")def multilayer_perceptron(_X, _weights, _biases):    #第1层神经网络 = tf.nn.激活函数(tf.加上偏置量(tf.矩阵相乘(输入Data, 权重W1), 偏置参数b1))    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights[‘w1‘]), _biases[‘b1‘]))     #第2层的格式与第1层一样，第2层的输入是第1层的输出。    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights[‘w2‘]), _biases[‘b2‘]))    #返回预测值    return (tf.matmul(layer_2, _weights[‘out‘]) + _biases[‘out‘])        # 预测pred = multilayer_perceptron(x, weights, biases)# 计算损失函数和优化cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost) corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    accr = tf.reduce_mean(tf.cast(corr, "float"))# 初始化init = tf.global_variables_initializer()print ("FUNCTIONS READY")# 训练training_epochs = 20batch_size      = 100display_step    = 4# LAUNCH THE GRAPHsess = tf.Session()sess.run(init)# 优化器for epoch in range(training_epochs):    avg_cost = 0.    total_batch = int(mnist.train.num_examples/batch_size)    # 迭代训练    for i in range(total_batch):        batch_xs, batch_ys = mnist.train.next_batch(batch_size)        feeds = {x: batch_xs, y: batch_ys}        sess.run(optm, feed_dict=feeds)        avg_cost += sess.run(cost, feed_dict=feeds)    avg_cost = avg_cost / total_batch    # 打印结果    if (epoch+1) % display_step == 0:        print ("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))        feeds = {x: batch_xs, y: batch_ys}        train_acc = sess.run(accr, feed_dict=feeds)        print ("TRAIN ACCURACY: %.3f" % (train_acc))        feeds = {x: mnist.test.images, y: mnist.test.labels}        test_acc = sess.run(accr, feed_dict=feeds)        print ("TEST ACCURACY: %.3f" % (test_acc))print ("OPTIMIZATION FINISHED")