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import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('data/', one_hot=True)# 神经网络的框架（输入层，隐层（两层），输出层）n_hidden_1 = 256 #隐层第一层神经元的个数n_hidden_2 = 128 #隐层第二层神经元的个数n_input    = 784 #样本特征（像素点）n_classes  = 10  #分类的类别# 输入和输出 x = tf.placeholder("float", [None, n_input])y = tf.placeholder("float", [None, n_classes])    # 神经网络的参数（w，b）std = 0.1#标准方差weights = {    'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=std)),    'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=std)),    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=std))}#w1大小为784*256矩阵；w2大小为256*128矩阵；高斯初始化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]))}#b1大小256；b2大小128；out大小10#前向传播（输入X，权重参数W，偏置项b）def multilayer_forward(_X, _weights, _biases):    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['w1']), _biases['b1'])) #计算W*X+b之后在进行Sigmoid激活    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['w2']), _biases['b2']))    return (tf.matmul(layer_2, _weights['out']) + _biases['out'])#输出层，无激活函数#一次前向传播的结果（预测值）pred = multilayer_forward(x, weights, biases)# 损失函数（cost）cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(y,pred)) #交叉熵函数（预测值，标签（真实值））#梯度下降，进行优化求解optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost) #计算精度（准确率）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()training_epochs = 30batch_size      = 46display_step    = 5# LAUNCH THE GRAPHsess = tf.Session()sess.run(init)# 优化for epoch in range(training_epochs):    avg_cost = 0.0    total_batch = int(mnist.train.num_examples/batch_size)    # 迭代    for i in range(total_batch):        batch_xs, batch_ys = mnist.train.next_batch(batch_size)        feeds = {x: batch_xs, y: batch_ys}#填充对应的值        sess.run(optm, feed_dict=feeds)#不断优化求解        avg_cost += sess.run(cost, feed_dict=feeds)    avg_cost = avg_cost / total_batch    # 显示    if epoch % display_step == 0:        print ("Epoch: %02d/%02d cost: %.6f" % (epoch, training_epochs, avg_cost))        feeds = {x: batch_xs, y: batch_ys}        train_acc = sess.run(accr, feed_dict=feeds)#训练集的精度        print ("TRAIN ACCURACY: %.3f" % (train_acc))        feeds = {x: mnist.test.images, y: mnist.test.labels}        test_acc = sess.run(accr, feed_dict=feeds)#测试集的精度        print ("TEST ACCURACY: %.3f" % (test_acc))print("Done")

运行结果：

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