#!/usr/bin/env python import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # In[2]: mnist = input_data.read_data_sets('MNIST_data', one_hot=True) # 每个批次的大小 batch_size = 100 # 计算一共有多少个批次 n_batch = mnist.train.num_examples // batch_size # 初始化权值 def weight_variable(shape):     initial = tf.truncated_normal(shape, stddev=0.1)  # 生成一个截断的正态分布     return tf.Variable(initial) # 初始化偏置 def bias_variable(shape):     initial = tf.constant(0.1, shape=shape)     return tf.Variable(initial) # 卷积层 def conv2d(x, W):     # x input tensor of shape `[batch, in_height, in_width, in_channels]`     # W filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]     # `strides[0] = strides[3] = 1`. strides[1]代表x方向的步长，strides[2]代表y方向的步长     # padding: A `string` from: `"SAME", "VALID"`     return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') # 池化层 def max_pool_2x2(x):     # ksize [1,x,y,1]     return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # 定义两个placeholder x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) # 改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]` x_image = tf.reshape(x, [-1, 28, 28, 1]) # 初始化第一个卷积层的权值和偏置 W_conv1 = weight_variable([5, 5, 1, 16])  # 5*5的采样窗口，32个卷积核从1个平面抽取特征 b_conv1 = bias_variable([16])  # 每一个卷积核一个偏置值 # 把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数 conv2d_1 = conv2d(x_image, W_conv1) + b_conv1 h_conv1 = tf.nn.relu(conv2d_1) h_pool1 = max_pool_2x2(h_conv1)  # 进行max-pooling     # 初始化第二个卷积层的权值和偏置 W_conv2 = weight_variable([5, 5, 16, 32])  # 5*5的采样窗口，64个卷积核从32个平面抽取特征 b_conv2 = bias_variable([32])  # 每一个卷积核一个偏置值     # 把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数 conv2d_2 = conv2d(h_pool1, W_conv2) + b_conv2 h_conv2 = tf.nn.relu(conv2d_2) h_pool2 = max_pool_2x2(h_conv2)  # 进行max-pooling # 28*28的图片第一次卷积后还是28*28，第一次池化后变为14*14 # 第二次卷积后为14*14，第二次池化后变为了7*7 # 进过上面操作后得到64张7*7的平面     # 初始化第一个全连接层的权值 W_fc1 = weight_variable([7 * 7 * 32, 512])  # 上一场有7*7*64个神经元，全连接层有1024个神经元 b_fc1 = bias_variable([512])  # 1024个节点     # 把池化层2的输出扁平化为1维 h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 32])     # 求第一个全连接层的输出 wx_plus_b1 = tf.matmul(h_pool2_flat, W_fc1) + b_fc1 h_fc1 = tf.nn.relu(wx_plus_b1)     # keep_prob用来表示神经元的输出概率 keep_prob = tf.placeholder(tf.float32) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)     # 初始化第二个全连接层 W_fc2 = weight_variable([512, 10]) b_fc2 = bias_variable([10]) wx_plus_b2 = tf.matmul(h_fc1_drop, W_fc2) + b_fc2         # 计算输出 prediction = tf.nn.softmax(wx_plus_b2) # 交叉熵代价函数 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction)) # 使用AdamOptimizer进行优化 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)         # 结果存放在一个布尔列表中 correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))  # argmax返回一维张量中最大的值所在的位置         # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.Session() as sess:     sess.run(tf.global_variables_initializer())     for epoch in range(11):         for batch in range(n_batch):             batch_xs, batch_ys = mnist.train.next_batch(batch_size)             sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.7})         acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})         print("Iter " + str(epoch) + ", Testing Accuracy= " + str(acc))

Iter 0, Testing Accuracy= 0.9366

Iter 1, Testing Accuracy= 0.9602

Iter 2, Testing Accuracy= 0.9677

Iter 3, Testing Accuracy= 0.9735

Iter 4, Testing Accuracy= 0.9769

Iter 5, Testing Accuracy= 0.9803

Iter 6, Testing Accuracy= 0.9783

Iter 7, Testing Accuracy= 0.9842

Iter 8, Testing Accuracy= 0.9839

Iter 9, Testing Accuracy= 0.9853

Iter 10, Testing Accuracy= 0.9848