1 import os
  2 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  3 import tensorflow as tf
  4 from tensorflow.examples.tutorials.mnist import input_data
  5 
  6 #载入数据集
  7 mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
  8 
  9 #每个批次的大小
 10 batch_size = 100
 11 
 12 #计算一个有多少个批次
 13 n_batch = mnist.train.num_examples // batch_size
 14 
 15 #初始化权值
 16 def weight_variable(shape):
 17     initial = tf.truncated_normal(shape, stddev=0.1)#生成一个截断的正态分布
 18     return tf.Variable(initial)
 19 
 20 #初始化偏置
 21 def bias_variable(shape):
 22     initial = tf.constant(0.1, shape=shape)
 23     return tf.Variable(initial)
 24 
 25 #卷积层
 26 def conv2d(x,W):
 27     # x input tensor of shape `[batch, in_height, in_width, in_channels]`
 28     # W filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]
 29     # `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
 30     # padding: A `string` from: `"SAME", "VALID"`
 31     return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
 32 
 33 #池化层
 34 def max_pool_2x2(x):
 35     # ksize [1,x,y,1]
 36     return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
 37 
 38 #定义两个placeholder
 39 x = tf.placeholder(tf.float32, [None, 784]) #28*28
 40 y = tf.placeholder(tf.float32, [None,10])
 41 
 42 #改变x的格式转为4D的向量[batch，in_height,in_width,in_channels]
 43 x_image = tf.reshape(x, [-1, 28, 28, 1])
 44 
 45 #初始化第一个卷积层的权值和偏置
 46 W_conv1 = weight_variable([5, 5, 1, 32])  #5*5的采样窗口，32个卷积核从1个平面抽取特征
 47 b_conv1 = bias_variable([32]) #每一个卷积核一个偏置值
 48 
 49 #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
 50 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
 51 h_pool1 = max_pool_2x2(h_conv1)
 52 
 53 #初始第二个卷积层的权值和偏置值
 54 W_conv2 = weight_variable([5, 5, 32, 64])#5*5的采样窗口，64个卷积核从32个平面抽取特征
 55 b_conv2 = bias_variable([64])
 56 
 57 #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
 58 h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
 59 h_pool2 = max_pool_2x2(h_conv2)
 60 
 61 #28*28的图片第一次卷积后还是28*28，第一次池化后变成14*14
 62 #第二次卷积为14*14，第二次池化后变成可7*7
 63 #经过上面的操作后得到64张7*7的平面
 64 
 65 #初始化第一个全连接层的权值
 66 W_fc1 = weight_variable([7*7*64, 1024])#上一层有7*7*64个神经元，全连接层有1024个神经元
 67 b_fc1 = bias_variable([1024])#1024个节点
 68 
 69 #把池化层2的输出扁平化为1维
 70 h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
 71 
 72 #求第一个全连接层的输出
 73 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
 74 
 75 #keep_prob用来表示神经元的输出概率
 76 keep_prob = tf.placeholder(tf.float32)
 77 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
 78 
 79 #初始化第二个全连接层
 80 W_fc2 = weight_variable([1024, 10])
 81 b_fc2 = bias_variable([10])
 82 
 83 #计算输出
 84 prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
 85 
 86 #交叉熵代价函数
 87 cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
 88 
 89 #使用AdmaOptimizer进行优化
 90 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
 91 
 92 #结果存放在一个布尔列表中
 93 correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
 94 
 95 #求准确率
 96 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
 97 
 98 #变量初始化
 99 init = tf.global_variables_initializer()
100 
101 with tf.Session() as sess:
102     sess.run(init)
103     for epoch in range(21):
104         for batch in range(n_batch):
105             batch_xs, batch_ys = mnist.train.next_batch(batch_size)
106             sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.7})
107         acc = sess.run(accuracy, feed_dict={x: mnist.test.images[:5000], y: mnist.test.labels[:5000], keep_prob: 1.0})
108         print('Iter : ' + str(epoch) + ',Testing Accuracy = ' + str(acc))