TensorFlow训练MNIST数据集（3） —— 卷积神经网络

 1 import tensorflow as tf
 2 from tensorflow.examples.tutorials.mnist import input_data
 3 import time
 4 
 5 # 读取数据
 6 mnist = input_data.read_data_sets(‘./data/mnist‘, one_hot=True)
 7 
 8 # 输入层
 9 inputLayer = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
10 
11 # 卷积层(1)
12 convFilter1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32], mean=0, stddev=0.1))
13 convBias1   = tf.Variable(tf.truncated_normal([32], mean=0, stddev=0.1))
14 convLayer1  = tf.nn.conv2d(input=inputLayer, filter=convFilter1, strides=[1, 1, 1, 1], padding=‘SAME‘)
15 convLayer1  = tf.add(convLayer1, convBias1)
16 convLayer1  = tf.nn.relu(convLayer1)
17 
18 # 池化层(1)
19 poolLayer1 = tf.nn.max_pool(value=convLayer1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)
20 
21 # 卷积层(2)
22 convFilter2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], mean=0, stddev=0.1))
23 convBias2   = tf.Variable(tf.truncated_normal([64], mean=0, stddev=0.1))
24 convLayer2  = tf.nn.conv2d(input=poolLayer1, filter=convFilter2, strides=[1, 1, 1, 1], padding=‘SAME‘)
25 convLayer2  = tf.add(convLayer2, convBias2)
26 convLayer2  = tf.nn.relu(convLayer2)
27 
28 # 池化层(2)
29 poolLayer2 = tf.nn.max_pool(value=convLayer2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)
30 
31 # 全连接层
32 fullWeight = tf.Variable(tf.truncated_normal(shape=[7 * 7 * 64, 1024], mean=0, stddev=0.1))
33 fullBias   = tf.Variable(tf.truncated_normal(shape=[1024], mean=0.0, stddev=0.1))
34 fullInput  = tf.reshape(poolLayer2, [-1, 7 * 7 * 64])
35 fullLayer  = tf.add(tf.matmul(fullInput, fullWeight), fullBias)
36 fullLayer  = tf.nn.relu(fullLayer)
37 
38 # dropout层
39 dropLayer = tf.nn.dropout(fullLayer, keep_prob=0.8)
40 
41 # 输出层
42 outputWeight = tf.Variable(tf.truncated_normal(shape=[1024, 10], mean=0.0, stddev=0.1))
43 outputBias   = tf.Variable(tf.truncated_normal(shape=[10], mean=0, stddev=0.1))
44 outputLayer  = tf.add(tf.matmul(dropLayer, outputWeight), outputBias)
45 
46 # 标签
47 outputLabel = tf.placeholder(tf.float32, shape=[None, 10])
48 
49 # 损失函数及目标函数
50 loss   = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=outputLabel, logits=outputLayer))
51 target = tf.train.AdamOptimizer().minimize(loss)
52 
53 # 记录起始训练时间
54 startTime = time.time()
55 
56 # 训练
57 with tf.Session() as sess:
58     sess.run(tf.global_variables_initializer())
59     batchSize = 64
60     for i in range(1000):
61         batch = mnist.train.next_batch(batchSize)
62         inputData = batch[0].reshape([batchSize, 28, 28, 1])
63         labelData = batch[1]
64         sess.run([target, loss], feed_dict={inputLayer: inputData, outputLabel: labelData})
65 
66         corrected = tf.equal(tf.argmax(outputLabel, 1), tf.argmax(outputLayer, 1))
67         accuracy = tf.reduce_mean(tf.cast(corrected, tf.float32))
68         accuracyValue = sess.run(accuracy, feed_dict={inputLayer: inputData, outputLabel: labelData})
69         print(i, ‘train set accuracy:‘, accuracyValue)
70 
71     # 打印结束时间
72     endTime = time.time()
73     print(‘train time:‘, endTime - startTime)
74 
75     # 测试
76     corrected  = tf.equal(tf.argmax(outputLabel, 1), tf.argmax(outputLayer, 1))
77     accuracy   = tf.reduce_mean(tf.cast(corrected, tf.float32))
78     testImages = mnist.test.images.reshape([-1, 28, 28, 1])
79     testLabels = mnist.test.labels
80     accuracyValue = sess.run(accuracy, feed_dict={inputLayer: testImages, outputLabel: testLabels})
81     print("accuracy on test set:", accuracyValue)
82 
83     sess.close()