tensorflow 2.0 学习 （十一）卷积神经网络 （一）MNIST数据集训练与预测 LeNet-5网络

 1 # encoding: utf-8
 2 
 3 import tensorflow as tf
 4 from tensorflow import keras
 5 from tensorflow.keras import layers, Sequential, losses, optimizers, datasets
 6 import matplotlib.pyplot as plt
 7 
 8 Epoch = 30
 9 path = r‘G:\2019\python\mnist.npz‘
10 (x, y), (x_val, y_val) = tf.keras.datasets.mnist.load_data(path)  # 60000 and 10000
11 print(‘datasets:‘, x.shape, y.shape, x.min(), x.max())
12 
13 x = tf.convert_to_tensor(x, dtype = tf.float32)  #/255.    #0:1  ;   -1:1(不适合训练，准确度不高)
14 # x = tf.reshape(x, [-1, 28*28])
15 y = tf.convert_to_tensor(y, dtype=tf.int32)
16 # y = tf.one_hot(y, depth=10)
17 #将60000组训练数据切分为600组，每组100个数据
18 train_db = tf.data.Dataset.from_tensor_slices((x, y))
19 train_db = train_db.shuffle(60000)      #尽量与样本空间一样大
20 train_db = train_db.batch(100)          #128
21 
22 x_val = tf.cast(x_val, dtype=tf.float32)
23 y_val = tf.cast(y_val, dtype=tf.int32)
24 test_db = tf.data.Dataset.from_tensor_slices((x_val, y_val))
25 test_db = test_db.shuffle(10000)
26 test_db = test_db.batch(100)        #128
27 
28 network = Sequential([
29     layers.Conv2D(6, kernel_size=3, strides=1),  # 6个卷积核
30     layers.MaxPooling2D(pool_size=2, strides=2),  # 池化层，高宽各减半
31     layers.ReLU(),
32     layers.Conv2D(16, kernel_size=3, strides=1),  # 16个卷积核
33     layers.MaxPooling2D(pool_size=2, strides=2),  # 池化层，高宽各减半
34     layers.ReLU(),
35     layers.Flatten(),
36 
37     layers.Dense(120, activation=‘relu‘),
38     layers.Dense(84, activation=‘relu‘),
39     layers.Dense(10)
40 ])
41 network.build(input_shape=(4, 28, 28, 1))
42 network.summary()
43 optimizer = tf.keras.optimizers.RMSprop(0.001)              # 创建优化器，指定学习率
44 criteon = losses.CategoricalCrossentropy(from_logits=True)
45 
46 # 保存训练和测试过程中的误差情况
47 train_tot_loss = []
48 test_tot_loss = []
49 
50 
51 for step in range(Epoch):
52     cor, tot = 0, 0
53     for x, y in train_db:
54         with tf.GradientTape() as tape:  # 构建梯度环境
55             # 插入通道维度 [None,28,28] -> [None,28,28,1]
56             x = tf.expand_dims(x, axis=3)
57             out = network(x)
58             y_true = tf.one_hot(y, 10)
59             loss =criteon(y_true, out)
60 
61             out_train = tf.argmax(out, axis=-1)
62             y_train = tf.cast(y, tf.int64)
63             cor += float(tf.reduce_sum(tf.cast(tf.equal(y_train, out_train), dtype=tf.float32)))
64             tot += x.shape[0]
65 
66             grads = tape.gradient(loss, network.trainable_variables)
67             optimizer.apply_gradients(zip(grads, network.trainable_variables))
68     print(‘After %d Epoch‘ % step)
69     print(‘training acc is ‘, cor/tot)
70     train_tot_loss.append(cor/tot)
71 
72     correct, total = 0, 0
73     for x, y in test_db:
74         x = tf.expand_dims(x, axis=3)
75         out = network(x)
76         pred = tf.argmax(out, axis=-1)
77         y = tf.cast(y, tf.int64)
78         correct += float(tf.reduce_sum(tf.cast(tf.equal(y, pred), dtype=tf.float32)))
79         total += x.shape[0]
80     print(‘testing acc is : ‘, correct/total)
81     test_tot_loss.append(correct/total)
82 
83 
84 plt.figure()
85 plt.plot(train_tot_loss, ‘b‘, label=‘train‘)
86 plt.plot(test_tot_loss, ‘r‘, label=‘test‘)
87 plt.xlabel(‘Epoch‘)
88 plt.ylabel(‘ACC‘)
89 plt.legend()
90 plt.savefig(‘exam8.2_train_test_CNN1.png‘)
91 plt.show()