第三讲 神经网络八股--mnist数据集分类 

 1 import tensorflow as tf
 2 from matplotlib import pyplot as plt
 3 
 4 
 5 mnist = tf.keras.datasets.mnist
 6 (x_train, y_train), (x_test, y_test) = mnist.load_data()
 7 
 8 
 9 # 可视化训练集输入特征的第一个元素
10 plt.imshow(x_train[0], cmap='gray')  #绘制灰度图
11 plt.show()
12 
13 
14 # 打印出训练集输入特征的第一个元素
15 print("x_train[0]:\n", x_train[0])
16 # 打印出训练集标签的第一个元素
17 print("y_train[0]:\n", y_train[0])
18 
19 
20 
21 # 打印出整个训练集输入特征形状
22 print("x_train.shape:\n", x_train.shape)
23 # 打印出整个训练集标签的形状
24 print("y_train.shape:\n", y_train.shape)
25 # 打印出整个测试集输入特征的形状
26 print("x_test.shape:\n", x_test.shape)
27 # 打印出整个测试集标签的形状
28 print("y_test.shape:\n", y_test.shape)
29 
30 
31 
32 import tensorflow as tf
33 
34 mnist = tf.keras.datasets.mnist
35 (x_train, y_train), (x_test, y_test) = mnist.load_data()
36 x_train, x_test = x_train/255.0, x_test/255.0
37 
38 model = tf.keras.models.Sequential([
39       tf.keras.layers.Flatten(),
40       tf.keras.layers.Dense(128, activation='relu'),
41       tf.keras.layers.Dense(10, activation='softmax')
42 ])
43 
44 model.compile(optimizer='adam',
45               loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
46               metrics=["sparse_categorical_accuracy"])
47 
48 model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test))
49 model.summary()
50 
51 
52 
53 
54 
55 import tensorflow as tf
56 from tensorflow.keras.layers import Dense, Flatten
57 from tensorflow.keras import Model
58 
59 mnist = tf.keras.datasets.mnist
60 (x_train, y_train), (x_test, y_test) = mnist.load_data()
61 x_train, x_test = x_train/255.0, x_test/255.0
62 
63 
64 class MnistModel(Model):
65   def __init__(self):
66     super(MnistModel, self).__init__()
67     self.flatten = Flatten()
68     self.d1 = Dense(128, activation='relu')
69     self.d2 = Dense(10, activation='softmax')
70   
71   def call(self, x):
72     x = self.flatten(x)
73     x = self.d1(x)
74     y = self.d2(x)
75     return y
76 
77 model = MnistModel()
78 
79 model.compile(optimizer='adam',
80               loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
81               metrics=['sparse_categorical_accuracy'])
82 
83 model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
84 model.summary()

 

posted @ 2020-05-04 22:40  WWBlog  阅读(254)  评论(0)    收藏  举报