1 import tensorflow as tf
2
3 fashion = tf.keras.datasets.fashion_mnist
4 (x_train, y_train), (x_test, y_test) = fashion.load_data()
5
6 print(x_train.shape, y_train.shape)
7 print(x_test.shape, y_test.shape)
8 print(x_train[0])
9 print(y_train[0])
10
11 x_train, x_test = x_train/255.0, x_test/255.0
12
13 model = tf.keras.models.Sequential([
14 tf.keras.layers.Flatten(),
15 tf.keras.layers.Dense(128, activation='relu'),
16 tf.keras.layers.Dense(10, activation='softmax')
17 ])
18
19 model.compile(optimizer='adam',
20 loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
21 metrics=['sparse_categorical_accuracy'])
22
23 model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
24 model.summary()
25
26
27
28
29
30 import tensorflow as tf
31 from tensorflow.keras.layers import Flatten, Dense
32 from tensorflow.keras import Model
33
34 fashion = tf.keras.datasets.fashion_mnist
35 (x_train, y_train), (x_test, y_test) = fashion.load_data()
36 x_train, x_test = x_train/255.0, x_test/255.0
37
38 class FashionModel(Model):
39 def __init__(self):
40 super(FashionModel, self).__init__()
41 self.flatten = Flatten()
42 self.d1 = Dense(128, activation='relu')
43 self.d2 = Dense(10, activation='softmax')
44
45 def call(self, x):
46 x = self.flatten(x)
47 x = self.d1(x)
48 y = self.d2(x)
49 return y
50
51
52 model = FashionModel()
53
54 model.compile(optimizer = 'adam',
55 loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
56 metrics=['sparse_categorical_accuracy'])
57
58 model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
59 model.summary()
