1 import tensorflow as tf
 2 print(tf.__version__)
 3 
 4 
 5 for gpu in tf.config.experimental.list_physical_devices(‘GPU‘):
 6   tf.config.experimental.set_memory_growth(gpu, True)
 7 
 8 
 9 def nin_block(num_channels, kernel_size, strides, padding):
10   blk = tf.keras.models.Sequential()
11   blk.add(tf.keras.layers.Conv2D(num_channels, kernel_size, strides=strides, 
12                                  padding=padding, activation=‘relu‘))
13   blk.add(tf.keras.layers.Conv2D(num_channels, kernel_size=1, activation=‘relu‘))
14   blk.add(tf.keras.layers.Conv2D(num_channels, kernel_size=1, activation=‘relu‘))
15   return blk
16 
17 
18 net = tf.keras.models.Sequential()
19 net.add(nin_block(96, kernel_size=11, strides=4, padding=‘valid‘))
20 net.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2))
21 net.add(nin_block(256, kernel_size=5, strides=1, padding=‘same‘))
22 net.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2))
23 net.add(nin_block(384, kernel_size=3, strides=1, padding=‘same‘))
24 net.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2))
25 net.add(tf.keras.layers.Dropout(0.5))
26 net.add(nin_block(10, kernel_size=3, strides=1, padding=‘same‘))
27 net.add(tf.keras.layers.GlobalAveragePooling2D())
28 net.add(tf.keras.layers.Flatten())
29 
30 
31 X = tf.random.uniform((1, 224, 224, 1))
32 for blk in net.layers:
33   X = blk(X)
34   print(blk.name, ‘output shape: \t‘, X.shape)
35 
36 
37 import numpy as np
38 
39 class DataLoader():
40   def __init__(self):
41     fashion_mnist = tf.keras.datasets.fashion_mnist
42     (self.train_images, self.train_labels), (self.test_images, self.test_labels) = fashion_mnist.load_data()
43     self.train_images = np.expand_dims(self.train_images.astype(np.float32)/255.0, axis=-1)
44     self.test_images = np.expand_dims(self.test_images.astype(np.float32)/255.0, axis=-1)
45     self.train_labels = self.train_labels.astype(np.int32)
46     self.test_labels = self.test_labels.astype(np.int32)
47     self.num_train, self.num_test = self.train_images.shape[0], self.test_images.shape[0]
48 
49   def get_batch_train(self, batch_size):
50     index = np.random.randint(0, np.shape(self.train_images)[0], batch_size)
51     #need to resize images to (224, 224)
52     resized_images = tf.image.resize_with_pad(self.train_images[index], 224, 224,)
53     return resized_images.numpy(), self.train_labels[index]
54 
55   def get_batch_test(self, batch_size):
56     index = np.random,randint(0, np.shape(self.test_images)[0], batch_size)
57     #need to resize to (224, 224)
58     resized_images = tf.image.resize_with_pad(self.test_images[index], 224, 224,)
59     return resized_images.numpy(), self.test_labels[index]
60 
61 batch_size = 128
62 dataLoader = DataLoader()
63 x_batch, y_batch = dataLoader.get_batch_train(batch_size)
64 print(‘x_batch shape:‘, x_batch.shape, ‘y_batch shape:‘, y_batch.shape)
65 
66 
67 
68 def train_nin():
69   #net.load_weights(‘5.8_nin_weights.h5‘)
70   epoch = 5
71   num_iter = dataLoader.num_train//batch_size
72   for e in range(epoch):
73     for n in range(num_iter):
74       x_batch, y_batch = dataLoader.get_batch_train(batch_size)
75       net.fit(x_batch, y_batch)
76       if n%200 == 0:
77         net.save_weights(‘5.8_nin_weights.h5‘)
78 
79 
80 #optimizer = tf.keras.optimizers.SGD(learning_rate=0.06, momentum=0.3, nesterov=False)
81 optimizer = tf.keras.optimizers.Adam(lr=1e-6)
82 net.compile(optimizer=optimizer, 
83             loss=‘sparse_categorical_crossentropy‘,
84             metrics=[‘accuracy‘])
85 x_batch, y_batch = dataLoader.get_batch_train(batch_size)
86 net.fit(x_batch, y_batch)
87 train_nin()