tensorflow 2.0 学习（十三）卷积神经网络（三） CIFAR10数据集与修改的ResNet18网络 + CoLab

ResNet网络结构如下：

tensorflow 2.0 学习（十三）卷积神经网络（三） CIFAR10数据集与修改的ResNet18网络 + CoLab

采用模型和数据分离的代码方式，模型如下：

  1 # encoding: utf-8
  2 import tensorflow as tf
  3 from tensorflow.keras import optimizers, datasets, Model, layers, Sequential, losses
  4 from tensorflow.keras.layers import Conv2D, Dense, add, BatchNormalization, GlobalAveragePooling2D
  5 import matplotlib.pyplot as plt
  6 
  7 # load data ---------
  8 (x, y), (x_test, y_test) = datasets.cifar10.load_data()
  9 y = tf.squeeze(y, axis=1)
 10 y_test = tf.squeeze(y_test, axis=1)
 11 # print(x.shape, y.shape, x_test.shape, y_test.shape)
 12 # (50000, 32, 32, 3) (50000,) (10000, 32, 32, 3) (10000,)
 13 
 14 
 15 def pre_process(x, y):
 16     x_reshape = tf.cast(x, dtype=tf.float32) / 255.
 17     y_reshape = tf.cast(y, dtype=tf.int32)  # 转化为整型32
 18     y_onehot = tf.one_hot(y_reshape, depth=10)  # 训练数据所需的one-hot编码
 19     return x_reshape, y_onehot
 20 
 21 
 22 train_db = tf.data.Dataset.from_tensor_slices((x, y))
 23 train_db = train_db.shuffle(1000).map(pre_process).batch(128)
 24 test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test))
 25 test_db = test_db.shuffle(1000).map(pre_process).batch(128)
 26 
 27 # sample = next(iter(train_db))
 28 # print('sample:', sample[0].shape, sample[1].shape,
 29 #       tf.reduce_min(sample[0]), tf.reduce_max(sample[0]))
 30 # sample: (128, 32, 32, 3) (128, 10)
 31 # tf.Tensor(0.0, shape=(), dtype=float32) tf.Tensor(1.0, shape=(), dtype=float32)
 32 # ----------------------
 33 
 34 
 35 # Net ------------------------
 36 class ResNet(Model):
 37     def __init__(self):
 38         super(ResNet, self).__init__()
 39         self.conv1 = Sequential([
 40             Conv2D(64, kernel_size=3, strides=1, padding='same', activation='relu')
 41         ])
 42         self.conv2 = Sequential([
 43             Conv2D(64, kernel_size=3, strides=1, padding='same', activation='relu'),
 44             BatchNormalization(),
 45             Conv2D(64, kernel_size=3, strides=1, padding='same', activation='relu'),
 46             BatchNormalization()
 47         ])
 48         self.conv3 = Sequential([
 49             Conv2D(64, kernel_size=3, strides=1, padding='same', activation='relu'),
 50             BatchNormalization(),
 51             Conv2D(64, kernel_size=3, strides=1, padding='same', activation='relu'),
 52             BatchNormalization()
 53         ])
 54         self.conv4 = Sequential([
 55             Conv2D(128, kernel_size=3, strides=2, padding='same', activation='relu'),
 56             BatchNormalization(),
 57             Conv2D(128, kernel_size=3, strides=1, padding='same', activation='relu'),
 58             BatchNormalization()
 59         ])
 60         self.conv5 = Sequential([
 61             Conv2D(128, kernel_size=3, strides=1, padding='same', activation='relu'),
 62             BatchNormalization(),
 63             Conv2D(128, kernel_size=3, strides=1, padding='same', activation='relu'),
 64             BatchNormalization()
 65         ])
 66         self.conv6 = Sequential([
 67             Conv2D(256, kernel_size=3, strides=2, padding='same', activation='relu'),
 68             BatchNormalization(),
 69             Conv2D(256, kernel_size=3, strides=1, padding='same', activation='relu'),
 70             BatchNormalization(),
 71         ])
 72         self.conv7 = Sequential([
 73             Conv2D(256, kernel_size=3, strides=1, padding='same', activation='relu'),
 74             BatchNormalization(),
 75             Conv2D(256, kernel_size=3, strides=1, padding='same', activation='relu'),
 76             BatchNormalization()
 77         ])
 78         self.conv8 = Sequential([
 79             Conv2D(512, kernel_size=3, strides=2, padding='same', activation='relu'),
 80             BatchNormalization(),
 81             Conv2D(512, kernel_size=3, strides=1, padding='same', activation='relu'),
 82             BatchNormalization()
 83         ])
 84         self.conv9 = Sequential([
 85             Conv2D(512, kernel_size=3, strides=1, padding='same', activation='relu'),
 86             BatchNormalization(),
 87             Conv2D(512, kernel_size=3, strides=1, padding='same', activation='relu'),
 88             BatchNormalization()
 89         ])
 90 
 91         self.avgPool = GlobalAveragePooling2D()
 92 
 93         self.fc10 = Dense(10)
 94 
 95         self.conv_128 = Conv2D(128, kernel_size=1, strides=2, padding='same', activation='relu')
 96         self.conv_256 = Conv2D(256, kernel_size=1, strides=2, padding='same', activation='relu')
 97         self.conv_512 = Conv2D(512, kernel_size=1, strides=2, padding='same', activation='relu')
 98 
 99     def call(self, inputs):
100         layer1 = self.conv1(inputs)
101         layer2 = self.conv2(layer1)
102         layer_one = add([layer1, layer2])
103 
104         layer3 = self.conv3(layer_one)
105         layer_two = add([layer_one, layer3])
106 
107         layer4 = self.conv4(layer_two)
108         layer4_1 = self.conv_128(layer_two)
109         layer_thi = add([layer4, layer4_1])
110 
111         layer5 = self.conv5(layer_thi)
112         layer6 = self.conv6(layer5)
113         layer6_1 = self.conv_256(layer5)
114         layer_fou = add([layer6, layer6_1])
115 
116         layer7 = self.conv7(layer_fou)
117         layer8 = self.conv8(layer7)
118         layer8_1 = self.conv_512(layer7)
119         layer_fiv = add([layer8, layer8_1])
120 
121         layer9 = self.conv9(layer_fiv)
122         layer9_1 = self.avgPool(layer9)
123         layer10 = self.fc10(layer9_1)
124 
125         return layer10
126 # --------------------------
127 
128 
129 def main():
130     model = ResNet()
131     model.build(input_shape=(None, 32, 32, 3))
132     model.summary()
133 
134     optimizer = tf.keras.optimizers.RMSprop(0.001)  # 创建优化器，指定学习率
135     criteon = losses.CategoricalCrossentropy(from_logits=True)
136     Epoch = 50
137     # 保存训练和测试过程中的误差情况
138     train_tot_loss = []
139     test_tot_loss = []
140 
141     for epoch in range(Epoch):
142         cor, tot = 0, 0
143         for step, (x, y) in enumerate(train_db):  # (128, 32, 32, 3), (128, 10)
144             with tf.GradientTape() as tape:  # 构建梯度环境
145                 # train
146                 out = model(x)  # (128, 10)
147 
148                 # calculate loss
149                 y = tf.cast(y, dtype=tf.float32)
150                 loss = criteon(y, out)
151 
152                 variables = model.trainable_variables
153                 grads = tape.gradient(loss, variables)
154                 optimizer.apply_gradients(zip(grads, variables))
155 
156                 # train var
157                 train_out = tf.nn.softmax(out, axis=1)
158                 train_out = tf.argmax(train_out, axis=1)
159                 train_out = tf.cast(train_out, dtype=tf.int64)
160 
161                 train_y = tf.nn.softmax(y, axis=1)
162                 train_y = tf.argmax(train_y, axis=1)
163 
164                 # calculate train var loss
165                 train_cor = tf.equal(train_y, train_out)
166                 train_cor = tf.cast(train_cor, dtype=tf.float32)
167                 train_cor = tf.reduce_sum(train_cor)
168                 cor += train_cor
169                 tot += x.shape[0]
170 
171         print('After %d Epoch' % epoch)
172         print('training acc is ', cor / tot)
173         train_tot_loss.append(cor / tot)
174 
175         correct, total = 0, 0
176         for x, y in test_db:
177             # test
178             pred = model(x)
179 
180             # test var
181             test_out = tf.nn.softmax(pred, axis=1)
182             test_out = tf.argmax(test_out, axis=1)
183             test_out = tf.cast(test_out, dtype=tf.int64)
184 
185             test_y = tf.nn.softmax(y, axis=1)
186             test_y = tf.argmax(test_y, axis=1)
187 
188             test_cor = tf.equal(test_y, test_out)
189             test_cor = tf.cast(test_cor, dtype=tf.float32)
190             test_cor = tf.reduce_sum(test_cor)
191             correct += test_cor
192             total += x.shape[0]
193 
194         print('testing acc is : ', correct / total)
195         test_tot_loss.append(correct / total)
196 
197     plt.figure()
198     plt.plot(train_tot_loss, 'b', label='train')
199     plt.plot(test_tot_loss, 'r', label='test')
200     plt.xlabel('Epoch')
201     plt.ylabel('ACC')
202     plt.legend()
203     # plt.savefig('exam8.3_train_test_CNN1.png')
204     plt.show()
205 
206 
207 if __name__ == "__main__":
208     main()