Inception10网络在python下TensorFlow2的实现

参考资料： 北京大学， 软微学院， 曹健老师， 《人工智能实践：TensorFlow2.0笔记》
运行环境：
python3.7
tensorflow 2.1.0
numpy 1.17.4
matplotlib 3.2.1

# inception10
import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, MaxPool2D, Dropout, Flatten, Dense,\
    GlobalAveragePooling2D
from tensorflow.keras import Model

np.set_printoptions(threshold=np.inf)

cifar10 = tf.keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0


class ConvBNRelu(Model):
    # 一个卷积，标准化，激活的模型
    def __init__(self, ch, kernelsz=3, strides=1, padding='same'):
        super(ConvBNRelu, self).__init__()
        self.model = tf.keras.models.Sequential([
            Conv2D(ch, kernelsz, strides=strides, padding=padding),
            BatchNormalization(),
            Activation('relu')
        ])

    def call(self, x):
        x = self.model(x, training=False)  # training=False, BN通过整个训练集计算均值、方差做归一化
        return x


# 一个inception块的定义
class InceptionBlk(Model):
    def __init__(self, ch, strides=1):
        super(InceptionBlk, self).__init__()
        self.ch = ch
        self.strides = strides
        self.c1 = ConvBNRelu(ch, kernelsz=1, strides=strides)

        self.c2_1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c2_2 = ConvBNRelu(ch, kernelsz=3, strides=1)

        self.c3_1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c3_2 = ConvBNRelu(ch, kernelsz=5, strides=1)

        self.p4_1 = MaxPool2D(3, strides=1, padding='same')
        self.c4_2 = ConvBNRelu(ch, kernelsz=1, strides=strides)

    def call(self, x):
        # *16
        x1 = self.c1(x)

        # *16
        x2_1 = self.c2_1(x)
        x2_2 = self.c2_2(x2_1)

        # *16
        x3_1 = self.c3_1(x)
        x3_2 = self.c3_2(x3_1)

        # *16
        x4_1 = self.p4_1(x)
        x4_2 = self.c4_2(x4_1)

        # 合并 1*32*32*64
        x = tf.concat([x1, x2_2, x3_2, x4_2], axis=3)
        return x


class Inception10(Model):
    def __init__(self, num_blocks, num_classes, init_ch=16, **kwargs):
        super(Inception10, self).__init__(**kwargs)
        # filters 数目 有两个block
        self.in_channels = init_ch
        self.out_channels = init_ch
        self.num_blocks = num_blocks
        self.init_ch = init_ch
        self.c1 = ConvBNRelu(init_ch)
        # 构造网络结构
        self.blocks = tf.keras.models.Sequential()

        for block_id in range(num_blocks):
            for layer_id in range(2):
                if layer_id == 0:
                    block = InceptionBlk(self.out_channels, strides=2)
                else:
                    block = InceptionBlk(self.out_channels, strides=1)
                self.blocks.add(block)

            # 扩大
            self.out_channels *=2
            # 第一块输出1*32*32*64  16*4
            # 第二块输出1*32*32*128 32*4
        self.p1 = GlobalAveragePooling2D()
        self.f1 = Dense(num_classes, activation='softmax')

    def call(self, x):

        x = self.c1(x)
        x = self.blocks(x)
        x = self.p1(x)
        y = self.f1(x)

        return y


# 分10类 2个块，每个块2个子块， 子块有4种
model = Inception10(num_blocks=2, num_classes=10)
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy']
              )

checkpoint_save_path = './checkpoint/Inception10.ckpt'
if os.path.exists(checkpoint_save_path + '.index'):
    print('-------------load the model-----------------')
    model.load_weights(checkpoint_save_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True)

history = model.fit(x_train, y_train, batch_size=128, epochs=5, validation_data=(x_test, y_test), validation_freq=1, callbacks=[cp_callback])
model.summary()

# print(model.trainable_variables)
file = open('./weights_inception10.txt', 'w')
for v in model.trainable_variables:
    file.write(str(v.name) + '\n')
    file.write(str(v.shape) + '\n')
    file.write(str(v.numpy()) + '\n')
file.close()

###############################################    show   ###############################################

# 显示训练集和验证集的acc和loss曲线
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()