"""定制resnet后面的層"""
def custom(input_size,num_classes,pretrain):
    # 引入初始化resnet50模型
    base_model = ResNet50(weights=pretrain,
                          include_top=False,
                          pooling=None,
                          input_shape=(input_size,input_size, 3),
                          classes=num_classes)
    #由于有預(yù)權(quán)重，前部分凍結(jié)，后面進(jìn)行遷移學(xué)習(xí)
    for layer in base_model.layers:
        layer.trainable = False
    #添加后面的層
    x = base_model.output
    x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
    x = layers.Dropout(0.5,name='dropout1')(x)
    #regularizers正則化層，正則化器允許在優(yōu)化過(guò)程中對(duì)層的參數(shù)或?qū)拥募せ钋闆r進(jìn)行懲罰
    #對(duì)損失函數(shù)進(jìn)行最小化的同時(shí)，也需要讓對(duì)參數(shù)添加限制，這個(gè)限制也就是正則化懲罰項(xiàng)，使用l2范數(shù)
    x = layers.Dense(512,activation='relu',kernel_regularizer= regularizers.l2(0.0001),name='fc2')(x)
    x = layers.BatchNormalization(name='bn_fc_01')(x)
    x = layers.Dropout(0.5,name='dropout2')(x)
    #40個(gè)分類
    x = layers.Dense(num_classes,activation='softmax')(x)
    model = Model(inputs=base_model.input,outputs=x)
    #模型編譯
    model.compile(optimizer="adam",loss = 'categorical_crossentropy',metrics=['accuracy'])
    return model

class Net():
    def __init__(self,img_size,gar_num,data_dir,batch_size,pretrain):
        self.img_size=img_size
        self.gar_num=gar_num
        self.data_dir=data_dir
        self.batch_size=batch_size
        self.pretrain=pretrain
    def build_train(self):
        """遷移學(xué)習(xí)"""
        model = resnet.custom(self.img_size, self.gar_num, self.pretrain)
        model.summary()
        train_sequence, validation_sequence = genit.gendata(self.data_dir, self.batch_size, self.gar_num, self.img_size)
        epochs=4
        model.fit_generator(train_sequence,steps_per_epoch=len(train_sequence),epochs=epochs,verbose=1,validation_data=validation_sequence,
                                     max_queue_size=10,shuffle=True)
        #微調(diào),在實(shí)際工程中，激活函數(shù)也被算進(jìn)層里，所以總共181層，微調(diào)是為了重新訓(xùn)練部分卷積層，同時(shí)訓(xùn)練最后的全連接層
        layers=149
        learning_rate=1e-4
        for layer in model.layers[:layers]:
            layer.trainable = False
        for layer in model.layers[layers:]:
            layer.trainable = True
        Adam =adam(lr=learning_rate, decay=0.0005)
        model.compile(optimizer=Adam, loss='categorical_crossentropy', metrics=['accuracy'])
        model.fit_generator(train_sequence,steps_per_epoch=len(train_sequence),epochs=epochs * 2,verbose=1,
            callbacks=[
                callbacks.ModelCheckpoint('./models/garclass.h5',monitor='val_loss', save_best_only=True, mode='min'),
                callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1,patience=10, mode='min'),
                callbacks.EarlyStopping(monitor='val_loss', patience=10),],
            validation_data=validation_sequence,max_queue_size=10,shuffle=True)
        print('finish train,look for garclass.h5')