自学教程：深度学习小工程练习之tensorflow垃圾分类详解

"""定制resnet后面的层"""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)    #由于有预权重，前部分冻结，后面进行迁移学习    for layer in base_model.layers:        layer.trainable = False    #添加后面的层    x = base_model.output    x = layers.GlobalAveragePooling2D(name='avg_pool')(x)    x = layers.Dropout(0.5,name='dropout1')(x)    #regularizers正则化层，正则化器允许在优化过程中对层的参数或层的激活情况进行惩罚    #对损失函数进行最小化的同时，也需要让对参数添加限制，这个限制也就是正则化惩罚项，使用l2范数    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个分类    x = layers.Dense(num_classes,activation='softmax')(x)    model = Model(inputs=base_model.input,outputs=x)    #模型编译    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):        """迁移学习"""        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)        #微调,在实际工程中，激活函数也被算进层里，所以总共181层，微调是为了重新训练部分卷积层，同时训练最后的全连接层        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')