keras--多GPU訓練

		from keras.callbacks import TensorBoard, ModelCheckpoint
		from keras.utils import multi_gpu_model # 導入keras多卡函數

		class ParallelModelCheckpoints(ModelCheckpoint): # 在保存模型時,由於存在兩個模型,所以需要指定model,\
														# 繼承ModelCheckpoint,重寫init()
		    def __init__(self, 
		    			 model, # 需要保存的模型
		                 filepath='./log/epoch-{epoch:02d}_loss-{loss:.4f}_acc-{val_acc:.4f}_lr-{lr:.5f}.h5',
		                 monitor='val_acc',
		                 verbose=1,
		                 save_best_only=True,
		                 save_weights_only=False,
		                 mode='auto',
		                 period=1):
		        self.single_model = model
		        super(ParallelModelCheckpoints, self).__init__(filepath, monitor, verbose,save_best_only, save_weights_only, mode, period)
		
		    def set_model(self, model):
		        super(ParallelModelCheckpoints, self).set_model(self.single_model)

		# 首先在cpu上創建原來的模型
        with tf.device('/cpu:0'):
            model = MobileNet(...)
        # 創建多卡模型
        parallel_model = multi_gpu_model(model, gpus=4) # 其中 4 是gpu的數量
        parallel_model.load_weights(h5_path, by_name=True) # 繼續訓練的時候導入參數是用的parallel_model模型,而不是model
        parallel_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
        
        model_checkpoint = ParallelModelCheckpoints(model) # 設置需要保存h5的模型

        print("Start training the model")  
        # 然後就可以訓練了
        training_history = parallel_model.fit_generator(
            train_generator,
            steps_per_epoch=step_size_train,
            validation_data=validation_generator,
            validation_steps=step_size_valid,
            epochs=epoch_list[-1],
            verbose=1,
            callbacks=[TensorBoard(log_dir='./tb'), model_checkpoint, stepDecayLR])
        print("Model training finished")