[keras] 多GPU运行设置/固定权重

- 日理万妓 2022-05-13 04:22 151阅读 0赞

\#\#\#\#１.所需要的库  
`from keras.utils import multi_gpu_model`  
参考keras官方文档[multi-gpu][]  
`keras.utils.multi_gpu_model(model, gpus=None, cpu_merge=True, cpu_relocation=False)`

#### 2.模型包装 ####

model = Model(inputs=input_rgb,outputs=softmax)　＃定义好的模型
     parallel_model=multi_gpu_model(model,gpus=4)　　＃用multigpu函数包装一下

#### 3.自定义模型　继承callback类 ####

由于模型在fit的时候，用的是多gpu模型，直接调用自带的callback checkpoint函数来存储模型参数的时候，会报错。  
我们在定义模型函数的时候，可以返回两个模型，一个是原始模型，另一个是多GPU模型。  
多gpu模型用来训练，调用fit/fit\_generator函数，原始模型送入自定义callback保存.h5文件。

#模型定义函数
    return parallel_model,model
    #调用模型定义函数
    self.model,self.original_model = self.atten_lstm()
    #将原始模型传入自定义callback进行存储
    save_model=MyCbk(rm.original_model)
    
    class MyCbk(Callback):
        def __init__(self, model):
            self.model_to_save = model
    
        def on_epoch_end(self, epoch, logs=None):
            self.model_to_save.save('epoch_%d.h5' % epoch)

#### 4.继承Modelcheckpoint类 ####

class ParallelModelCheckpoint(ModelCheckpoint):
    
        def __init__(self,model,filepath, monitor='val_loss', verbose=0,
                     save_best_only=False, save_weights_only=False,
                     mode='auto', period=1):
            self.single_model = model
            super(ParallelModelCheckpoint,self).__init__(filepath, monitor, verbose,save_best_only, save_weights_only,mode, period)
    
        def set_model(self, model):
            super(ParallelModelCheckpoint,self).set_model(self.single_model)
    
    check_point = ParallelModelCheckpoint(single_model ,'best.hd5')

除了 ModelCheckpoint，其他的callback函数是可以正常调用的

tb = TensorBoard(log_dir=os.path.join('..','..','data', 'logs',dataset,model))
        early_stopper = EarlyStopping(patience=100)
        checkpointer = ModelCheckpoint(filepath=os.path.join('..','..','data', 'checkpoints', dataset,model,data_type +'.{epoch:03d}-{val_loss:.3f}-{val_acc:.3f}.hdf5'),
            verbose=1,
            save_best_only=True)

有个问题，自定义的callback函数，怎么传入val\_loss val\_acc?

#### 5.继承keras.callbacks.Callback ####

class CustomModelCheckpoint(Callback):
        def __init__(self, model, path):
            self.model = model
            self.path = path
            self.best_acc = 0
            print "init checkpoint saver"
    
        def on_epoch_end(self, epoch, logs):
            val_loss = logs['val_loss']
            val_acc =logs['val_acc']
            if val_acc > self.best_acc:
                print("\nValidation acc increased from {} to {}, saving model".format(self.best_acc, val_acc))
                self.model.save_weights(self.path+'/'+'acc_%.3f_loss_%.3f_epoch%d_.hdf5'%(val_acc,val_loss,epoch), overwrite=True)
                self.best_acc = val_acc
                print self.path+'/'+'acc_%.3f_loss_%.3f_epoch%d_.hdf5'%(val_acc,val_loss,epoch)
    
    
    model.fit(X_train, y_train,
                  batch_size=batch_size*G, epochs=nb_epoch, verbose=0, shuffle=True,
                  validation_data=(X_valid, y_valid),
                  callbacks=[CustomModelCheckpoint(model, '/path/to/save/model.h5')])

用这种方法的好处是可以知道val\_loss

### 固定某些层的权重 ###

for layer in model.layers[:-6]: #除了后6层，其他层的权重固定
                layer.trainable = False
    for layer in base_model.layers:  
        layer.trainable = False
       
     ## 可以直接传参数进去
     frozen_layer = Dense(32, trainable=False)
     ## 也可以初始化后再设置trainable =False，但是必须重新compile
    x = Input(shape=(32,))
    layer = Dense(32)
    layer.trainable = False
    y = layer(x)
    
    frozen_model = Model(x, y)
    # in the model below, the weights of `layer` will not be updated during training
    frozen_model.compile(optimizer='rmsprop', loss='mse')

参考  
[https://blog.csdn.net/u012862372/article/details/80367607][https_blog.csdn.net_u012862372_article_details_80367607]  
[https://blog.csdn.net/z5217632/article/details/80952372][https_blog.csdn.net_z5217632_article_details_80952372]

[multi-gpu]: https://keras.io/utils/#multi_gpu_model
[https_blog.csdn.net_u012862372_article_details_80367607]: https://blog.csdn.net/u012862372/article/details/80367607
[https_blog.csdn.net_z5217632_article_details_80952372]: https://blog.csdn.net/z5217632/article/details/80952372