Question

def LstmSingle(name, train, vaild, test, idx, epoch=500, layer_size=32,time_stap=10,learning_rate=5e-3, batch_size=8):
    idx_text = ["news","sns","sq"]
    wgh_path = "./weight/" + name + "_" + idx_text[idx] + ".wgh"
    
    K.clear_session()
    model = Sequential()
    model.add(LSTM(layer_size, input_shape = (time_stap, 2) , return_sequences = True))
    model.add(LSTM(layer_size, input_shape = (time_stap, 2)))
    model.add(Dense(layer_size, activation='relu'))
    model.add(Dense(layer_size, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='mse', optimizer=Adam(learning_rate=learning_rate))
    
    history = None
    if not os.path.isfile(wgh_path):
        checkpoint = ModelCheckpoint(filepath=wgh_path, monitor='val_loss', verbose=0, save_best_only=True)
        history = model.fit(
            train["x"][idx],
            train["y"], 
            validation_data = (vaild["x"][idx], vaild["y"]), 
            epochs=epoch, 
            batch_size=batch_size, 
            verbose=0, 
            callbacks=[checkpoint]
        )

        #garbege collect
        del checkpoint
        gc.collect()
    #end if

    model.load_weights(wgh_path)

    #Print learning histroy
    if history != None:
        plt.xlabel('Epoch')
        plt.ylabel('Loss')
        plt.title('Learning progress')
        plt.plot(history.history['loss'] , color="black", label="loss")
        plt.plot(history.history['val_loss'] , color="blue", label="val_loss")
        plt.legend()
        plt.show()
    #end if
    
    predict_train = model.predict(train["x"][idx])
    predict_vaild = model.predict(vaild["x"][idx])
    predict_test = model.predict(test["x"][idx])
    K.clear_session()
    model.reset_states()
    
    #garbege collect
    del model
    gc.collect()
    
    return {
        "train" : predict_train, 
        "vaild" : predict_vaild, 
        "test" : predict_test
    }
#end def

我训练了上面的模型，但是当我训练下一个训练数据时，先前使用的内存仍然没有初始化。

根据任务管理器。我的模型内存使用量最大为10.3GB。当我拟合模型时，内存使用量正在增加。拟合模型后的内存仍然不会减少。

我该如何解决？

训练lstm模型后，内存未初始化

0 个答案: