Problem:我有S个序列,每个序列包含T个时间步,每个时间步包含F个特征,所以总的来说,一个
Goal:使用LSTMs对架构进行建模/训练,以学习/实现函数逼近器模型M,并给出序列s,以预测Target_1和Target_2?
比如:
M(s) ~ (Target_1, Target_2)
我真的很难找到一种方法,下面是Keras实现的一个例子,可能不起作用.我制作了两个模型,一个用于第一个目标值,一个用于第二个目标值.
model1 = Sequential()
model1.add(Masking(mask_value=-10.0))
model1.add(LSTM(1, input_shape=(batch, timesteps, features), return_sequences = True))
model1.add(Flatten())
model1.add(Dense(hidden_units, activation = "relu"))
model1.add(Dense(1, activation = "linear"))
model1.compile(loss='mse', optimizer=Adam(learning_rate=0.0001))
model1.fit(x_train, y_train[:,0], validation_data=(x_test, y_test[:,0]), epochs=epochs, batch_size=batch, shuffle=False)
model2 = Sequential()
model2.add(Masking(mask_value=-10.0))
model2.add(LSTM(1, input_shape=(batch, timesteps, features), return_sequences=True))
model2.add(Flatten())
model2.add(Dense(hidden_units, activation = "relu"))
model2.add(Dense(1, activation = "linear"))
model2.compile(loss='mse', optimizer=Adam(learning_rate=0.0001))
model2.fit(x_train, y_train[:,1], validation_data=(x_test, y_test[:,1]), epochs=epochs, batch_size=batch, shuffle=False)
我想以某种方式充分利用LSTMs时间相关记忆,以实现良好的回归.