如何通过model.fit()提取LSTM模型的细胞状态?

问题描述

我的LSTM模型就是这样，我想获得state_c

My LSTM model is like this, and I would like to get state_c

def _get_model(input_shape, latent_dim, num_classes):

  inputs = Input(shape=input_shape)
  lstm_lyr,state_h,state_c = LSTM(latent_dim,dropout=0.1,return_state = True)(inputs)
  fc_lyr = Dense(num_classes)(lstm_lyr)
  soft_lyr = Activation('relu')(fc_lyr)
  model = Model(inputs, [soft_lyr,state_c])
  model.compile(optimizer='adam', loss='mse', metrics=['accuracy'])
return model
model =_get_model((n_steps_in, n_features),latent_dim ,n_steps_out)
history = model.fit(X_train,Y_train)

但是我无法从历史记录中提取state_c.如何退还?

But I canot extract the state_c from the history. How to return that?

推荐答案

我不确定如何获取state_c"的含义，因为您的LSTM层已经在返回带有标志return_state=True的state_c. .我假设您正在尝试在这种情况下训练多输出模型.当前，您只有一个输出，但是您的模型是由多个输出编译的.

I am unsure of what you mean by "How to get state_c", because your LSTM layer is already returning the state_c with the flag return_state=True. I assume you are trying to train the multi-output model in this case. Currently, you only have a single output but your model is compiled with multiple outputs.

这是您使用多输出模型的方式.

Here is how you work with multi-output models.

from tensorflow.keras import layers, Model, utils

def _get_model(input_shape, latent_dim, num_classes):
    inputs = layers.Input(shape=input_shape)
    lstm_lyr,state_h,state_c = layers.LSTM(latent_dim,dropout=0.1,return_state = True)(inputs)
    fc_lyr = layers.Dense(num_classes)(lstm_lyr)
    soft_lyr = layers.Activation('relu')(fc_lyr)
    model = Model(inputs, [soft_lyr,state_c])   #<------- One input, 2 outputs
    model.compile(optimizer='adam', loss='mse')
    return model


#Dummy data
X = np.random.random((100,15,5))
y1 = np.random.random((100,4))
y2 = np.random.random((100,7))

model =_get_model((15, 5), 7 , 4)
model.fit(X, [y1,y2], epochs=4) #<--------- #One input, 2 outputs

Epoch 1/4
4/4 [==============================] - 2s 6ms/step - loss: 0.6978 - activation_9_loss: 0.2388 - lstm_9_loss: 0.4591
Epoch 2/4
4/4 [==============================] - 0s 6ms/step - loss: 0.6615 - activation_9_loss: 0.2367 - lstm_9_loss: 0.4248
Epoch 3/4
4/4 [==============================] - 0s 7ms/step - loss: 0.6349 - activation_9_loss: 0.2392 - lstm_9_loss: 0.3957
Epoch 4/4
4/4 [==============================] - 0s 8ms/step - loss: 0.6053 - activation_9_loss: 0.2392 - lstm_9_loss: 0.3661

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