如何通过model.fit()提取LSTM模型的细胞状态

Question

我的LSTM模型是这样的，我想得到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)

但我无法从历史中提取state_c。如何退货？

Answer 1

我不确定你说的"；如何获得state_c〃；，因为您的LSTM层已经返回了带有标志CCD_ 3的CCD_。在这种情况下，我假设您正在尝试训练多输出模型。目前，您只有一个输出，但您的模型是用多个输出编译的。

以下是如何使用多输出模型。

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