如何从 Tensorflow Keras 中的堆叠 BiLSTM 层中获取隐藏状态矩阵？
How to get hidden state matrices out of stacked BiLSTM layer in Tensorflow Keras?
我正在尝试为此架构编写代码（问答模型：论文 https://www.hindawi.com/journals/cin/2019/9543490/）并寻求帮助如何从堆叠的 BiLSTM 层中获取隐藏状态矩阵 Hq 和 Ha。有没有大佬指教一下。
# Creating Embedding Layer for Query
# Considered fixed length as 40 for both question and answer as per research paper
embedding_layer1 = layers.Embedding(vocab_size_query, 300, weights=[embedding_matrix_query], input_length =40, trainable=False)
input_text1 =Input(shape=(40,), name="input_text")
x = embedding_layer1(input_text1)
# Creating Bidirectional layer for Query
# Each word in the context and question should be made aware of the nearby words occurring. We use a bi-directional recurrent neural network (LSTM’s) here.
x = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x)
x = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x)
flatten_1 = Flatten()(x)
## Creating Embedding Layer for Passage
embedding_layer2 = layers.Embedding(vocab_size_answer, 300, weights=[embedding_matrix_answer], input_length =40, trainable=False)
input_text2 =Input(shape=(40,), name="input_text")
x2 = embedding_layer2(input_text2)
# Creating Bidirectional layer for Passage
x2 = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x2)
x2 = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x2)
flatten_2 = Flatten()(x2)
根据模型结构和你的源代码，你可以通过提取flatten_1和flatten_2层的输出得到Hq和Ha。要提取中间层的输出，您可以创建一个新模型，将输入作为原始输入，并将输出作为适当的层。
from tensorflow.keras.models import Model
model = ... # create the original model
layer_name = 'my_layer'
intermediate_layer_model = Model(inputs=model.input,
outputs=model.get_layer(layer_name).output)
intermediate_output = intermediate_layer_model.predict(data)
我正在尝试为此架构编写代码（问答模型：论文 https://www.hindawi.com/journals/cin/2019/9543490/）并寻求帮助如何从堆叠的 BiLSTM 层中获取隐藏状态矩阵 Hq 和 Ha。有没有大佬指教一下。
# Creating Embedding Layer for Query
# Considered fixed length as 40 for both question and answer as per research paper
embedding_layer1 = layers.Embedding(vocab_size_query, 300, weights=[embedding_matrix_query], input_length =40, trainable=False)
input_text1 =Input(shape=(40,), name="input_text")
x = embedding_layer1(input_text1)
# Creating Bidirectional layer for Query
# Each word in the context and question should be made aware of the nearby words occurring. We use a bi-directional recurrent neural network (LSTM’s) here.
x = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x)
x = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x)
flatten_1 = Flatten()(x)
## Creating Embedding Layer for Passage
embedding_layer2 = layers.Embedding(vocab_size_answer, 300, weights=[embedding_matrix_answer], input_length =40, trainable=False)
input_text2 =Input(shape=(40,), name="input_text")
x2 = embedding_layer2(input_text2)
# Creating Bidirectional layer for Passage
x2 = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x2)
x2 = Bidirectional(LSTM(128,recurrent_dropout=0.5,kernel_regularizer=regularizers.l2(0.001),return_sequences=True))(x2)
flatten_2 = Flatten()(x2)
根据模型结构和你的源代码，你可以通过提取flatten_1和flatten_2层的输出得到Hq和Ha。要提取中间层的输出，您可以创建一个新模型，将输入作为原始输入，并将输出作为适当的层。
from tensorflow.keras.models import Model
model = ... # create the original model
layer_name = 'my_layer'
intermediate_layer_model = Model(inputs=model.input,
outputs=model.get_layer(layer_name).output)
intermediate_output = intermediate_layer_model.predict(data)