Question

我正在尝试将嵌入层与数字要素层组合在一起。我确实喜欢：

tensor_feature = Input(shape=(MAX_LENGTH, 3))
tensor_embed = Input(shape=(MAX_LENGTH, ))
tensor_embed = Embedding(len(word2index), 128)(tensor_embed)

merged_tensor = concatenate([tensor_embed, tensor_feature]) 
model = Bidirectional(LSTM(256, return_sequences=True))(merged_tensor)
model = Bidirectional(LSTM(128, return_sequences=True))(model)
model = TimeDistributed(Dense(len(tag2index)))(model)
model = Activation('softmax')(model)
model = Model(inputs=[tensor_embed,tensor_feature],outputs=model)

注意到MAX_LENGTH是82。
不幸的是，我遇到了这样的错误：

ValueError：图断开：无法获取张量值 Tensor("input_2:0", shape=(?, 82), dtype=float32)位于“ input_2”层。可以顺利访问以下先前的图层：[]

同时组合输入和输出。请帮忙。

Answer 1

您正在覆盖tensor_embed，这是用于嵌入输出的输入层，并再次将其用作模型中的输入。将您的代码更改为

tensor_feature = Input(shape=(MAX_LENGTH, 3))
tensor_embed_feature = Input(shape=(MAX_LENGTH, ))
tensor_embed = Embedding(len(word2index), 128)(tensor_embed_feature)

merged_tensor = concatenate([tensor_embed, tensor_feature]) 
model = Bidirectional(LSTM(256, return_sequences=True))(merged_tensor)
model = Bidirectional(LSTM(128, return_sequences=True))(model)
model = TimeDistributed(Dense(len(tag2index)))(model)
model = Activation('softmax')(model)
model = Model(inputs=[tensor_embed_feature,tensor_feature],outputs=model)

如何在Keras中结合两层？一种具有嵌入功能，一种具有“其他”功能

1 个答案: