如何在Keras中合并两个LSTM层

Question

我正在使用Keras处理句子相似性任务（使用STS数据集），但在合并层时出现问题。数据包括1184个句子对，每个得分在0到5之间。下面是我的numpy数组的形状。我已将每个句子填充为50个单词，并通过嵌入层运行它们，使用100维的glove嵌入。当合并两个网络时，我遇到了一个错误..

Exception: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 arrays but instead got the following list of 2 arrays:

这是我的代码样式：

total training data = 1184
X1.shape = (1184, 50)
X2.shape = (1184, 50)
Y.shape = (1184, 1)


embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
    embedding_vector = embeddings_index.get(word)
    if embedding_vector is not None:
        # words not found in embedding index will be all-zeros.
        embedding_matrix[i] = embedding_vector

embedding_layer = Embedding(len(word_index) + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=50,
                            trainable=False)

s1rnn = Sequential()
s1rnn.add(embedding_layer)
s1rnn.add(LSTM(128, input_shape=(100, 1)))
s1rnn.add(Dense(1))

s2rnn = Sequential()
s2rnn.add(embedding_layer)
s2rnn.add(LSTM(128, input_shape=(100, 1)))
s2rnn.add(Dense(1))

model = Sequential()
model.add(Merge([s1rnn,s2rnn],mode='concat'))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='RMSprop', metrics=['accuracy'])
model.fit([X1,X2], Y,batch_size=32, nb_epoch=100, validation_split=0.05)

- pchowdhry

Answer 1

问题不在于合并层。您需要创建两个嵌入层来输入2个不同的输入。

以下修改应该有效：

embedding_layer_1 = Embedding(len(word_index) + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=50,
                            trainable=False)

embedding_layer_2 = Embedding(len(word_index) + 1,
                            EMBEDDING_DIM,
                            weights=[embedding_matrix],
                            input_length=50,
                            trainable=False)


s1rnn = Sequential()
s1rnn.add(embedding_layer_1)
s1rnn.add(LSTM(128, input_shape=(100, 1)))
s1rnn.add(Dense(1))

s2rnn = Sequential()
s2rnn.add(embedding_layer_2)
s2rnn.add(LSTM(128, input_shape=(100, 1)))
s2rnn.add(Dense(1))

- indraforyou