如何在Keras中合并两个LSTM层 [英] How to merge two LSTM layers in Keras

问题描述

我正在与Keras一起进行句子相似性任务(使用STS数据集)，但是在合并图层时遇到了问题.数据由1184个句子对组成，每个句子对的得分介于0到5之间.以下是我的numpy数组的形状.我使用100个尺寸的手套嵌入程序，将每个句子填充到50个单词，然后贯穿并嵌入图层.合并两个网络时出现错误.

I’m working with Keras on a sentence similarity task (using the STS dataset) and am having problems merging the layers. The data consists of 1184 sentence pairs each scored between 0 and 5. Below are the shapes of my numpy arrays. I’ve padded each of the sentences to 50 words and run them through and embedding layer, using the glove embedding’s with 100 dimensions. When merging the two networks I'm getting an error..

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:

这是我的代码的样子

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)

推荐答案

问题不在于合并层.您需要创建两个嵌入层以提供2个不同的输入.

The problem is not with the merge layer. You need to create two embedding layers to feed in 2 different inputs.

以下修改应起作用:

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))

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