text_model.py 文件源码

def CNNWithKeywordLayer(embed_matrix, embed_input, sequence_length, keywords_length, filter_sizes, num_filters, dropout_prob, hidden_dims, model_variation, embedding_dim=300):
    ''' 2-way input model: left is cnn for sentence embedding while right is keywords

    '''
    embed1 = Embedding(embed_input, embedding_dim,input_length=sequence_length, weights=[embed_matrix])
    # 1. question model part
    question_branch = Sequential()
    cnn_model = TextCNN(sequence_length, embedding_dim, filter_sizes, num_filters)
    question_branch.add(embed1)
    question_branch.add(cnn_model)
    # 2. keyword model part
    #keyword_branch = KeywordLayer(keywords_length, embed_input, embedding_dim, embed_matrix)
    keyword_branch = LSTMLayer(embed_matrix, embed_input, keywords_length, dropout_prob, hidden_dims, embedding_dim)
    # 3. merge layer
    merged = Merge([question_branch, keyword_branch], mode='concat')
    final_model = Sequential()
    final_model.add(merged)
    final_model.add(Dense(hidden_dims, W_constraint = maxnorm(3)))
    final_model.add(Dropout(0.5))
    final_model.add(Activation('relu'))
    final_model.add(Dense(1))
    final_model.add(Activation('sigmoid'))
    #sgd = SGD(lr=0.01, momentum=0.9)
    final_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
    return final_model