text_model.py 文件源码

def QuestionWithAnswersModel(embed_matrix, embed_input, sequence_length, ans_cnt, keywords_length, filter_sizes, num_filters, dropout_prob, hidden_dims, embedding_dim=300):
    ''' path1: question embedding (CNN model)
        path2: answer embeddin(Hierachical RNN model)
        merge
    '''
    # path 1
    embed1 = Embedding(embed_input, embedding_dim,input_length=sequence_length, weights=[embed_matrix])
    question_branch = Sequential()
    cnn_model = TextCNN(sequence_length, embedding_dim, filter_sizes, num_filters)
    question_branch.add(embed1)
    question_branch.add(cnn_model)
    # path 2
    answer_branch = HierarchicalRNN(embed_matrix, embed_input, ans_cnt, keywords_length, embedding_dim)
    merged = Merge([question_branch, answer_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'))
    final_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
    return final_model
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