maskedreshape.py 文件源码

def create_two_level_bi_lstm(input_4d, embedding_layer,
                                 max_sentence_length, max_n_analyses, max_word_root_length,
                                 lstm_dim, embedding_dim):
        r = Reshape((max_sentence_length * max_n_analyses * max_word_root_length,))
        # input_4d = Lambda(lambda x: x, output_shape=lambda s: s)(input_4d)
        rr = r(input_4d)
        input_embeddings = embedding_layer(rr)
        print input_embeddings
        r = MaskedReshape(
            (max_sentence_length * max_n_analyses, max_word_root_length, embedding_dim),
            (max_sentence_length * max_n_analyses, max_word_root_length))
        # input_embeddings = Lambda(lambda x: x, output_shape=lambda s: s)(input_embeddings)
        rr = r(input_embeddings)
        lstm_layer = Bidirectional(LSTM(lstm_dim,
                                        input_shape=(max_word_root_length, embedding_dim)))
        td_lstm_layer = TimeDistributed(lstm_layer,
                                        input_shape=(max_word_root_length, embedding_dim))

        lstm_layer_output = td_lstm_layer(rr)
        lstm_layer_output_relu = Activation('relu')(lstm_layer_output)
        print "lstm_layer_output_relu", lstm_layer_output_relu
        r = Reshape((max_sentence_length, max_n_analyses, 2 * lstm_dim))
        lstm_layer_output_relu = Lambda(lambda x: x, output_shape=lambda s: s)(lstm_layer_output_relu)
        lstm_layer_output_relu_reshaped = r(lstm_layer_output_relu)
        print "lstm_layer_output_relu_reshaped", lstm_layer_output_relu_reshaped
        return input_embeddings, lstm_layer_output_relu_reshaped