mr_cnn_rnn.py 文件源码

def build_model():
        print('Build model...%d of %d' % (i + 1, folds))
        main_input = Input(shape=(maxlen, ), dtype='int32', name='main_input')
        embedding  = Embedding(max_features, embedding_dims,
                      weights=[np.matrix(W)], input_length=maxlen,
                      name='embedding')(main_input)

        embedding = Dropout(0.50)(embedding)


        conv4 = Convolution1D(nb_filter=nb_filter,
                              filter_length=4,
                              border_mode='valid',
                              activation='relu',
                              subsample_length=1,
                              name='conv4')(embedding)
        maxConv4 = MaxPooling1D(pool_length=2,
                                 name='maxConv4')(conv4)

        conv5 = Convolution1D(nb_filter=nb_filter,
                              filter_length=5,
                              border_mode='valid',
                              activation='relu',
                              subsample_length=1,
                              name='conv5')(embedding)
        maxConv5 = MaxPooling1D(pool_length=2,
                                name='maxConv5')(conv5)

        x = merge([maxConv4, maxConv5], mode='concat')

        x = Dropout(0.15)(x)

        x = RNN(rnn_output_size)(x)

        x = Dense(hidden_dims, activation='relu', init='he_normal',
                  W_constraint = maxnorm(3), b_constraint=maxnorm(3),
                  name='mlp')(x)

        x = Dropout(0.10, name='drop')(x)

        output = Dense(1, init='he_normal',
                       activation='sigmoid', name='output')(x)

        model = Model(input=main_input, output=output)
        model.compile(loss={'output':'binary_crossentropy'},
                    optimizer=Adadelta(lr=0.95, epsilon=1e-06),
                    metrics=["accuracy"])
        return model