model.py 文件源码

def get_convo_nn2(no_word=200, n_gram=21, no_char=178):
    input1 = Input(shape=(n_gram,))
    input2 = Input(shape=(n_gram,))

    a = Embedding(no_char, 32, input_length=n_gram)(input1)
    a = SpatialDropout1D(0.15)(a)
    a = BatchNormalization()(a)

    a_concat = []
    for i in range(1,9):
        a_concat.append(conv_unit(a, n_gram, no_word, window = i))
    for i in range(9,12):
        a_concat.append(conv_unit(a, n_gram, no_word-50, window = i))
    a_concat.append(conv_unit(a, n_gram, no_word-100, window = 12))
    a_sum = Maximum()(a_concat)

    b = Embedding(12, 12, input_length=n_gram)(input2)
    b = SpatialDropout1D(0.15)(b)

    x = Concatenate(axis=-1)([a, a_sum, b])
    #x = Concatenate(axis=-1)([a_sum, b])
    x = BatchNormalization()(x)

    x = Flatten()(x)
    x = Dense(100, activation='relu')(x)
    out = Dense(1, activation='sigmoid')(x)

    model = Model(inputs=[input1, input2], outputs=out)
    model.compile(optimizer=Adam(),
                  loss='binary_crossentropy', metrics=['acc'])
    return model