test_core.py 文件源码

def test_merge_mask_2d():
    from keras.layers import Input, merge, Masking
    from keras.models import Model

    rand = lambda *shape: np.asarray(np.random.random(shape) > 0.5, dtype='int32')

    # inputs
    input_a = Input(shape=(3,))
    input_b = Input(shape=(3,))

    # masks
    masked_a = Masking(mask_value=0)(input_a)
    masked_b = Masking(mask_value=0)(input_b)

    # three different types of merging
    merged_sum = merge([masked_a, masked_b], mode='sum')
    merged_concat = merge([masked_a, masked_b], mode='concat', concat_axis=1)
    merged_concat_mixed = merge([masked_a, input_b], mode='concat', concat_axis=1)

    # test sum
    model_sum = Model([input_a, input_b], [merged_sum])
    model_sum.compile(loss='mse', optimizer='sgd')
    model_sum.fit([rand(2, 3), rand(2, 3)], [rand(2, 3)], nb_epoch=1)

    # test concatenation
    model_concat = Model([input_a, input_b], [merged_concat])
    model_concat.compile(loss='mse', optimizer='sgd')
    model_concat.fit([rand(2, 3), rand(2, 3)], [rand(2, 6)], nb_epoch=1)

    # test concatenation with masked and non-masked inputs
    model_concat = Model([input_a, input_b], [merged_concat_mixed])
    model_concat.compile(loss='mse', optimizer='sgd')
    model_concat.fit([rand(2, 3), rand(2, 3)], [rand(2, 6)], nb_epoch=1)