unimodal_nodelta.py 文件源码

def test_concatlayer():
    a = np.array([
        [
            [1, 2, 3, 4],
            [1, 2, 3, 4],
            [1, 2, 3, 4]
        ],
        [
            [1, 2, 3, 4],
            [1, 2, 3, 4],
            [1, 2, 3, 4]
        ]
    ], dtype=np.int32)
    b = np.array([
        [
            [5, 6, 7],
            [5, 6, 7],
            [5, 6, 7]
        ],
        [
            [5, 6, 7],
            [5, 6, 7],
            [5, 6, 7]
        ]
    ], dtype=np.int32)

    input_var = T.tensor3('input', dtype='int32')
    dct_var = T.tensor3('dct', dtype='int32')
    l_in = InputLayer((None, None, 4), input_var, name='input')
    l_dct = InputLayer((None, None, 3), dct_var, name='dct')
    l_merge = ConcatLayer([l_in, l_dct], axis=2, name='merge')
    network = las.layers.get_all_layers(l_merge)
    print_network(network)
    output = las.layers.get_output(l_merge)
    merge_fn = theano.function([input_var, dct_var], output, allow_input_downcast=True)
    res = merge_fn(a, b)
    assert res.shape == (2, 3, 7)