efe5916109e220a429a2cff110edb952d747466f,tests/keras/layers/test_convolutional.py,,test_maxpooling_3d,#,255

Before Change

@pytest.mark.skipif(K._BACKEND != "theano", reason="Requires Theano backend")
def test_maxpooling_3d():
    nb_samples = 9
    stack_size = 7
    input_len_dim1 = 10
    input_len_dim2 = 11
    input_len_dim3 = 12
    pool_size = (3, 3, 3)

    input = np.ones((nb_samples, stack_size, input_len_dim1,
                     input_len_dim2, input_len_dim3))
    for strides in [(1, 1, 1), (2, 2, 2)]:
        layer = convolutional.MaxPooling3D(strides=strides,
                                           border_mode="valid",
                                           pool_size=pool_size)
        layer.input = K.variable(input)
        for train in [True, False]:
            K.eval(layer.get_output(train))
        layer.get_config()


@pytest.mark.skipif(K._BACKEND != "theano", reason="Requires Theano backend")
def test_averagepooling_3d():

After Change

    pool_size = (3, 3, 3)

    for strides in [(1, 1, 1), (2, 2, 2)]:
        layer_test(convolutional.MaxPooling3D,
                   kwargs={"strides": strides,
                           "border_mode": "valid",
                           "pool_size": pool_size},
                   input_shape=(3, 4, 11, 12, 10))


@pytest.mark.skipif(K._BACKEND != "theano", reason="Requires Theano backend")
def test_averagepooling_3d():

Instances