def test_alpha_init(self, ParametricRectifierLayer, init_alpha):
input_shape = (None, 3, 28, 28)
# default: alphas only over 2nd axis
layer = ParametricRectifierLayer(input_shape, alpha=init_alpha)
alpha = layer.alpha
assert layer.shared_axes == (0, 2, 3)
assert alpha.get_value().shape == (3, )
assert np.allclose(alpha.get_value(), init_alpha((3, )))
# scalar alpha
layer = ParametricRectifierLayer(input_shape,
alpha=init_alpha,
shared_axes='all')
alpha = layer.alpha
assert layer.shared_axes == (0, 1, 2, 3)
assert alpha.get_value().shape == ()
assert np.allclose(alpha.get_value(), init_alpha((1, )))
# alphas shared over the 1st axis
layer = ParametricRectifierLayer(input_shape,
alpha=init_alpha,
shared_axes=0)
alpha = layer.alpha
assert layer.shared_axes == (0, )
assert alpha.get_value().shape == (3, 28, 28)
assert np.allclose(alpha.get_value(), init_alpha((3, 28, 28)))
# alphas shared over the 1st and 4th axes
layer = ParametricRectifierLayer(input_shape,
alpha=init_alpha,
shared_axes=(0, 3))
alpha = layer.alpha
assert layer.shared_axes == (0, 3)
assert alpha.get_value().shape == (3, 28)
assert np.allclose(alpha.get_value(), init_alpha((3, 28)))
def test_get_output_for(self, ParametricRectifierLayer, init_alpha):
input_shape = (3, 3, 28, 28)
# random input tensor
input = np.random.randn(*input_shape).astype(theano.config.floatX)
# default: alphas shared only along 2nd axis
layer = ParametricRectifierLayer(input_shape, alpha=init_alpha)
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * \
alpha_v[None, :, None, None]
assert np.allclose(layer.get_output_for(input).eval(), expected)
# scalar alpha
layer = ParametricRectifierLayer(input_shape, alpha=init_alpha,
shared_axes='all')
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * alpha_v
assert np.allclose(layer.get_output_for(input).eval(), expected)
# alphas shared over the 1st axis
layer = ParametricRectifierLayer(input_shape, alpha=init_alpha,
shared_axes=0)
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * \
alpha_v[None, :, :, :]
assert np.allclose(layer.get_output_for(input).eval(), expected)
# alphas shared over the 1st and 4th axes
layer = ParametricRectifierLayer(input_shape, shared_axes=(0, 3),
alpha=init_alpha)
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * \
alpha_v[None, :, :, None]
assert np.allclose(layer.get_output_for(input).eval(), expected)
def test_get_output_for(self, ParametricRectifierLayer, init_alpha):
input_shape = (3, 3, 28, 28)
# random input tensor
input = np.random.randn(*input_shape).astype(theano.config.floatX)
# default: alphas shared only along 2nd axis
layer = ParametricRectifierLayer(input_shape, alpha=init_alpha)
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * \
alpha_v[None, :, None, None]
assert np.allclose(layer.get_output_for(input).eval(), expected)
# scalar alpha
layer = ParametricRectifierLayer(input_shape,
alpha=init_alpha,
shared_axes='all')
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * alpha_v
assert np.allclose(layer.get_output_for(input).eval(), expected)
# alphas shared over the 1st axis
layer = ParametricRectifierLayer(input_shape,
alpha=init_alpha,
shared_axes=0)
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * \
alpha_v[None, :, :, :]
assert np.allclose(layer.get_output_for(input).eval(), expected)
# alphas shared over the 1st and 4th axes
layer = ParametricRectifierLayer(input_shape,
shared_axes=(0, 3),
alpha=init_alpha)
alpha_v = layer.alpha.get_value()
expected = np.maximum(input, 0) + np.minimum(input, 0) * \
alpha_v[None, :, :, None]
assert np.allclose(layer.get_output_for(input).eval(), expected)
def test_undefined_shape(self, ParametricRectifierLayer):
with pytest.raises(ValueError):
ParametricRectifierLayer((None, 3, 28, 28), shared_axes=(1, 2, 3))
