def test_forward_prop_leaky_relu(self):
layer_cuda = ActivationLayerCUDA("leakyReLU")
layer_cuda.set_input_shape((4,))
input = np.array([57287, -6154385, 14938, -54787], dtype=np.float64)
expected_output = np.array([57287, -61543.85, 14938, -547.87], dtype=np.float64)
output = layer_cuda.forward_prop(input)
numpy.testing.assert_array_almost_equal(output, expected_output)
def test_back_prop_leaky_relu(self):
layer_cuda = ActivationLayerCUDA("leakyReLU")
layer_cuda.set_input_shape((4,))
input = np.array([1, -1, 1, -1], dtype=np.float64)
layer_cuda.forward_prop(input)
out_grad = np.ones(4)
expected_in_grad = np.array([1, 0.01, 1, 0.01], dtype=np.float64)
in_grad = layer_cuda.back_prop(out_grad)
numpy.testing.assert_array_almost_equal(in_grad, expected_in_grad)
def test_get_output_shape(self):
layer_cuda = ActivationLayerCUDA("ReLU")
layer_cuda.set_input_shape((100,))
self.assertEqual(layer_cuda.get_output_shape(), (100,))