def test_back_prop_sigmoid(self):
layer = ActivationLayer("sigmoid")
layer.set_input_shape((4,))
input = np.array([0, 2365836, 0, -154366], dtype=np.float64)
layer.forward_prop(input)
expected_in_grad = np.array([0.25, 0, 0.25, 0], dtype=np.float64)
out_grad = np.ones(4)
in_grad = layer.back_prop(out_grad)
numpy.testing.assert_array_almost_equal(in_grad, expected_in_grad)
def test_back_prop_leaky_relu(self):
layer = ActivationLayer("leakyReLU")
layer.set_input_shape((4,))
input = np.array([1, -1, 1, -1], dtype=np.float64)
layer.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.back_prop(out_grad)
numpy.testing.assert_array_almost_equal(in_grad, expected_in_grad)
def test_forward_prop_relu(self):
layer = ActivationLayer("ReLU")
layer.set_input_shape((4,))
input = np.array([1536, -83741, 84597, -1543], dtype=np.float64)
expected_output = np.array([1536, 0, 84597, 0], dtype=np.float64)
output = layer.forward_prop(input)
numpy.testing.assert_array_equal(output, expected_output)
def test_forward_prop_sigmoid_small_input(self):
layer = ActivationLayer("sigmoid")
layer.set_input_shape((4,))
input = np.array([-427562359, -329756, -86, 0], dtype=np.float64)
expected_output = np.array([0, 0, 0, 0.5], dtype=np.float64)
output = layer.forward_prop(input)
numpy.testing.assert_array_almost_equal(output, expected_output)
def test_forward_prop_sigmoid(self):
layer = ActivationLayer("sigmoid")
layer.set_input_shape((4,))
input = np.array([0, 0, 0, 0], dtype=np.float64)
expected_output = np.array([0.5, 0.5, 0.5, 0.5], dtype=np.float64)
output = layer.forward_prop(input)
numpy.testing.assert_array_equal(output, expected_output)
def test_forward_prop_leaky_relu(self):
layer = ActivationLayer("leakyReLU")
layer.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.forward_prop(input)
numpy.testing.assert_array_almost_equal(output, expected_output)
