def test_output_error_passed_back_in_the_chain_matches_input_dim(self):
x, err, lr = np.array([1, 2, 3]), np.array([
0.5,
]), 3
layer = Output(x)
layer.solve_bwd(err, lr)
self.assertEqual(layer.e.shape, layer.x.shape)
def test_output_gradient_descent(self):
x, err, lr = np.array([1, 2, 3]), np.array([
0.5,
]), 3
layer = Output(x)
layer.solve_bwd(err, lr)
expected = -lr * np.repeat(err, x.shape[0]) * layer.partial_s * layer.x
self.assertTrue((expected == layer.delta_w).all())
def test_output_acc_error_to_be_passed_back_in_the_chain(self):
x, err, lr = np.array([1, 2, 3]), np.array([
0.5,
]), 3
layer = Output(x)
layer.solve_bwd(err, lr)
expected = np.repeat(err, x.shape[0]) * layer.partial_s * layer.w
self.assertTrue((expected == layer.e).all())
def test_output_partial_s(self):
x = np.array([1, 2, 3])
layer = Output(x)
layer.solve_bwd(np.array([
0.5,
]))
partial_s = layer.s * (1 - layer.s)
self.assertTrue((layer.partial_s == partial_s).all())
self.assertEqual(layer.partial_s.shape, x.shape)
def test_output_solve_bwd_check_args_shape(self):
layer = Output(np.array([1, 2]))
regex = 'The shape for net error should be 1'
with self.assertRaisesRegex(ValueError, regex):
layer.solve_bwd(np.array([1, 2]))
def test_output_value(self):
layer = Output(np.array([1, 2, 3]))
expected = 1 / (1 + np.exp(-layer.z))
self.assertTrue((layer.s == expected).all())
def test_output_solve_bwd_check_args_np(self):
layer = Output(np.array([1, 2]))
regex = 'The network error should be a numpy array'
with self.assertRaisesRegex(TypeError, regex):
layer.solve_bwd('void')
def test_output_for_output_layer_is_one(self):
layer = Output(np.array([1, 2, 3]))
self.assertEqual(layer.s.size, 1)
def test_z_is_a_dot_product_between_the_input_and_weight(self):
i = np.array([1, 2, 3])
layer = Output(i)
self.assertTrue((layer.z == np.dot(i, layer.w)).all())
def test_layer_x_is_input(self):
i = np.array([1, 2, 3])
layer = Output(i)
self.assertTrue((layer.x == i).all())
def test_output_requires_an_arg(self):
with self.assertRaises(TypeError):
Output()
def test_output_is_a_Layer_subclass(self):
self.assertIsInstance(Output(np.array([1, 2])), Layer)