def test_multi_backward(self):
x = step19.Variable(np.array(2.0))
a = step19.square(x)
y = step19.add(step19.square(a), step19.square(a))
y.backward()
assert y.data == 32
assert x.grad == 64
def test_backward(self):
x = step19.Variable(np.random.rand(1))
y = step19.square(x)
y.backward()
num_grad = step19.numerical_diff(step19.square, x)
flg = np.allclose(x.grad, num_grad)
assert flg
class TestVariable:
def test_name(self):
x = step19.Variable(np.array(1), "test")
assert x.name == "test"
@pytest.mark.parametrize("input, expected", [(step19.Variable(np.array(1)), 'int32'), (step19.Variable(np.array([1.0, 2.0, 3.0])), 'float64'), (step19.Variable(np.array([[1.0, 2.0], [2.0, 3.0]])), 'float64')])
def test_dtype(self, input, expected):
assert input.dtype == expected
@pytest.mark.parametrize("input, expected", [(step19.Variable(np.array(1.0)), 0), (step19.Variable(np.array([1.0, 2.0, 3.0])), 1), (step19.Variable(np.array([[1.0, 2.0], [2.0, 3.0]])), 2)])
def test_ndim(self, input, expected):
assert input.ndim == expected
@pytest.mark.parametrize("input, expected", [(step19.Variable(np.array(1.0)), 1), (step19.Variable(np.array([1.0, 2.0, 3.0])), 3), (step19.Variable(np.array([[1.0, 2.0], [2.0, 3.0]])), 4)])
def test_size(self, input, expected):
assert input.size == expected
@pytest.mark.parametrize("input, expected", [(step19.Variable(np.array(1.0)), 1), (step19.Variable(np.array([1.0, 2.0, 3.0])), 3), (step19.Variable(np.array([[1.0, 2.0], [2.0, 3.0]])), 2)])
def test_len(self, input, expected):
assert len(input) == expected
@pytest.mark.parametrize("input, expected", [(step19.Variable(np.array(1.0)), ()), (step19.Variable(np.array([1.0, 2.0, 3.0])), (3,)), (step19.Variable(np.array([[1.0, 2.0], [2.0, 3.0]])), (2, 2))])
def test_shape(self, input, expected):
assert input.shape == expected
@pytest.mark.parametrize("input, expected", [(step19.Variable(np.array(1.0)), "variable(1.0)\n"), (step19.Variable(np.array([1.0, 2.0, 3.0])), "variable([1. 2. 3.])\n"), (step19.Variable(np.array([[1.0, 2.0], [2.0, 3.0]])), "variable([[1. 2.]\n [2. 3.]])\n")])
def test_shape(self, input, expected, capsys):
print(input)
captured = capsys.readouterr()
assert captured.out == expected
def test_name(self):
x = step19.Variable(np.array(1), "test")
assert x.name == "test"
def test_forward(self):
x = step19.Variable(np.array(2.0))
y = step19.square(x)
assert y.data == 4.0
def test_add(self):
x0 = step19.Variable(np.array(2))
x1 = step19.Variable(np.array(3))
assert step19.add(x0, x1).data == 5