def test_tanh(self) -> None:
np.random.seed(0)
t1 = Tensor(np.ones(shape=(3, 3, 3)) * 1_000, requires_grad=True)
t2 = tanh(t1)
assert t2.shape == (3, 3, 3)
np.testing.assert_allclose(t2.data, 1)
def test_tanh_grad(self) -> None:
np.random.seed(0)
t1 = Tensor(np.ones(shape=(3, 3, 3)) * 1_000, requires_grad=True)
t2 = tanh(t1)
t2.backward(1)
np.testing.assert_allclose(t1.grad, 0)
def test_tanh_no_grad(self) -> None:
t1 = Tensor(np.zeros(shape=(3, 3, 3)), requires_grad=False)
t2 = tanh(t1)
assert t2.shape == (3, 3, 3)
assert not t2.requires_grad
def predict(self, x: Tensor) -> Tensor:
x = x @ self.layer1 + self.bias1
x = tanh(x)
x = x @ self.layer2 + self.bias2
return x