def test_forward_case_2(self):
layer = TanhLayer()
layer.initialize_parameters()
x = np.array([[-3.0, 4.0]])
actual = layer.forward(x)
x_torch = self.numpy_to_torch(x)
expect_torch = torch.tanh(x_torch)
expect = self.torch_to_numpy(expect_torch)
self.assertEquals(expect.shape, actual.shape)
self.assertClose(expect, actual)
def test_backward_cast_2(self):
layer = TanhLayer()
layer.initialize_parameters()
x = np.array([[-3.0, 4.0]])
y = layer.forward(x)
dy = np.ones(y.shape)
dx_actual = layer.backward(dy)
x_torch = self.numpy_to_torch(x, requires_grad=True)
y_torch = torch.tanh(x_torch)
dy_torch = torch.ones(y_torch.shape)
y_torch.backward(gradient=dy_torch)
dx_torch = x_torch.grad
dx_expect = self.torch_to_numpy(dx_torch)
self.assertEquals(dx_actual.shape, dx_expect.shape)
self.assertClose(dx_expect, dx_actual)