def test_activation_softplus():
# simple tensor
x = torch.tensor([0.0, 1.0, 0.5, 2.0])
expt = torch.log(1.0 + torch.exp(x)) - np.log(2)
assert torch.allclose(expt, shifted_softplus(x), atol=0.0, rtol=1.0e-7)
# random tensor
torch.manual_seed(42)
x = torch.randn((10, 5), dtype=torch.double)
expt = torch.log(1.0 + torch.exp(x)) - np.log(2)
assert torch.allclose(expt, shifted_softplus(x), atol=0.0, rtol=1.0e-7)
x = 10 * torch.randn((10, 5), dtype=torch.double)
expt = torch.log(1.0 + torch.exp(x)) - np.log(2)
assert torch.allclose(expt, shifted_softplus(x), atol=0.0, rtol=1.0e-7)
def test_shape_ssp():
in_data = torch.rand(10)
out_data = shifted_softplus(in_data)
assert in_data.shape == out_data.shape
def forward(self, input):
return shifted_softplus(input)
