def test_softmax_classification_batch_multi_target(self) -> None:
num_in = 40
inputs = torch.arange(0.0, num_in * 3.0,
requires_grad=True).reshape(3, num_in)
baselines = torch.arange(1.0, num_in + 1).reshape(1, num_in)
model = SoftmaxDeepLiftModel(num_in, 20, 10)
dl = DeepLift(model)
self.softmax_classification(model, dl, inputs, baselines,
torch.tensor([2, 2, 2]))
def test_softmax_classification_batch_multi_baseline(self) -> None:
num_in = 40
input = torch.arange(0.0, num_in * 2.0,
requires_grad=True).reshape(2, num_in)
baselines = torch.randn(5, 40)
model = SoftmaxDeepLiftModel(num_in, 20, 10)
dl = DeepLiftShap(model)
self.softmax_classification(model, dl, input, baselines,
torch.tensor(2))
def test_softmax_classification_zero_baseline(self) -> None:
num_in = 20
input = torch.arange(0.0, num_in * 1.0,
requires_grad=True).unsqueeze(0)
baselines = 0.0
model = SoftmaxDeepLiftModel(num_in, 20, 10)
dl = DeepLift(model)
self.softmax_classification(model, dl, input, baselines,
torch.tensor(2))