Comment on IG tutorial on text

According to a recommendation from the authors of Integrated Gradients, it is written that we should use the softmax output for Integrated Gradients.

or multi-class classification models, the prediction head is typically a softmax operator on a ‘logits’ tensor. The attribution must be computed from this softmax output and not the ‘logits’ tensor. — See [1]'s Identifying the output tensor.

However, on IMDB TorchText Interpret, we use the logit value, i.e. we use model as the forward function.

Should we update according to be consistent with the recommendation? What do you think?

[wangyongjie-ntu]

@NarineK Thanks very much for your patient reply.

Why do I want to use DeepLIFT on prob? I proposed a draft method on probability and selected the IG, deepLIFT etc. as baselines. To fairly compare the performance, I want to compute the importance score on the probability function.

I just remove the torch.max operation in above snippets and directly feed the fc into torch.nn.softmax. Currently, deepLIFT works.

[wangyongjie-ntu]

@NarineK Do you mean this kind of code?

`` class Mobile(nn.Module):

def __init__(self):
    super(Mobile, self).__init__()
    self.model = nn.Sequential(
            nn.Linear(20, 16),
            nn.ReLU(),
            nn.Linear(16, 12),
            nn.ReLU(),
            nn.Linear(12, 4)
            )
    self.softmax = nn.Softmax(dim = 1)

def forward(self, x):

    fc = self.model(x)
    max_factor, _ = torch.max(fc, dim = 1)
    max_factor = max_factor.expand(4, len(max_factor)).t()
    normed_fc = fc - max_factor
    prob = self.softmax(normed_fc)
    prob = prob + 10e-10

    return prob

``