Custom loss function - train_loss / valid_loss is nan

Hi,

I’m trying to use a custom loss function with some tabular data. My loss function is:

def myLossFunc(pred:Tensor, targ:Tensor):
    params = torch.log10(1+targ)
    pred_approx = torch.log10(1-pred)
    to_sum = params*pred_approx
    return to_sum.sum()

That is, my target is some coefficient between [-0.5;0.5], my y_range is [0;1] and I want to have the sum of the log(1+target)*log(1-prediction) as a loss function.

I load my data as follows (idx is some permuted array):

data = (TabularList.from_df(df, cont_names=['inputA','inputB',...])
                  .split_by_idx(idx[:3000])
                  .label_from_df(cols='targ_coeffecient').databunch())

and generate my model as follows:

learner = tabular_learner(data, layers=[100,100], y_range=torch.tensor([0, 1]), loss_func=myLossFunc)

However, when running fit_one_cycle, train_loss and valid_loss are always NaN. I don’t understand why. What is wrong with my loss function please? How to use custom loss functions with fast.ai please?

Thanks.

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