I have a model trained on 16 features, seq_len of 120 and in batches of 256.
I would like to test the loss on the model on a testset, with random sampling from a normal distribution for one features at a time so I can measure how important each features is (important features would give a high rise in loss when its random sampled)
Any advice on how to manipulate one feature at a time? the shape of the input is (256, 120, 16)
h = model.init_hidden(batch_size)
    with torch.no_grad():
        for inp, labels in loader:
                       
            if(train_on_gpu):
                inp, labels = inp.cuda(), labels.cuda()
            

            outputs, h = model(inp, h)
            _, predicts = torch.max(outputs, 1)