for b in range(0, self.nb_batches):
target = torch.LongTensor(self.batch_size).bernoulli_(0.5)
input = svrt.generate_vignettes(problem_number, target)
- acc += input.sum() / input.numel()
- acc_sq += (input * input).sum() / input.numel()
+ acc += float(input.sum()) / input.numel()
+ acc_sq += float((input * input).sum()) / input.numel()
self.targets.append(target)
self.input_storages.append(svrt.compress(input.storage()))
if logger is not None: logger(self.nb_batches * self.batch_size, b * self.batch_size)