scripts/run_model.py [232:243]:
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    all_scores.append(scores.data.cpu().clone())
    all_probs.append(probs.data.cpu().clone())

    num_correct += (preds == answers).sum()
    num_samples += preds.size(0)
    print('Ran %d samples' % num_samples)

  acc = float(num_correct) / num_samples
  print('Got %d / %d = %.2f correct' % (num_correct, num_samples, 100 * acc))

  all_scores = torch.cat(all_scores, 0)
  all_probs = torch.cat(all_probs, 0)
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scripts/run_model.py [278:289]:
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    all_scores.append(scores.data.cpu().clone())
    all_probs.append(probs.data.cpu().clone())

    num_correct += (preds == answers).sum()
    num_samples += preds.size(0)
    print('Ran %d samples' % num_samples)

  acc = float(num_correct) / num_samples
  print('Got %d / %d = %.2f correct' % (num_correct, num_samples, 100 * acc))

  all_scores = torch.cat(all_scores, 0)
  all_probs = torch.cat(all_probs, 0)
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