Confusion about computing the gradient of predictor?

[wenerhg]

In 'NAO/cnn/encoder/encoder.py', the gradient of predictor is computed as follow:
new_arch_outputs = self.encoder_outputs - self.params['predict_lambda'] * grads_on_outputs

However, I think the gradient should be computed as described in paper:

Is there an error with computing the gradient of predictor in code?

[renqianluo]

@wenerhg In the paper, for the general purpose, we denote f as the performance which is the larger the better, so we move towards the positive direction of the gradient, which shows as '+'. In the code, we use error rate as f which is the lower the better, so we need to move towards the negative direction, which shows as '-'.

[renqianluo]

@wenerhg in other words, if you use accuracy as f in code, you should move towards the positive direction and use '+'