f5f63fb9c06cd626ff64a31b976e148c92ff99d1,OpenNMT/onmt/Optim.py,Optim,step,#Optim#,30

Before Change

        grad_norm = math.sqrt(grad_norm)
        shrinkage = self.max_grad_norm / grad_norm

        for param in self.params:
            if shrinkage < 1:
                param.grad.data.mul_(shrinkage)

        self.optimizer.step()
        return grad_norm

    // decay learning rate if val perf does not improve or we hit the start_decay_at limit

After Change

    def step(self):
        // Compute gradients norm.
        if self.max_grad_norm:
            clip_grad_norm(self.params, self.max_grad_norm)
        self.optimizer.step()

    // decay learning rate if val perf does not improve or we hit the start_decay_at limit
    def updateLearningRate(self, ppl, epoch):

Instances