6aad1de658a933d3fa376f7fe9abf419da8a8bd2,python/baseline/dy/lm/train.py,LanguageModelTrainerDynet,test,#LanguageModelTrainerDynet#Any#Any#Any#,61

Before Change

            total_loss += loss_val
            if initial_state is not None:
                initial_state = [x.npvalue() for x in initial_state]
            iters += len(y)

        if phase == "Valid":
            self.valid_epochs += 1
            output = self.valid_epochs
        else:
            output = 0

        self.log.debug({"phase": phase, "time": time.time() - start})
        metrics["avg_loss"] = total_loss / iters
        metrics["perplexity"] = np.exp(total_loss / iters)
        for reporting in reporting_fns:
            reporting(metrics, output, phase)
        return metrics

After Change

            epochs = self.valid_epochs

        metrics = self.calc_metrics(total_loss, total_toks)
        self.report(
            epochs, metrics, start,
            phase, "EPOCH", reporting_fns
        )
        return metrics


@register_training_func("lm")

Instances