update trainer

fastNLP/core/trainer.py

@@ -171,11 +171,11 @@ class Trainer(object):
 
             loss = self.get_loss(prediction, batch_y)
             self.grad_backward(loss)
-            if torch.rand(1).item() < 0.001:
-                print('[grads at epoch: {:>3} step: {:>4}]'.format(kwargs['epoch'], step))
-                for name, p in self._model.named_parameters():
-                    if p.requires_grad:
-                        print('\t{} {} {}'.format(name, tuple(p.size()), torch.sum(p.grad).item()))
+            # if torch.rand(1).item() < 0.001:
+            #     print('[grads at epoch: {:>3} step: {:>4}]'.format(kwargs['epoch'], step))
+            #     for name, p in self._model.named_parameters():
+            #         if p.requires_grad:
+            #             print('\t{} {} {}'.format(name, tuple(p.size()), torch.sum(p.grad).item()))
             self.update()
             self._summary_writer.add_scalar("loss", loss.item(), global_step=step)