0e8ec93b28ecd86898b3668d48d346f510e95167,pl_examples/domain_templates/computer_vision_fine_tuning.py,TransferLearningModel,validation_epoch_end,#TransferLearningModel#Any#,308

Before Change

                                     for output in outputs]).mean()
        val_acc_mean = torch.stack([output["num_correct"]
                                    for output in outputs]).sum().float()
        val_acc_mean /= (len(outputs) * self.batch_size)
        return {"log": {"val_loss": val_loss_mean,
                        "val_acc": val_acc_mean,
                        "step": self.current_epoch}}

    def configure_optimizers(self):
        optimizer = optim.Adam(filter(lambda p: p.requires_grad,
                                      self.parameters()),

After Change

        Compute and log validation loss and accuracy at the epoch level.

        val_loss_mean = torch.stack([output["val_loss"] for output in outputs]).mean()
        train_acc_mean = self.valid_acc.compute()
        log_dict = {"val_loss": val_loss_mean, "val_acc": train_acc_mean}
        self.log_dict(log_dict, prog_bar=True)
        self.log_dict({"step": self.current_epoch})

Instances