2733bef356c53286d475a67476d88d4840923830,code/deep/finetune_AlexNet_ResNet/finetune_office31.py,,finetune,#,87

Before Change

    criterion = nn.CrossEntropyLoss()
    for epoch in range(1, N_EPOCH + 1):
        // lr_schedule(optimizer, epoch)
        print("Learning rate: {:.8f}".format(optimizer.param_groups[0]["lr"]))
        print("Learning rate: {:.8f}".format(optimizer.param_groups[-1]["lr"]))
        for phase in ["src", "val", "tar"]:
            if phase == "src":

After Change

            if phase == "val" and epoch_acc > best_acc:
                stop = 0
                best_acc = epoch_acc
                torch.save(model.state_dict(), "model.pkl")
        if stop >= args.early_stop:
            break
        print()
    model.load_state_dict(torch.load("model.pkl"))

Instances