I’m faced with a problem. After saving and then reloading my model, the test accuracy changes a lot. The problem is some parameters from submodels haven’t been saved.
Now, I want to debug it. I need to make test predictions the same when it runs each time. I know the performance of some layers is non-deterministic. I use this code:
        torch.backends.cudnn.deterministic = True
        torch.manual_seed(999)

Now, after loading the model, test accuracy is the same each time. But it’s still very different from the training model. Training model means the model before saving and reloading. I have already turned the model into eval mode by
model.eval()

Here are some questions:


I put the code at this position. Does it really make any effect? Or I must put it at the front of the whole project. My model has dropout layers.
# train the model
torch.backends.cudnn.deterministic = True
torch.manual_seed(999)
model.eval()
# test the model



After evaluation on the eval_data, I want to make the model non-deterministic again. I think this is better for training. What should I do?


My model performs differently on the test data each time, partly because of the non-deterministic model, partly because of the wrong saving and reloading. I guess some submodules haven’t been registered. Therefore, those parameters are not in model.state_dict(). Could you please tell me what kind of submodels will be registered in model.state_dict()?


I use PyTorch 0.3.1.
Thanks a lot！