Low performance on various GPUs about neuralcde HOT 3 CLOSED

Hi there. (In passing I'd note that I'm not a professor!)

So the GPUs we uses were some mid-level ones -- to quote from the paper, two GeForce RTX 2080 Ti, and two Quadro
GP100. I doubt that the GPU choice is going to be responsible for poor performance though.

On the topic of model performance -- I am (obviously) quite surprised by the poor results you seem to be getting. IIRC with Speech Commands you should get to about 85% accuracy after just a few epochs, and then the rest of the training time slowly improves things a little further.

Are you definitely running the neural CDE models, and not one of the benchmark-for-comparison RNN models? The RNN models were all flaky on this dataset: sometimes they would produce excellent results, sometimes they would produce awful results, and it differed from training run to training run. That aside, it's also plausible that a change in software library somewhere has quietly broken something.

As a first place to start, I'd recommend trying the Speech Commands example from this repository. This was a follow-up paper that happened later, with a new codebase. (And substantially tidier code.) Getting some more data might help diagnose the issue.

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Many thanks for your response.

I will try using the new code in your recommendation. In order to reply above questions. I tried to remove all other models. I just run NeuralCDE model. And, due to datasets/speech_commands.py does not contain any deprecated methods so I have not changed anything. But I think some small changes have broken something.

Again, thanks for your advices.

from neuralcde.

I'm sorry madam, In case someone falls into my fault, I will leave some comments for them.

Please make sure that torchaudio.load goes with normalize=False (default will be True), I have erased this term when process data.
If you want to train with fewer classes, please make sure, X and Y variables was generate as same size as batch_index.

Thanks.

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