CODEWITHSUNDEEP
pythonmachine-learningpytorch
Alex
Alex

Reputation: 21

Problem with executing python machine learning code I found on GitHub

I need some clear instructions on how to execute some code.

Context: This is a python machine learning peptide binding script, but you don't need to know biology to help me. I am trying to recreate this scientific paper to test its validity and if I can use it. I work in the biotech industry and am only somewhat familiar with C# and python. The paper is linked to a GitHub page. And the GitHub page has some instructions on how to execute the code. But every time I try to execute this code as instructed, it gives me an error. I already installed its requirements of the most updated pytorch, numpy, scikit-learn; I also switched between GPU and CPU, but no method worked. I don't know what to do at this point.

Paper Title: "Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs" by Ido Springer, Hanan Besser. etc.

Paper's Github8 (found in the paper's abstract): https://github.com/louzounlab/ERGO

These are the example codes I input in the terminal. The example code was found in a comment at the end of ERGO.py

GPU ver:

python ERGO.py train lstm mcpas specific cuda:0 --model_file=model.pt --train_data_file=train_data --test_data_file=test_data

GPU code results:

Traceback (most recent call last):   File "D:\D Download\ERGO-master\ERGO.py", line 437, in <module>
    main(args)   File "D:\D Download\ERGO-master\ERGO.py", line 141, in main
    model, best_auc, best_roc = lstm.train_model(train_batches, test_batches, args.device, arg, params)   File "D:\D Download\ERGO-master\lstm_utils.py", line 163, in train_model
    model.to(device)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 927, in to
    return self._apply(convert)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 579, in _apply
    module._apply(fn)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 602, in _apply
    param_applied = fn(param)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 925, in convert
    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\cuda\__init__.py", line 211, in _lazy_init
    raise AssertionError("Torch not compiled with CUDA enabled") AssertionError: Torch not compiled with CUDA enabled

CPU code ver (only replaced specific cuda:0 with specific cpu):

python ERGO.py train lstm mcpas specific cpu --model_file=model.pt --train_data_file=train_data --test_data_file=test_data

CPU code results:

epoch: 1 C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\functional.py:1960: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.   warnings.warn("nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.") Traceback (most recent call last):   File "D:\D Download\ERGO-master\ERGO.py", line 437, in <module>
    main(args)   File "D:\D Download\ERGO-master\ERGO.py", line 141, in main
    model, best_auc, best_roc = lstm.train_model(train_batches, test_batches, args.device, arg, params)   File "D:\D Download\ERGO-master\lstm_utils.py", line 173, in train_model
    loss = train_epoch(batches, model, loss_function, optimizer, device)   File "D:\D Download\ERGO-master\lstm_utils.py", line 137, in train_epoch
    loss = loss_function(probs, batch_signs)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\loss.py", line 613, in forward
    return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)   File "C:\Users\username\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\functional.py", line 3074, in binary_cross_entropy
    raise ValueError( ValueError: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([50, 1])) is deprecated. Please ensure they have the same size.

Upvotes: 1

Views: 96

Answers (1)

mmansur
mmansur

Reputation: 33

Looking at the ValueError, it seems that what you're trying to do is deprecated in pytorch, so you have a more recent version of the package than the one it was developed in. I suggest you try

pip install pytorch 1.4.0

in command line.

I'm not familiar with pytorch but menaging tensor shapes in tensorflow is the biggest pain in the a** for me. What it actually looks like to be the problem is that the input has an extra dimension than it should, so you would have to manually reshape it.

Upvotes: 1

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