Pytorch cross entropy loss
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#PYTORCH CROSS ENTROPY LOSS CODE#
I changed your sample code to work on MNIST dataset. In your sample code, you passed a float value. It seems you need to pass a 1D LongTensor for the target. The prediction should be a value from 0-9 In other words, when I plug in a test example into the trained model: pred = model(test).argmax(dim=1, keepdim=True) Link to notebook: import torch import torch.nn as nn import torch.nn. I expect for the code to train and be able to predict. This notebook breaks down how crossentropy function is implemented in pytorch, and how it is related to softmax, logsoftmax, and NLL (negative log-likelihood). other points that help clarify my general problem:.RuntimeError: Expected object of scalar type Long but got scalar type Float for argument #2 'target' in call to _thnn_nll_loss_forward Ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index) Return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)įile "C:\Users\User\Anaconda3\envs\torch\lib\site-packages\torch\nn\functional.py", line 1838, in nll_loss Ignore_index=self.ignore_index, reduction=self.reduction)įile "C:\Users\User\Anaconda3\envs\torch\lib\site-packages\torch\nn\functional.py", line 2009, in cross_entropy Pydev_imports.execfile(file, globals, locals) # execute the scriptįile "C:\Program Files\JetBrains\P圜harm Community Edition 2019.2\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfileĮxec(compile(contents+"\n", file, 'exec'), glob, loc)įile "C:/Users/User/Desktop/paper_recreation/PCA_CNN/CNN_DEBUG.py", line 109, in įile "C:\Users\User\Anaconda3\envs\torch\lib\site-packages\torch\nn\modules\module.py", line 541, in _call_įile "C:\Users\User\Anaconda3\envs\torch\lib\site-packages\torch\nn\modules\loss.py", line 916, in forward Return self._exec(is_module, entry_point_fn, module_name, file, globals, locals)įile "C:\Program Files\JetBrains\P圜harm Community Edition 2019.2\helpers\pydev\pydevd.py", line 1412, in _exec Globals = n(setup, None, None, is_module)įile "C:\Program Files\JetBrains\P圜harm Community Edition 2019.2\helpers\pydev\pydevd.py", line 1405, in run The program is throwing the following error: Traceback (most recent call last):įile "C:\Program Files\JetBrains\P圜harm Community Edition 2019.2\helpers\pydev\pydevd.py", line 2060, in įile "C:\Program Files\JetBrains\P圜harm Community Edition 2019.2\helpers\pydev\pydevd.py", line 2054, in main Optimizer = (model.parameters(), lr=learning_rate) Test_loader = (dataset=custom_dataset.MNIST_test, Shuffle=True) # outputs (sample, targets) -> (64圆6, 64x1) Train_loader = (dataset=custom_dataset.MNIST_train,
#PYTORCH CROSS ENTROPY LOSS HOW TO#
I don't know how to properly share data, but long-story short the input has 66 features between (using PCA to decompose the MNIST image) import torch I have re-written my code, fact checked my methodology with a colleague, and also done some rubber-duck programming to no avail. What have you done to try and solve the problem?.I predict it has something to do with the way that my Net is setup/outputting.
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float() when entering into the loss function. I have done a lot of online searching, and others had similar problems.