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View Code? Open in Web Editor NEWAuto-encoder on torch - trying out the various AEs
Auto-encoder on torch - trying out the various AEs
Hi, I find that there are three linear and bn at the beginning of Decoder (196-200 in resnet.py). Why adding these layers? Much Thanks.
When running resnet.py, there is following error:
Traceback (most recent call last):
File "/home/wentian/.pycharm_helpers/pydev/pydevd.py", line 1741, in <module>
main()
File "/home/wentian/.pycharm_helpers/pydev/pydevd.py", line 1735, in main
globals = debugger.run(setup['file'], None, None, is_module)
File "/home/wentian/.pycharm_helpers/pydev/pydevd.py", line 1135, in run
pydev_imports.execfile(file, globals, locals) # execute the script
File "/home/wentian/.pycharm_helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "/home/wentian/work/caption_t_r_2/src/test.py", line 359, in <module>
pred = autoencoder(inputs)
File "/home/wentian/library/miniconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in __call__
result = self.forward(*input, **kwargs)
File "/home/wentian/work/caption_t_r_2/src/test.py", line 262, in forward
x = self.decoder(x)
File "/home/wentian/library/miniconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in __call__
result = self.forward(*input, **kwargs)
File "/home/wentian/work/caption_t_r_2/src/test.py", line 213, in forward
x = F.relu(self.bn3(x))
File "/home/wentian/library/miniconda3/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in __call__
result = self.forward(*input, **kwargs)
File "/home/wentian/library/miniconda3/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py", line 45, in forward
self._check_input_dim(input)
File "/home/wentian/library/miniconda3/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py", line 193, in _check_input_dim
.format(input.dim()))
ValueError: expected 4D input (got 2D input)
At line 262, the shape of x
is (11, 48).
I tried to understand why did you put this Binary layer between encoder and decoder. I mean are you trying to create a map of positive values? If this is the case, I am not sure if restricting them will work better than just freeing them up, also wouldn't using something like x[x > 0] or something be simpler?
Thank you for this great repository. I am using the Resnet Autoencoder implementation code for my academic work, but I did not see any license or permission regarding academic usage. Could you please clarify this issue for me?
You could choose a license from the GitHub documentation page.
https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/licensing-a-repository
If you want it short and simple, many open-source repositories are using the MIT license (just a suggestion). https://choosealicense.com/licenses/mit/
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