Comments (15)
Hi, Thanks for pointing this out. The check pts error been fixed.
For the nan error, I think it might be the optimization issue. You can try smaller learning rate.
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The nan error seems roots in the relu of the controller output, which produce all 0 key, results to nan in backward of consine distance. The latest version should be more stable.
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Thanks a lot 👍 - will try it, and give you feedback :)
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Hi, I keep playing around with hyper parameters, but even with the latest version, I still get a lot of runs with NaNs
early on? When it works it's cool, bit's a bit tiring restarting again and again?
Avg. Logistic Loss: 0.6933
Iteration 50/100000
Avg. Logistic Loss: 0.5672
Iteration 100/100000
Avg. Logistic Loss: 0.3351
Iteration 150/100000
Avg. Logistic Loss: 0.2968
Iteration 200/100000
Avg. Logistic Loss: 0.2923
Iteration 250/100000
Avg. Logistic Loss: 0.2872
Iteration 300/100000
Avg. Logistic Loss: 0.2894
Iteration 350/100000
Avg. Logistic Loss: 0.2839
Iteration 400/100000
Avg. Logistic Loss: 0.2857
Iteration 450/100000
Avg. Logistic Loss: nan
Iteration 500/100000
Avg. Logistic Loss: nan
Iteration 550/100000
Avg. Logistic Loss: nan
Iteration 600/100000
Avg. Logistic Loss: nan
Iteration 650/100000
Avg. Logistic Loss: nan
Iteration 700/100000
Avg. Logistic Loss: nan
Iteration 750/100000
Avg. Logistic Loss: nan
Iteration 800/100000
Avg. Logistic Loss: nan
Iteration 850/100000
Avg. Logistic Loss: nan
Iteration 900/100000
Avg. Logistic Loss: nan
Iteration 950/100000
Avg. Logistic Loss: nan
Have you got any advice on ways to make the training stable/good hyper-parameter settings/weights initialisation? I'll keep playing around with it though.Thanks, Aj
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That's strange, I tested it several times, and it looks quite stable to me and could converge to 0.01.
Did you update all the files? you can try with a new git clone.
I will also take a closer look.
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Fresh pull from Git, but it's working OK this run!
How many iterations do you need for 0.001
? It usually converges to 0.01
for me, and then I get Nans
I'm going to try it on better GPU maybe its just this machine - which GPU are using?
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Glad to know that it is working, :D.
Did you get 0.01 and nan from the elder version? I could get 0.001 with a smaller network config (nhid and mem_size =64 and shorter seq). It usually takes more than 15000 iterations. I am using a laptop Geforece 940m gpu.
Let me know if you still get annoying nan.
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Erm I've got 0.01 from the latest version too, so it looks like its my GPU. What do you get if you use a CPU?
For some reason when I try on my CPU it crashes, and gives the,
AssertionError: leaf variable was used in an inplace operation
and this is with the latest pull and fresh pytorch? To be honest I don't understand why I get this with the CPU, and not when I run it with CUDA - very strange ???
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I could run it on cpu, and it's much faster than the gpu version. = =
The problem I have is, the running in cpu mode will consume more and more memory gradually, some other people's also reported similar issues with pytorch.
I am using the latest torch.
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How did you manage to run it on CPU ????? I might try reinstalling pytorch without CUDA ???
Yes, I've seen this memory leak too when running multithread A3C !!! I manged to fix it by getting rid of all logging, and any unnecessary things stored in the training loop.
It's a pytorch, (not an algorithm thing), as I never get it in Tensorflow. There's some sort of a memory leak, which you can plug with garbage collection, but even then it still grows for long runs ???
Have you tried MxNet ?
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Well here's my error message with the latest version of the code, and a fresh install of PyTorch,
Iteration 0/100000Traceback (most recent call last):
File "train.py", line 153, in <module>
output, _ = ncomputer.forward(input_data)
File "../../neucom/dnc.py", line 110, in forward
interface['erase_vector']
File "../../neucom/memory.py", line 389, in write
allocation_weight = self.get_allocation_weight(sorted_usage, free_list)
File "../../neucom/memory.py", line 144, in get_allocation_weight
flat_unordered_allocation_weight.cpu()
File "/home/ajay/anaconda3/lib/python3.6/site-packages/torch/autograd/variable.py", line 636, in scatter_
return Scatter(dim, True)(self, index, source)
RuntimeError: a leaf Variable that requires grad has been used in an in-place operation.
Have you got any ideas how to fix this?
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To run on cpu, I just need to set cuda to False, and it could run seamlessly. Could you try it on python 2.7? I can only think of this as the cause.
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I got it to run on the CPU, i.e. not get the leaf error thing, by reversing the commenting out of the lines around 144
in neucom/memory.py
, (which have cpu()
in them), that allowed it to run using python 3.6. But it did not train, it just converged to about 0.26
, which is much worse than GPU ???
So it does work with PyTorch on both CPU and GPU, but it's a lot different to TensorFlow, even though the algorithm and code are very similar ???
I will give it ago with an install of Anaconda 2.7
, and PyTorch CPU - spent so much time on this - really want to get it to work !
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if you changed that line, it should have some issues when you run it in gpu mode.
That function will complain if it's inputs host on gpu at the time I wrote it.
You can just use gpu, cpu mode has some memory issues as well which need to be fixed.
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OK, thanks a lot :)
I won't be able to do any testing today, but please let me know, if and when you fix the CPU capability - that will be really really COOL 👍
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Related Issues (7)
- AssertionError: leaf variable was used in an inplace operation HOT 4
- Compare with DeepMind's implementation HOT 2
- backward through cumprod HOT 12
- Error running copy example HOT 6
- Error,,which pytorch version? HOT 1
- TypeError: norm received an invalid combination of arguments - got (int, bool), but expected one of: HOT 2
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