Comments (6)
Tried re-running with a smaller data set and now get this
-- Epoch 1 / 50
/home/ubuntu/torch/install/bin/luajit: ...u/torch/install/share/lua/5.1/rnn/SequencerCriterion.lua:42: expecting target table
stack traceback:
[C]: in function 'assert'
...u/torch/install/share/lua/5.1/rnn/SequencerCriterion.lua:42: in function 'forward'
./seq2seq.lua:74: in function 'train'
train.lua:85: in main chunk
[C]: in function 'dofile'
...untu/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk
[C]: at 0x0000cff9
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@bobhinkle could be related to this dependency issue, he posts a solution #1 if not that it could be a memory overflow? if you are running this locally, i suggest running it on AWS. See ML for Hackers #4 for an AWS walkthrough.
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@llSourcell I had the same issue and got expecting target table
.
I don't think it's related to the dependencies because I tried with and without CUDA:
th train.lua --cuda --dataset 500 --hiddenSize 100 --maxEpoch 10 --saturateEpoch 4
th train.lua --dataset 500 --hiddenSize 100 --maxEpoch 10 --saturateEpoch 4
That stuff about dependencies was mainly about when running with OpenCL.
I'm running on a CentOS machine with Lua 5.3.3. I also tried with Lua 5.1.4.
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Temporary solution: Comment out the failing asserts in SequencerCriterion.lua (in ~/torch/install/share/lua/5.2/rnn/SequencerCriterion.lua
for me because I installed Torch with TORCH_LUA_VERSION=LUA52 ./install.sh
by default you'll find it at ~/torch/install/share/lua/5.1/rnn/SequencerCriterion.lua
).
Those checks on target
don't really seem that necessary to us.
I trained it pretty quickly using th train.lua --dataset 500 --hiddenSize 100 --maxEpoch 10 --saturateEpoch 4
and it works but the answers aren't that good, hopefully that just because of my constraints and not because something else went wrong.
@llSourcell maybe changing the type of decoderTarget
would be a better fix?
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@juharris thanks so much for your posts on here with issues. I now have 7 ML for hackers repos to maintain with much more content to come so I may need some help with this issue. The quick fix you posted, could you make a PR with it? I would really appreciate. I'll merge it immediately
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@llSourcell The fix isn't in this repo. The fix is the rnn
package. Looks like they'll have a fix coming in the original repo: macournoyer/neuralconvo#31
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Related Issues (8)
- Installation command for openCL packages
- Put clear link to original project and MIT licence HOT 4
- unable to convert argument 3 from cdata<struct THCudaTensor*> to cdata<struct THCudaLongTensor*> HOT 1
- Any example to use ubuntu-corpus for data training
- expecting target table HOT 2
- Error while training
- How to create a corpuss like cornell movie format?
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