Comments (5)
Hello @saria85 ,
For running DocNADE, please check out www.dmi.usherb.ca/~larocheh/code/DocNADE.zip.
Run run_w2v.py to train the word2vec model and get document representations.
As for parameters in run_w2v.py, you dot not need docnames.
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Hi,
It taking lots of time for running, did you experience the same thing?
Also, it only created two file new_test.libsvm and new_train.libsvm.
which is like a vector then where can I find the clusters of the topics.
Do you have any idea?
Thanks,
:)
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Also Im going to have wordclouds visualize my topics, you said that I need to change the list of words in pred.py according to my output
My question is that can I choose any word I like or it should be according to their strength, like choosing the first five highest weight in each topic.
Thanks, ;)
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@saria85 DocNADE takes quite a time to train. Based on the paper, a topic i is visualized by picking the 10 words w with strongest connection W_iw. You might need to dig deeper into the code to fetch the weight matrix.
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You can visualize any word in your vocabulary.
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Related Issues (20)
- topics or word representations HOT 15
- seeing rare word HOT 1
- [Question] output topics representation HOT 7
- [Question] Back-propagation through losers via alpha HOT 5
- using sigmoid function HOT 4
- how alpha get collaborated in backpropagation phase HOT 6
- VAE loss and classification accuracy HOT 4
- PCA projection plot. HOT 1
- neurons in the middle layer HOT 2
- Dense_tied function HOT 1
- VAE HOT 10
- steps to achieve accuracy HOT 25
- different result over running different version of tensorflow HOT 2
- visualization using pca and tsne HOT 5
- getting NVDM run HOT 9
- Sharing the trained models? HOT 1
- the val_loss can't converge in tensorflow 1.15.2, while tf 1.2.1 works HOT 3
- Custom Dataset Help! HOT 3
- Reproduce reported results HOT 4
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