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Scenarios to train and test results #15

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rpytel1 opened this issue Oct 16, 2019 · 0 comments
Open
10 tasks

Scenarios to train and test results #15

rpytel1 opened this issue Oct 16, 2019 · 0 comments

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@rpytel1
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rpytel1 commented Oct 16, 2019

Char based approach

  • one-hot encoding config/char_based/one_hot.json
  • 10 embedding layer dim config/char_based/10_dim.json
  • 20 embedding layer dim config/char_based/20_dim.json

Try for both LSTM 1 layer and 2 layers (max 5/7 epochs)

Word-based approach

  • 500 embedding layer dim config/word_based/500dim.json
  • 100 embedding layer dim config/word_based/100dim.json
  • 20 embedding layer dim 'config/word_based/20dim.json

Try for both LSTM 1 layer and 2 layers (max 5/7 epochs)

Code2vec training

  • config/code_2_vec/code2vec.json (Still need some fixing with paths for dicts and data)

Code2vec pretrained output

  • Linear (Casper)
  • SVM (Jan)
  • Random Forest (Jan)
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