Comments (1)
Since Theano is no longer maintained, I don't plan to add anything to this repo.
But changing the current code to one-pred-per-seq version shouldn't be that hard.
Below are some things that need to be done in gram.py:
- line 81-98: use line 56-74 of rnn_predict.
- line 119: y should be either a matrix (in case of multi-class prediction) or a vector (in case of binary prediction), not a 3D tensor, because you make only one prediction per sequence.
- line 121: you don't need lengths anymore. Be sure to delete lengths in everywhere in the code.
-line 135-147: see line 89-98 of rnn_predict. - line 149, load_data(): Load an appropriate label file.
- line 224-240: you don't need a 3d tensor y. Prepare an appropriate y.
Basically those are the high-level directions. rnn_predict should give you a pretty good idea how to modify gram.py for binary prediction.
Best,
Ed
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Related Issues (19)
- Description of arguments HOT 1
- function arguments HOT 2
- How to calculate accuracy@20 in each frequency group? HOT 6
- Dimensions not matching? HOT 2
- some question about level2.pk and ancestors HOT 1
- Needing the code for calculating metrics HOT 1
- Empty level1.pk when working with the new version of MIMIC
- Null Level two HOT 1
- Loading embedding file from glove training HOT 3
- Access to the Hyper-parameter tuning document
- Label HOT 3
- gradient with Theano
- comparison between med2vec and gram HOT 2
- query about "def padMatrix" HOT 2
- query about the num of ancestors HOT 2
- error running gram.py HOT 4
- Domain knowledge graph issue? HOT 2
- Low-frequency labels hard to predict HOT 3
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