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Update README.md

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Pavol Bielik 2015-01-22 18:20:47 +01:00
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@ -103,11 +103,11 @@ To train to model we simply run:
> ./bin/training/train --logtostderr -num_threads 16 --input training_data
where training_data is the file produced in previous step and num_threads specifies how many threads should the training algorithm used. To get full options available for training (such as learning rate, regularization and margin), use:
where `training_data` is the file produced in previous step and `num_threads` specifies how many threads should the training algorithm use. To get full options available for training (such as learning rate, regularization and margin), use:
> ./bin/training/train --help
As a result, it creates two files with the trained model: `model_strings` and `model_features`.
After the training finishes, two files are created which contains the trained model: `model_strings` and `model_features`.
Predicting Properties (Nice2Predict)
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@ -117,4 +117,5 @@ To predict properties for new programs, start a server after a model was trained
> ./bin/server/nice2server --logtostderr
Then, the server will predict properties for programs given in JsonRPC format. One can debug and observe deobfuscation from the viewer available in the [viewer/viewer.html](https://github.com/eth-srl/Nice2Predict/blob/master/viewer/viewer.html) (online demo available at http://www.nice2predict.org).
The server takes as an input same JSON format as described above and returns best assigment to the unknown properties (labelled as `inf`).