CS224u Final Project
Use Recurrent Neural Networks to identify the sentiment of an input text.
Created by Nico Cserepy, Stephen Koo, and Ben Weems
Start with the following command to list all the possible options.
python train.py -h
Example training and evaluation run:
python train.py --train-embedding --parameters=params.tflearn --epochs=5
To continue training from where you left off (if you previously saved parameters at params.tflearn):
python train.py --train-embedding --parameters=params.tflearn --epochs=5 --continue-training
To try a different set of GLoVE vectors:
python train.py --glove=YOURVECTORS.txt --data-cache=newdata.pkl --force-preprocess
The --force-preprocess flag here may be important here if you previously used a different set of GLoVE vectors with the same data cache location, since the preprocessing steps depend on the GLoVE vectors you are using. If --force-preprocess is not specified, the program will attempt to load previously cached preprocessed data from the specified location (or the default sst_data.pkl).
To create a new model, create a new module at data/models/<modelname>.py.
The module should define function called build(), which should
return a network built by tflearn (see data/models/lstm.py for an example).