lrh

Learning RNN Hierarchies

about.md has the basic details of what the code is trying to do and why.

• Layers are defined in layers/ along with tests
• All layers derive from abstract base class defined in base.py
• All data and data prepping scripts are in data/ folder
• network.py has tools for taking in a list as a model and doing layer by layer forward/backward pass, getting gradients, setting/getting parameters
• train_ptb.py trains a model on Penn Tree Bank text file, which has to be placed in the data/ folder
• train_mnist.py trains a model on Sequential MNIST. mnist.pkl.gz has to be placed in data/ folder
• As the network trains, logs are generated. Final logs and models are stored as pickle objects in results/experiment_name, where experiment_name is a string defined in train_ scripts

##Requires:

• Numpy
• Scipy (for one special function to calculate entropy)
• matplotlib
• climin