LSTM, GRU cell implementation from scratch
Assignment 4 weights for Deep Learning, CS60010.
Currently includes weights for LSTM and GRU for hidden layer size as 32, 64, 128 and 256.
The aim of this assignment was to compare performance of LSTM, GRU and MLP for a fixed number of iterations, with variable hidden layer size. Please refer to Report.pdf for the details.
Suggested reading: colah's blog on LSTM. That's really all you need.
- Loss as a function of iterations
sh run.sh - Run all hidden units LSTM GRU and report accuracy. Output here
python train.py --train - Run training, save weights into weights/ folder. Defaults to LSTM, hidden_unit 32, 30 iterations / epochs
python train.py --train --hidden_unit 32 --model lstm --iter 5: Train LSTM and dump weights. Run training with specified number of iterations. Default iterations are 50.
python train.py --test --hidden_unit 32 --model lstm - Load precomputed weights and report test accuracy.
data_loaderis used to load data from zip files indatafolder.moduledefines the basic LSTM and GRU code.trainhandles input and states the model.
The MIT License (MIT) 2018 - Kaustubh Hiware.