Implementation of LSTM and GRU cells for PyTorch

This repository is an implementation of the LSTM and GRU cells without using the PyTorch LSTMCell and GRUCell.

It is tested on the MNIST dataset for classification.

The 28x28 MNIST images are treated as sequences of 28x1 vector.

The RNN consist of

• A linear layer that maps 28-dimensional input to and 128-dimensional hidden layer
• One intermediate recurrent neural network (LSTM or GRU)
• A fully connected layer which maps the 128 dimensional input to 10-dimensional vector of class labels.

Requirements

Python>=3.5

PyTorch== 0.4.0