PTB language modeling (RNN/LSTM, Pytorch)
A reproduction of Recurrent Neural Network Regularization (https://arxiv.org/abs/1409.2329). Use main.py to train a RNN to predict words based on a given sequence of words and apply dropout to LSTM model to reduce overfitting. The main.py accepts the following optional arguments.
--data location of the training data
--checkpoint loading the existing model
--emsize embedding size
--nhid the dimension of hidden layers
--nlayers the number of layers
--lr learning rate
--clip gradient clipping
--epochs epochs number
--batch_size batch size
--bptt sequence length
--dropout dropout
--save location to save the current model
--opt choose a optimizer (SGD, Momentum, Adam, RMSprop)