This example trains a multi-layer RNN (Elman, GRU, or LSTM) on a language modeling task. By default, the training script uses the PTB dataset, provided. The trained model can then be used by the generate script to generate new text.
python main.py --cuda --epochs 6 # Train a LSTM on PTB with CUDA, reaching perplexity of 117.61
python main.py --cuda --epochs 6 --tied # Train a tied LSTM on PTB with CUDA, reaching perplexity of 110.44
python main.py --cuda --tied # Train a tied LSTM on PTB with CUDA for 40 epochs, reaching perplexity of 87.17
python generate.py # Generate samples from the trained LSTM model.The model uses the nn.RNN module (and its sister modules nn.GRU and nn.LSTM)
which will automatically use the cuDNN backend if run on CUDA with cuDNN installed.
During training, if a keyboard interrupt (Ctrl-C) is received, training is stopped and the current model is evaluated against the test dataset.
The main.py script accepts the following arguments:
optional arguments:
-h, --help show this help message and exit
--data DATA location of the data corpus
--model MODEL type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)
--emsize EMSIZE size of word embeddings
--nhid NHID number of hidden units per layer
--nlayers NLAYERS number of layers
--lr LR initial learning rate
--clip CLIP gradient clipping
--epochs EPOCHS upper epoch limit
--batch-size N batch size
--bptt BPTT sequence length
--dropout DROPOUT dropout applied to layers (0 = no dropout)
--decay DECAY learning rate decay per epoch
--tied tie the word embedding and softmax weights
--seed SEED random seed
--cuda use CUDA
--log-interval N report interval
--save SAVE path to save the final modelWith these arguments, a variety of models can be tested. As an example, the following arguments produce slower but better models:
python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40 # Test perplexity of 80.97
python main.py --cuda --emsize 650 --nhid 650 --dropout 0.5 --epochs 40 --tied # Test perplexity of 75.96
python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40 # Test perplexity of 77.42
python main.py --cuda --emsize 1500 --nhid 1500 --dropout 0.65 --epochs 40 --tied # Test perplexity of 72.30These perplexities are equal or better than Recurrent Neural Network Regularization (Zaremba et al. 2014) and are similar to Using the Output Embedding to Improve Language Models (Press & Wolf 2016 and Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling (Inan et al. 2016), though both of these papers have improved perplexities by using a form of recurrent dropout (variational dropout).
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