lstm_bucketing.pl - Example of training LSTM RNN on Penn Tree Bank data using high level RNN interface
SYNOPSIS
--num-layers number of stacked RNN layers, default=2
--num-hidden hidden layer size, default=200
--num-embed embedding layer size, default=200
--gpus list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu.
Increase batch size when using multiple gpus for best performance.
--kv-store key-value store type, default='device'
--num-epochs max num of epochs, default=25
--lr initial learning rate, default=0.01
--optimizer the optimizer type, default='sgd'
--mom momentum for sgd, default=0.0
--wd weight decay for sgd, default=0.00001
--batch-size the batch size type, default=32
--disp-batches show progress for every n batches, default=50
--chkp-prefix prefix for checkpoint files, default='lstm_'
--chkp-epoch save checkpoint after this many epoch, default=0 (saving checkpoints is disabled)
Module Install Instructions
To install AI::MXNet, copy and paste the appropriate command in to your terminal.