char_lstm.pl - Example of training char LSTM RNN on tiny shakespeare using high level RNN interface
--test Whether to test or train (default 0) --num-layers number of stacked RNN layers, default=2 --num-hidden hidden layer size, default=200 --num-seq sequence size, default=32 --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='adam' --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 --model-prefix prefix for checkpoint files for loading/saving, default='lstm_' --load-epoch load from epoch --stack-rnn stack rnn to reduce communication overhead (1,0 default 0) --bidirectional whether to use bidirectional layers (1,0 default 0) --dropout dropout probability (1.0 - keep probability), default 0
To install AI::MXNet, copy and paste the appropriate command in to your terminal.
cpanm
cpanm AI::MXNet
CPAN shell
perl -MCPAN -e shell install AI::MXNet
For more information on module installation, please visit the detailed CPAN module installation guide.