NAME

char_lstm.pl - Example of training char LSTM RNN on tiny shakespeare using high level RNN interface

SYNOPSIS

--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