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