NAME

    char_lstm.pl - Example of training char LSTM RNN on tiny shakespeare using high level RNN interface
                   with optional inferred sampling (RNN generates Shakespeare like text)

SYNOPSIS

    --num-layers     number of stacked RNN layers, default=2
    --num-hidden     hidden layer size, default=256
    --num-embed      embed size, default=10
    --num-seq        sequence size, default=60
    --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
    --bidirectional  use bidirectional cell, default false (0)
    --disp-batches   show progress for every n batches, default=50
    --chkp-prefix    prefix for checkpoint files, default='lstm_'
    --cell-mode      RNN cell mode (LSTM, GRU, RNN, default=LSTM)
    --sample-size    a size of inferred sample text (default=10000) after each epoch
    --chkp-epoch     save checkpoint after this many epoch, default=1 (saving every checkpoint)