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examples/char_lstm.pl  view on Meta::CPAN

    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)

=head1 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_'

examples/cudnn_lstm_bucketing.pl  view on Meta::CPAN

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

=head1 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

examples/lstm_bucketing.pl  view on Meta::CPAN

=head1 NAME

    lstm_bucketing.pl - Example of training LSTM RNN on Penn Tree Bank data using high level RNN interface

=head1 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)