AI-MXNet

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lib/AI/MXNet/KVStore.pm  view on Meta::CPAN

        AI::MXNetCAPI::KVStorePullEx(
            $self->handle, scalar(@{ $keys }), $keys, $vals, $priority
        )
    );
}

=head2  set_optimizer

    Register an optimizer to the store

    If there are multiple machines, this process (should be a worker node)
    will pack this optimizer and send it to all servers. It returns after
    this action is done.

    Parameters
    ----------
    optimizer : Optimizer
        the optimizer
=cut

method set_optimizer(AI::MXNet::Optimizer $optimizer)

lib/AI/MXNet/KVStore.pm  view on Meta::CPAN

}

=head2 create

    Create a new KVStore.

    Parameters
    ----------
    name : {'local'}
    The type of KVStore
        - local works for multiple devices on a single machine (single process)
        - dist works for multi-machines (multiple processes)
    Returns
    -------
    kv : KVStore
        The created AI::MXNet::KVStore
=cut

method create(Str $name='local')
{
    my $handle = check_call(AI::MXNetCAPI::KVStoreCreate($name));
    return __PACKAGE__->new(handle => $handle);

lib/AI/MXNet/Module/Base.pm  view on Meta::CPAN

    Parameters
    ----------
    $mon : AI::MXNet::Monitor
=cut

method install_monitor(AI::MXNet::Monitor $mon) { confess("NotImplemented") }

=head2 prepare

    Prepare the module for processing a data batch.

    Usually involves switching a bucket and reshaping.

    Parameters
    ----------
    $data_batch : AI::MXNet::DataBatch
=cut

method prepare(AI::MXNet::DataBatch $data_batch){}

lib/AI/MXNet/Optimizer.pm  view on Meta::CPAN

        Step size.
        Default value is set to 0.05.

    wd : float, optional
        L2 regularization coefficient add to all the weights

    rescale_grad : float, optional
        rescaling factor of gradient. Normally should be 1/batch_size.

    eps: float, optional
        A small float number to make the updating processing stable
        Default value is set to 1e-7.

    clip_gradient : float, optional
        clip gradient in range [-clip_gradient, clip_gradient]
=cut
package AI::MXNet::AdaGrad;
use Mouse;

extends 'AI::MXNet::Optimizer';

t/test_io.t  view on Meta::CPAN

{
    GetCifar10();
    my $dataiter = mx->io->ImageRecordIter({
            path_imgrec => "data/cifar/train.rec",
            mean_img => "data/cifar/cifar10_mean.bin",
            rand_crop => 0,
            and_mirror => 0,
            shuffle => 0,
            data_shape => [3,28,28],
            batch_size => 100,
            preprocess_threads => 4,
            prefetch_buffer => 1
    });
    my @labelcount;
    my $batchcount = 0;
    while(my $batch = <$dataiter>)
    {
        my $nplabel = $batch->label->[0];
        for my $i (0..$nplabel->shape->[0]-1)
        {
            $labelcount[int($nplabel->at($i)->asscalar)] += 1;