NAME

    AI::MXNet::Vizualization - Vizualization support for Perl interface to MXNet machine learning library

SYNOPSIS

    use strict;
    use warnings;
    use AI::MXNet qw(mx);

    ### model
    my $data = mx->symbol->Variable('data');
    my $conv1= mx->symbol->Convolution(data => $data, name => 'conv1', num_filter => 32, kernel => [3,3], stride => [2,2]);
    my $bn1  = mx->symbol->BatchNorm(data => $conv1, name => "bn1");
    my $act1 = mx->symbol->Activation(data => $bn1, name => 'relu1', act_type => "relu");
    my $mp1  = mx->symbol->Pooling(data => $act1, name => 'mp1', kernel => [2,2], stride =>[2,2], pool_type=>'max');

    my $conv2= mx->symbol->Convolution(data => $mp1, name => 'conv2', num_filter => 32, kernel=>[3,3], stride=>[2,2]);
    my $bn2  = mx->symbol->BatchNorm(data => $conv2, name=>"bn2");
    my $act2 = mx->symbol->Activation(data => $bn2, name=>'relu2', act_type=>"relu");
    my $mp2  = mx->symbol->Pooling(data => $act2, name => 'mp2', kernel=>[2,2], stride=>[2,2], pool_type=>'max');


    my $fl   = mx->symbol->Flatten(data => $mp2, name=>"flatten");
    my $fc1  = mx->symbol->FullyConnected(data => $fl,  name=>"fc1", num_hidden=>30);
    my $act3 = mx->symbol->Activation(data => $fc1, name=>'relu3', act_type=>"relu");
    my $fc2  = mx->symbol->FullyConnected(data => $act3, name=>'fc2', num_hidden=>10);
    my $softmax = mx->symbol->SoftmaxOutput(data => $fc2, name => 'softmax');

    ## creates the image file working directory
    mx->viz->plot_network($softmax, save_format => 'png')->render("network.png");

DESCRIPTION

     Vizualization support for Perl interface to MXNet machine learning library

Class methods

print_summary

    convert symbol for detail information

    Parameters
    ----------
    symbol: AI::MXNet::Symbol
        symbol to be visualized
    shape: hashref
        hashref of shapes, str->shape (arrayref[int]), given input shapes
    line_length: int
        total length of printed lines
    positions: arrayref[float]
        relative or absolute positions of log elements in each line
    Returns
    ------
        nothing

plot_network

    convert symbol to dot object for visualization

    Parameters
    ----------
    title: str
        title of the dot graph
    symbol: AI::MXNet::Symbol
        symbol to be visualized
    shape: HashRef[Shape]
        If supplied, the visualization will include the shape
        of each tensor on the edges between nodes.
    node_attrs: HashRef of node's attributes
        for example:
            {shape => "oval",fixedsize => "false"}
            means to plot the network in "oval"
    hide_weights: Bool
        if True (default) then inputs with names like `*_weight`
        or `*_bias` will be hidden

    Returns
    ------
    dot: Diagraph
        dot object of symbol