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