AI-MXNet
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examples/mnist.pl view on Meta::CPAN
#!/usr/bin/perl
use strict;
use warnings;
# derived from http://mxnet.io/tutorials/python/mnist.html
use LWP::UserAgent ();
use PDL ();
#use Gtk2 '-init';
use AI::MXNet ('mx');
my $ua = LWP::UserAgent->new();
sub download_data {
my($url, $force_download) = @_;
$force_download = 1 if @_ < 2;
my $fname = (split m{/}, $url)[-1];
if($force_download or not -f $fname) {
$ua->get($url, ':content_file' => $fname);
}
return $fname;
}
sub read_data {
my($label_url, $image_url) = @_;
my($magic, $num, $rows, $cols);
open my($flbl), '<:gzip', download_data($label_url);
read $flbl, my($buf), 8;
($magic, $num) = unpack 'N2', $buf;
my $label = PDL->new();
$label->set_datatype($PDL::Types::PDL_B);
$label->setdims([ $num ]);
read $flbl, ${$label->get_dataref}, $num;
$label->upd_data();
open my($fimg), '<:gzip', download_data($image_url);
read $fimg, $buf, 16;
($magic, $num, $rows, $cols) = unpack 'N4', $buf;
my $image = PDL->new();
$image->set_datatype($PDL::Types::PDL_B);
$image->setdims([ $rows, $cols, $num ]);
read $fimg, ${$image->get_dataref}, $num * $rows * $cols;
$image->upd_data();
return($label, $image);
}
my $path='http://yann.lecun.com/exdb/mnist/';
my($train_lbl, $train_img) = read_data(
"${path}train-labels-idx1-ubyte.gz", "${path}train-images-idx3-ubyte.gz");
my($val_lbl, $val_img) = read_data(
"${path}t10k-labels-idx1-ubyte.gz", "${path}t10k-images-idx3-ubyte.gz");
sub show_sample {
print 'label: ', $train_lbl->slice('0:9'), "\n";
my $hbox = Gtk2::HBox->new(0, 2);
for my $i (0 .. 9) {
my $img = $train_img->slice(":,:,$i");
my($w, $h) = $img->dims;
$img->make_physical();
# ugh, pixbufs don't have a grayscale colorspace?!
# burst it to rgb I guess.
my $data = pack 'c*', map { $_, $_, $_ } unpack 'c*', ${$img->get_dataref};
$hbox->add(Gtk2::Image->new_from_pixbuf(
Gtk2::Gdk::Pixbuf->new_from_data($data, 'rgb', 0, 8, $w, $h, $w * 3)
));
}
my $win = Gtk2::Window->new('toplevel');
$win->signal_connect(delete_event => sub { Gtk2->main_quit() });
$win->add($hbox);
$win->show_all();
Gtk2->main();
}
sub show_network {
my($viz) = @_;
my $load = Gtk2::Gdk::PixbufLoader->new();
$load->write($viz->graph->as_png);
$load->close();
my $img = Gtk2::Image->new_from_pixbuf($load->get_pixbuf());
my $sw = Gtk2::ScrolledWindow->new(undef, undef);
$sw->add_with_viewport($img);
my $win = Gtk2::Window->new('toplevel');
$win->signal_connect(delete_event => sub { Gtk2->main_quit() });
$win->add($sw);
$win->show_all();
Gtk2->main();
}
#show_sample();
sub to4d {
my($img) = @_;
return $img->reshape(28, 28, 1, ($img->dims)[2])->float / 255;
}
my $batch_size = 100;
my $train_iter = mx->io->NDArrayIter(
data => to4d($train_img),
label => $train_lbl,
batch_size => $batch_size,
shuffle => 1,
);
my $val_iter = mx->io->NDArrayIter(
data => to4d($val_img),
label => $val_lbl,
batch_size => $batch_size,
);
# Create a place holder variable for the input data
my $data = mx->sym->Variable('data');
sub nn_fc {
# Epoch[9] Train-accuracy=0.978889
# Epoch[9] Time cost=145.437
# Epoch[9] Validation-accuracy=0.964600
my($data) = @_;
# Flatten the data from 4-D shape (batch_size, num_channel, width, height)
# into 2-D (batch_size, num_channel*width*height)
$data = mx->sym->Flatten(data => $data);
# The first fully-connected layer
# my $fc1 = mx->sym->FullyConnected(data => $data, name => 'fc1', num_hidden => 128);
# # Apply relu to the output of the first fully-connnected layer
# my $act1 = mx->sym->Activation(data => $fc1, name => 'relu1', act_type => "relu");
# The second fully-connected layer and the according activation function
my $fc2 = mx->sym->FullyConnected(data => $data, name => 'fc2', num_hidden => 64);
my $act2 = mx->sym->Activation(data => $fc2, name => 'relu2', act_type => "relu");
# The thrid fully-connected layer, note that the hidden size should be 10, which is the number of unique digits
my $fc3 = mx->sym->FullyConnected(data => $act2, name => 'fc3', num_hidden => 10);
# The softmax and loss layer
my $mlp = mx->sym->SoftmaxOutput(data => $fc3, name => 'softmax');
return $mlp;
}
sub nn_conv {
my($data) = @_;
# Epoch[9] Batch [200] Speed: 1625.07 samples/sec Train-accuracy=0.992090
# Epoch[9] Batch [400] Speed: 1630.12 samples/sec Train-accuracy=0.992850
# Epoch[9] Train-accuracy=0.991357
# Epoch[9] Time cost=36.817
# Epoch[9] Validation-accuracy=0.988100
my $conv1= mx->symbol->Convolution(data => $data, name => 'conv1', num_filter => 20, kernel => [5,5], 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 =>[1,1], pool_type=>'max');
my $conv2= mx->symbol->Convolution(data => $mp1, name => 'conv2', num_filter => 50, 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=>[1,1], pool_type=>'max');
my $fl = mx->symbol->Flatten(data => $mp2, name=>"flatten");
my $fc1 = mx->symbol->FullyConnected(data => $fl, name=>"fc1", num_hidden=>100);
my $act3 = mx->symbol->Activation(data => $fc1, name=>'relu3', act_type=>"relu");
my $fc2 = mx->symbol->FullyConnected(data => $act3, name=>'fc2', num_hidden=>30);
my $act4 = mx->symbol->Activation(data => $fc2, name=>'relu4', act_type=>"relu");
my $fc3 = mx->symbol->FullyConnected(data => $act4, name=>'fc3', num_hidden=>10);
my $softmax = mx->symbol->SoftmaxOutput(data => $fc3, name => 'softmax');
return $softmax;
}
my $mlp = $ARGV[0] ? nn_conv($data) : nn_fc($data);
#We visualize the network structure with output size (the batch_size is ignored.)
#my $shape = { data => [ $batch_size, 1, 28, 28 ] };
#show_network(mx->viz->plot_network($mlp, shape => $shape));
my $model = mx->mod->Module(
symbol => $mlp, # network structure
);
$model->fit(
$train_iter, # training data
num_epoch => 10, # number of data passes for training
eval_data => $val_iter, # validation data
batch_end_callback => mx->callback->Speedometer($batch_size, 200), # output progress for each 200 data batches
optimizer => 'adam',
);
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