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examples/mnist.pl  view on Meta::CPAN

    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',
);