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July 27, 2019 00:48
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#!/usr/bin/env perl | |
# Licensed to the Apache Software Foundation (ASF) under one | |
# or more contributor license agreements. See the NOTICE file | |
# distributed with this work for additional information | |
# regarding copyright ownership. The ASF licenses this file | |
# to you under the Apache License, Version 2.0 (the | |
# "License"); you may not use this file except in compliance | |
# with the License. You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, | |
# software distributed under the License is distributed on an | |
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | |
# KIND, either express or implied. See the License for the | |
# specific language governing permissions and limitations | |
# under the License. | |
use strict; | |
use warnings; | |
use AI::MXNet qw(mx); | |
use AI::MXNet::Gluon qw(gluon); | |
use AI::MXNet::AutoGrad qw(autograd); | |
use AI::MXNet::Gluon::NN qw(nn); | |
use AI::MXNet::Base; | |
use Getopt::Long qw(HelpMessage); | |
GetOptions( | |
'lr=f' => \(my $lr = 0.1), | |
'log-interval=i' => \(my $log_interval = 100), | |
'momentum=f' => \(my $momentum = 0.9), | |
'hybridize=i' => \(my $hybridize = 0 ), | |
'cuda=i' => \(my $cuda = 0 ), | |
'load_params=i' => \(my $load_params = 0 ), | |
'batch-size=i' => \(my $batch_size = 100), | |
'epochs=i' => \(my $epochs = 1 ), | |
'help' => sub { HelpMessage(0) }, | |
) or HelpMessage(1); | |
# define network | |
my $net = nn->Sequential(); | |
$net->name_scope(sub { | |
$net->add(nn->Dense(128, activation=>'relu')); | |
$net->add(nn->Dense(64, activation=>'relu')); | |
$net->add(nn->Dense(2)); | |
}); | |
$net->hybridize() if $hybridize; | |
$net->load_parameters('mnist.params') if $load_params; | |
# data | |
sub transformer | |
{ | |
my ($data, $label) = @_; | |
$data = $data->reshape([-1])->astype('float32')/255; | |
#my $label = $label_tmp->at(0); | |
return ($data, $label); | |
} | |
my $train_data = gluon->data->DataLoader( | |
#gluon->data->vision->MNIST('./data', train=>1, transform => \&transformer), | |
gluon->data->vision->ImageFolderDataset(root => '~/cart/training/train', flag => 0, transform => \&transformer), | |
batch_size=>$batch_size, shuffle=>1, last_batch=>'discard' | |
); | |
my $val_data = gluon->data->DataLoader( | |
#gluon->data->vision->MNIST('./data', train=>0, transform=> \&transformer), | |
gluon->data->vision->ImageFolderDataset(root => '~/cart/training/test', flag => 0, transform => \&transformer), | |
batch_size=>$batch_size, shuffle=>0 | |
); | |
# train | |
sub test | |
{ | |
my $ctx = shift; | |
my $metric = mx->metric->Accuracy(); | |
while(defined(my $d = <$val_data>)) | |
{ | |
my ($data, $label) = @$d; | |
$data = $data->as_in_context($ctx); | |
$label = $label->as_in_context($ctx); | |
my $output = $net->($data); | |
$metric->update([$label], [$output]); | |
} | |
return $metric->get; | |
} | |
sub train | |
{ | |
my ($epochs, $ctx) = @_; | |
# Collect all parameters from net and its children, then initialize them. | |
$net->initialize(mx->init->Xavier(magnitude=>2.24), ctx=>$ctx); | |
# Trainer is for updating parameters with gradient. | |
my $trainer = gluon->Trainer($net->collect_params(), 'sgd', { learning_rate => $lr, momentum => $momentum }); | |
my $metric = mx->metric->Accuracy(); | |
my $loss = gluon->loss->SoftmaxCrossEntropyLoss(); | |
for my $epoch (0..$epochs-1) | |
{ | |
# reset data iterator and metric at begining of epoch. | |
$metric->reset(); | |
enumerate(sub { | |
my ($i, $d) = @_; | |
my ($data, $label) = @$d; | |
$data = $data->as_in_context($ctx); | |
$label = $label->as_in_context($ctx); | |
# Start recording computation graph with record() section. | |
# Recorded graphs can then be differentiated with backward. | |
my $output; | |
autograd->record(sub { | |
$output = $net->($data); | |
my $L = $loss->($output, $label); | |
$L->backward; | |
}); | |
# take a gradient step with batch_size equal to data.shape[0] | |
$trainer->step($data->shape->[0]); | |
# update metric at last. | |
$metric->update([$label], [$output]); | |
if($i % $log_interval == 0 and $i > 0) | |
{ | |
my ($name, $acc) = $metric->get(); | |
print "[Epoch $epoch Batch $i] Training: $name=$acc\n"; | |
} | |
}, \@{ $train_data }); | |
my ($name, $acc) = $metric->get(); | |
print "[Epoch $epoch] Training: $name=$acc\n"; | |
my ($val_name, $val_acc) = test($ctx); | |
print "[Epoch $epoch] Validation: $val_name=$val_acc\n" | |
} | |
$net->save_parameters('mnist.params'); | |
} | |
train($epochs, $cuda ? mx->gpu(0) : mx->cpu); |
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