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training.py
executable file
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training.py
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#!/usr/bin/env python
from __future__ import print_function
try:
import matplotlib
matplotlib.use('Agg')
except ImportError:
pass
import argparse
import chainer
import chainer.links as L
from chainer import training
from chainer.training import extensions
from original_models import VGG
from original_data import get_data
def main():
parser = argparse.ArgumentParser(description='Chainer example:cfiar-VGG')
parser.add_argument('--batchsize', '-b', type=int, default=64,
help='Number of images in each mini-batch')
parser.add_argument('--epoch', '-e', type=int, default=300,
help='Number of sweeps over the dataset to train')
parser.add_argument('--frequency', '-f', type=int, default=-1,
help='Frequency of taking a snapshot')
parser.add_argument('--gpu', '-g', type=int, default=0,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out', '-o', default='result',
help='Directory to output the result')
parser.add_argument('--resume', '-r', default='',
help='Resume the training from snapshot')
parser.add_argument('--learnrate', '-l', type=float, default=0.05,
help='Learning rate for SGD')
args = parser.parse_args()
print('GPU: {}'.format(args.gpu))
print('# Minibatch-size: {}'.format(args.batchsize))
print('# epoch: {}'.format(args.epoch))
print('')
train = get_data(r"/home/notsuji/chainer_src/origin/mnist_data/Data/train")
test = get_data(r"/home/notsuji/chainer_src/origin/mnist_data/Data/test")
# Setup model
model = L.Classifier(VGG(10))
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
# Setup an optimizer
optimizer = chainer.optimizers.MomentumSGD(args.learnrate)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(5e-4))
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test, args.batchsize, repeat=False, shuffle=False)
# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=args.gpu)
trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)
# Evaluate the model with the test dataset for each epoch
trainer.extend(extensions.Evaluator(test_iter, model, device=args.gpu), trigger=(1, 'epoch'))
# Reduce the learning rate by half every 25 epochs.
trainer.extend(extensions.ExponentialShift('lr', 0.5),trigger=(25, 'epoch'))
# Dump a computational graph from 'loss' variable at the first iteration
# The "main" refers to the target link of the "main" optimizer.
trainer.extend(extensions.dump_graph('main/loss'))
# Take a snapshot for each specified epoch
trainer.extend(extensions.snapshot(filename='snapshot_epoch_{.updater.epoch}'), trigger=(10, 'epoch'))
trainer.extend(
extensions.snapshot_object(model, 'model_epoch_{.updater.epoch}'),
trigger=(args.epoch, 'epoch'))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport())
# Save two plot images to the result dir
if extensions.PlotReport.available():
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch', file_name='loss.png'))
trainer.extend(
extensions.PlotReport(
['main/accuracy', 'validation/main/accuracy'],
'epoch', file_name='accuracy.png'))
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
if args.resume:
# Resume from a snapshot
chainer.serializers.load_npz(args.resume, trainer)
# Run the training
trainer.run()
if __name__ == '__main__':
main()