xs, ts = batch[:-1], batch[-1]
evaluator.on_batch_begin({'train': False, 'xs': xs, 'ts': ts})
model(*xs)
evaluator.on_batch_end({'train': False, 'xs': xs, 'ts': ts})
evaluator.on_epoch_validate_end({'epoch': 0})
if __name__ == "__main__":
Log.AppLogger.configure(mkdir=True)
App.add_command(
'decode', decode, {
'gpu':
arg('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)'),
'model_file':
arg('--modelfile',
type=str,
required=True,
help='Trained model archive file'),
'save_to':
arg('--out',
type=str,
default=None,
help='Save results to the specified directory'),
'target_file':
arg('--targetfile',
type=str,
},
converter=lambda x: chainer.dataset.convert.to_device(device, x))
trainer.add_listener(
training.listeners.ProgressBar(lambda n: tqdm(total=n)), priority=200)
trainer.fit((train_x, train_y), (test_x, test_y), n_epoch, batch_size)
if __name__ == "__main__":
App.configure(name='chainer-mnist', logoption='d')
App.add_command(
'train',
train, {
'batch_size':
arg('--batchsize',
'-b',
type=int,
default=100,
help='Number of images in each mini-batch'),
'device':
arg('--device',
type=int,
default=-1,
metavar='ID',
help='Device ID (negative value indicates CPU)'),
'n_epoch':
arg('--epoch',
'-e',
type=int,
default=20,
help='Number of sweeps over the dataset to train'),
'n_units':
# App.configure(logdir=App.basedir + '/../logs')
if '--savedir' in sys.argv:
savedir_index = sys.argv.index('--savedir') + 1
savedir = sys.argv[savedir_index]
if '--modelfile' in sys.argv:
savedir_index = sys.argv.index('--modelfile') + 1
savedir = os.path.dirname(sys.argv[savedir_index])
App.configure(logdir=App.basedir + '/' + savedir)
logging.AppLogger.configure(mkdir=True)
App.add_command(
'train', train, {
'batch_size':
arg('--batchsize',
type=int,
default=20,
metavar='NUM',
help='Number of examples in each mini-batch'),
'cache_dir':
arg('--cachedir',
type=str,
default=(App.basedir + '/../cache'),
metavar='DIR',
help='Cache directory'),
'test_file':
arg('--devfile',
type=str,
default=None,
metavar='FILE',
help='Development data file'),
'device':
]))
print()
UAS, LAS, count = UAS + _uas, LAS + _las, count + _count
Log.i("[evaluation] UAS: {:.8f}, LAS: {:.8f}".format(
UAS / count * 100, LAS / count * 100))
if __name__ == "__main__":
Log.AppLogger.configure(mkdir=True)
App.add_command(
'train', train, {
'backend':
arg('--backend',
type=str,
choices=('chainer', 'pytorch'),
default='chainer',
help='Backend framework for computation'),
'batch_size':
arg('--batchsize',
'-b',
type=int,
default=32,
help='Number of examples in each mini-batch'),
'embed_file':
arg('--embedfile',
type=str,
default=None,
help='Pretrained word embedding file'),
'embed_size':
arg('--embedsize',
trainer = training.Trainer(optimizer,
model,
loss_func=compute_loss,
accuracy_func=compute_accuracy)
trainer.configure(framework_utils.config)
trainer.fit(train_dataset,
None,
batch_size=batch_size,
epochs=n_epoch,
validation_data=test_dataset,
verbose=App.verbose)
if __name__ == "__main__":
logging.AppLogger.configure(mkdir=True)
App.add_command(
'train', train, {
'train_file': arg('--trainfile', type=str, required=True),
'test_file': arg('--testfile', type=str),
'word_embed_file': arg('--embedfile', type=str),
'n_epoch': arg('--epoch', type=int, default=20),
'batch_size': arg('--batchsize', type=int, default=10),
'lr': arg('--lr', type=float, default=0.01),
'gpu': arg('--gpu', type=int, default=-1),
'seed': arg('--seed', type=int, default=1),
})
chainer.config.debug = False
chainer.config.type_check = False
App.run()
arc_mlp_units=kwargs.get('arc_mlp_units', 500),
rel_mlp_units=kwargs.get('rel_mlp_units', 100),
arc_mlp_dropout=kwargs.get('arc_mlp_dropout', dropout_ratio),
rel_mlp_dropout=kwargs.get('rel_mlp_dropout', dropout_ratio))
return parser
if __name__ == "__main__":
App.configure(logdir=App.basedir + '/../logs', loglevel='debug')
logging.AppLogger.configure(mkdir=True)
App.add_command(
'train', train, {
'batch_size':
arg('--batchsize',
type=int,
default=5000,
metavar='NUM',
help='Number of tokens in each mini-batch'),
'cache_dir':
arg('--cachedir',
type=str,
default=(App.basedir + '/../cache'),
metavar='DIR',
help='Cache directory'),
'test_file':
arg('--devfile',
type=str,
default=None,
metavar='FILE',
help='Development data file'),
'device':
loader=loader)))
# Start training
trainer.fit(train_dataset, None,
batch_size=batch_size,
epochs=n_epoch,
validation_data=test_dataset,
verbose=App.verbose)
if __name__ == "__main__":
Log.AppLogger.configure(mkdir=True)
App.add_command('train', train, {
'batch_size':
arg('--batchsize', '-b', type=int, default=32,
help='Number of examples in each mini-batch'),
'embed_file':
arg('--embedfile', type=str, default=None,
help='Pretrained word embedding file'),
'embed_size':
arg('--embedsize', type=int, default=100,
help='Size of embeddings'),
'gpu':
arg('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)'),
'grad_clip':
arg('--gradclip', type=float, default=5.0,
help='L2 norm threshold of gradient norm'),
'l2_lambda':
arg('--l2', type=float, default=0.0,
help='Strength of L2 regularization'),