def run(args):
dataset = AlderleyWrapper()
save_args(args) # save command line to a file for reference
train_loader = alderley_cgan_data_loader(args, dataset=dataset) # get the data
model = CGANModel(
args,
discriminators=[PixelDiscriminator(), patchCDiscriminatorNetwork(args)],
generator=UnetUpsample())
# Build trainer
trainer = Trainer(model)
trainer.build_criterion(CWGANDiscriminatorLoss(penalty_weight=args.penalty_weight, model=model))
trainer.build_optimizer('Adam', [parameter for discriminator in model.discriminators for parameter in discriminator.parameters()], lr=args.discriminator_lr)
trainer.save_every((1, 'epochs'))
trainer.save_to_directory(args.save_directory)
trainer.set_max_num_epochs(args.epochs)
trainer.register_callback(CGenerateDataCallback(args, dataset=dataset))
trainer.register_callback(CGeneratorTrainingCallback(
args,
parameters=model.generator.parameters(),
criterion=WGANGeneratorLoss(), dataset=dataset))
trainer.bind_loader('train', train_loader, num_inputs=2)
# Custom logging configuration so it knows to log our images
if args.cuda:
trainer.cuda()
# Go!
trainer.fit()
def test_multi_gpu(self):
import torch
if not torch.cuda.is_available():
return
from inferno.trainers.basic import Trainer
from inferno.io.box.cifar10 import get_cifar10_loaders
import os
# Make model
net = self._make_test_model()
# Make trainer
trainer = Trainer(model=net) \
.build_optimizer('Adam') \
.build_criterion('CrossEntropyLoss') \
.build_metric('CategoricalError') \
.validate_every((1, 'epochs')) \
.save_every((1, 'epochs'), to_directory=os.path.join(self.ROOT_DIR, 'saves')) \
.save_at_best_validation_score() \
.set_max_num_epochs(2)\
.cuda(devices=[0, 1, 2, 3])
train_loader, validate_loader = get_cifar10_loaders(
root_directory=self.ROOT_DIR, download=True)
trainer.bind_loader('train', train_loader)
trainer.bind_loader('validate', validate_loader)
trainer.fit()
def test_cifar(self):
from inferno.trainers.basic import Trainer
from inferno.io.box.cifar10 import get_cifar10_loaders
# Build cifar10 loaders
trainloader, testloader = get_cifar10_loaders(
root_directory=join(self.ROOT_DIR, 'data'),
download=self.DOWNLOAD_CIFAR)
# Make model
net = self._make_test_model()
tic = time.time()
# Make trainer
trainer = Trainer(model=net)\
.build_optimizer('Adam')\
.build_criterion('CrossEntropyLoss')\
.build_metric('CategoricalError')\
.validate_every((1, 'epochs'))\
.save_every((1, 'epochs'), to_directory=join(self.ROOT_DIR, 'saves'))\
.save_at_best_validation_score()\
.set_max_num_epochs(2)
# Bind trainer to datasets
trainer.bind_loader('train',
trainloader).bind_loader('validate', testloader)
# Check device and fit
if self.CUDA:
if self.HALF_PRECISION:
trainer.cuda().set_precision('half').fit()
else:
trainer.cuda().fit()
else:
trainer.fit()
toc = time.time()
print("[*] Elapsed time: {} seconds.".format(toc - tic))
def train_net_with_inferno(config_dict, net, criterion, optimizer, trainloader,
valloader):
"""
Trains the NeuralNet with inferno
:param config_dict: dict with configs
:param net: NeuralNet
:param criterion: criterion for NN
:param optimizer: optimizer for NN
:param trainloader: dataloader with traind ata
:param valloader: dataloader with validation data
"""
print("Start training with inferno!")
from inferno.trainers.basic import Trainer
from inferno.trainers.callbacks.essentials import SaveAtBestValidationScore
model_folder = os.path.join(config_dict["project_folder"], "model/")
if not os.path.exists(model_folder):
os.mkdir(model_folder)
trainer = Trainer(net) \
.save_every((1, 'epochs'), to_directory=model_folder) \
.build_criterion(criterion) \
.build_optimizer(optimizer) \
.build_metric(sorensen_dice_metric) \
.evaluate_metric_every('never') \
.validate_every((1, 'epochs'), for_num_iterations=50) \
.register_callback(SaveAtBestValidationScore(smoothness=.5))
trainer.set_max_num_epochs(config_dict['max_train_epochs'])
trainer.bind_loader('train',
trainloader).bind_loader('validate', valloader)
trainer.cuda()
trainer.fit()
def test_serialization(self):
trainer = self.get_trainer(3)
# Serialize
trainer.save()
# Unserialize
trainer = Trainer().load(os.path.join(self.ROOT_DIR, 'saves'))
train_loader, test_loader = self.get_random_dataloaders(
input_channels=3)
trainer.bind_loader('train',
train_loader).bind_loader('validate', test_loader)
trainer.fit()
def test_serialization(self):
if not hasattr(self, 'trainer'):
self.setUp()
# Serialize
self.trainer.save()
# Unserialize
trainer = Trainer().load(os.path.join(self.ROOT_DIR, 'saves'))
train_loader, test_loader = \
get_cifar10_loaders(root_directory=os.path.join(self.ROOT_DIR, 'data'),
download=self.DOWNLOAD_CIFAR)
trainer.bind_loader('train', train_loader).bind_loader('validate', test_loader)
trainer.fit()
trainer.print("Inspect logs at: {}".format(self.trainer.log_directory))
def task(env):
dataset_name = 'MNIST'
dataset_path = env.dataset(dataset_name)
batch_size = 128
transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
mnist_set = torchvision.datasets.MNIST(str(dataset_path), train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(mnist_set, batch_size=batch_size,
shuffle=True, num_workers=2)
mnist_test_set = torchvision.datasets.MNIST(str(dataset_path), train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(mnist_test_set, batch_size=512,
shuffle=True, num_workers=2)
model = Model(n_classes=10, in_channels=1, layers=4)
exp = TrainLog(env, dataset_name,model,log_time=True)
logging.info(' saving to %s', exp.save_directory)
logging.info(' logging to %s', exp.log_directory)
# Load loaders
# train_loader, validate_loader = get_cifar10_loaders(DATASET_DIRECTORY,
# download=DOWNLOAD_CIFAR)
# Build trainer
iterations = 5000
trainer = Trainer(model) \
.build_criterion('NLLLoss') \
.build_metric('CategoricalError') \
.build_optimizer('Adam', lr=0.001) \
.save_every((1, 'epochs')) \
.save_to_directory(str(exp.save_directory)) \
.validate_every((1, 'epochs'))\
.set_max_num_iterations(iterations) \
.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
log_images_every='never'),
log_directory=str(exp.log_directory))
# Bind loaders
trainer.bind_loader('train', train_loader)
trainer.bind_loader('validate', test_loader)
trainer.cuda()
# Go!
logging.info('start training')
trainer.fit()
trainer.set_max_num_iterations(trainer.iteration_count + iterations)
trainer.build_optimizer('Adam',lr=0.0001)
logging.info('slower lr')
trainer.fit()
def train_model(args):
"""
Performs the training
"""
if os.path.exists(
os.path.join(args.save_directory,
Trainer()._checkpoint_filename)):
# Skip training if checkpoint exists
return
model = WsjModel(args)
kwargs = {
'num_workers': args.num_workers,
'pin_memory': True
} if args.cuda else {}
train_loader = DataLoader(WsjDataset('train', args),
shuffle=True,
batch_size=args.batch_size,
collate_fn=wsj_collate_fn,
**kwargs)
validate_loader = DataLoader(WsjDataset('dev', args),
shuffle=True,
batch_size=args.batch_size,
collate_fn=wsj_collate_fn,
**kwargs)
# Build trainer
trainer = Trainer(model) \
.build_criterion(ctc_loss()) \
.build_metric(ctc_loss()) \
.build_optimizer('Adam') \
.validate_every((1, 'epochs')) \
.save_every((1, 'epochs')) \
.save_to_directory(args.save_directory) \
.set_max_num_epochs(args.epochs) \
#.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
#log_images_every='never'),
#log_directory=args.save_directory)
# Bind loaders
trainer.bind_loader('train', train_loader, num_inputs=3)
trainer.bind_loader('validate', validate_loader, num_inputs=3)
trainer.register_callback(EpochTimer())
if args.cuda:
trainer.cuda()
# Go!
trainer.fit()
trainer.save()
def my_train(load, folder):
click.echo('starting training')
os.makedirs(folder, exist_ok=True)
# joint_transform = Compose(
# RandomRotate(),
# RandomTranspose(),
# RandomFlip()
# )
# setup logger
os.makedirs('derived_data/log', exist_ok=True)
Logger.instance().setup('derived_data/log')
vae = Vae()
ds = HemoDataset(root_folder=root_folder, image_transform=None, training=True)
train_loader = torch.utils.data.DataLoader(ds, batch_size=1536, num_workers=8)
# Build trainer
trainer = Trainer(vae)
trainer.save_to_directory(folder)
if load:
trainer.load()
# trainer.cuda(devices=[0, 1])
trainer.cuda()
trainer.build_criterion(vae.loss_function())
trainer.build_optimizer('Adam', lr=0.001)
# trainer.validate_every((2, 'epochs'))
trainer.save_every((1, 'epochs'))
trainer.set_max_num_epochs(100)
trainer.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
log_images_every='never',
# log_images_every=(1, 'iteration'),
log_directory=folder))
# Bind loaders
trainer.bind_loader('train', train_loader, num_inputs=1, num_targets=1)
# bind callbacks
trainer.register_callback(GarbageCollection(), trigger='end_of_training_iteration')
trainer.register_callback(ShowMinimalConsoleInfo(), trigger='end_of_training_iteration')
# trainer.bind_loader('train', train_loader, num_inputs=3, num_targets=1)
trainer.fit()
pushover_notification.send('embeddings generated')
def run(args):
dataset = RorschachWrapper()
save_args(args) # save command line to a file for reference
train_loader = rorschach_cgan_data_loader(args,
dataset=dataset) # get the data
# todo
model = patchCWGANModel(args,
discriminator=patchCDiscriminatorNetwork(args),
generator=CGeneratorNetwork(args))
# Build trainer
trainer = Trainer(model)
trainer.build_criterion(
CWGANDiscriminatorLoss(penalty_weight=args.penalty_weight,
model=model))
trainer.build_optimizer('Adam',
model.discriminator.parameters(),
lr=args.discriminator_lr)
trainer.save_every((1, 'epochs'))
trainer.save_to_directory(args.save_directory)
trainer.set_max_num_epochs(args.epochs)
trainer.register_callback(CGenerateDataCallback(args, dataset=dataset))
trainer.register_callback(
CGeneratorTrainingCallback(args,
parameters=model.generator.parameters(),
criterion=WGANGeneratorLoss(),
dataset=dataset))
trainer.bind_loader('train', train_loader, num_inputs=2)
# Custom logging configuration so it knows to log our images
logger = TensorboardLogger(log_scalars_every=(1, 'iteration'),
log_images_every=(args.log_image_frequency,
'iteration'))
trainer.build_logger(logger, log_directory=args.save_directory)
logger.observe_state('generated_images')
logger.observe_state('real_images')
logger._trainer_states_being_observed_while_training.remove(
'training_inputs')
if args.cuda:
trainer.cuda()
# Go!
trainer.fit()
def run(args):
model = SpeechModel(args)
trainer = Trainer()
if os.path.exists(
os.path.join(args.save_directory, trainer._checkpoint_filename)):
trainer.load(from_directory=args.save_directory)
model.load_state_dict(trainer.model.state_dict())
if args.cuda:
model = model.cuda()
else:
train_loader = make_loader('train', args, batch_size=args.batch_size)
dev_loader = make_loader('dev', args, batch_size=args.batch_size)
# Build trainer
trainer = Trainer(model) \
.build_criterion(CTCCriterion, size_average=True) \
.build_optimizer('Adam', weight_decay=1e-7, lr=5e-5) \
.validate_every((1, 'epochs')) \
.save_every((1, 'epochs')) \
.save_to_directory(args.save_directory) \
.set_max_num_epochs(args.epochs) \
.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
log_images_every='never'),
log_directory=args.save_directory)
# Bind loaders
trainer.bind_loader('train', train_loader, num_inputs=3, num_targets=1)
trainer.bind_loader('validate',
dev_loader,
num_inputs=3,
num_targets=1)
if args.cuda:
trainer.cuda()
# Go!
trainer.fit()
trainer.save()
test_loader = make_loader('test',
args=args,
shuffle=False,
batch_size=1,
test=True)
run_logits(loader=test_loader, model=model, args=args)
def train_model(args):
"""
Perform training then call prediction
"""
model = all_cnn_module()
model.apply(initializer)
train_loader, validate_loader, test_loader = make_loaders(args)
# Build trainer
savepath = os.path.join(args.save_directory,
Trainer()._checkpoint_filename)
if os.path.exists(savepath):
trainer = Trainer().load(from_directory=args.save_directory)
if args.cuda:
trainer.cuda()
else:
trainer = Trainer(model) \
.build_criterion('CrossEntropyLoss') \
.build_metric('CategoricalError') \
.save_every((1, 'epochs')) \
.validate_every((1, 'epochs')) \
.save_to_directory(args.save_directory) \
.set_max_num_epochs(args.epochs) \
.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
log_images_every='never'),
log_directory=args.save_directory)
# These are the params from the paper
trainer.build_optimizer('SGD',
lr=0.01,
momentum=0.9,
weight_decay=0.001)
# Also works with Adam and default settings
# trainer.build_optimizer('Adam')
trainer.bind_loader('train', train_loader)
trainer.bind_loader('validate', validate_loader)
if args.cuda:
trainer.cuda()
# Go!
trainer.fit()
trainer.save()
write_predictions(args, trainer.model, test_loader)
def run(args):
save_args(args) # save command line to a file for reference
train_loader = mnist_data_loader(args) # get the data
model = GANModel(args,
discriminator=DiscriminatorNetwork(args),
generator=GeneratorNetwork(args))
# Build trainer
trainer = Trainer(model)
trainer.build_criterion(
WGANDiscriminatorLoss(penalty_weight=args.penalty_weight, model=model))
trainer.build_optimizer('Adam',
model.discriminator.parameters(),
lr=args.discriminator_lr)
trainer.save_every((1, 'epochs'))
trainer.save_to_directory(args.save_directory)
trainer.set_max_num_epochs(args.epochs)
trainer.register_callback(GenerateDataCallback(args))
trainer.register_callback(
GeneratorTrainingCallback(args,
parameters=model.generator.parameters(),
criterion=WGANGeneratorLoss()))
trainer.bind_loader('train', train_loader)
# Custom logging configuration so it knows to log our images
logger = TensorboardLogger(log_scalars_every=(1, 'iteration'),
log_images_every=(args.log_image_frequency,
'iteration'))
trainer.build_logger(logger, log_directory=args.save_directory)
logger.observe_state('generated_images')
logger.observe_state('real_images')
logger._trainer_states_being_observed_while_training.remove(
'training_inputs')
if args.cuda:
trainer.cuda()
# Go!
trainer.fit()
# Generate video from saved images
if not args.no_ffmpeg:
generate_video(args.save_directory)
def main():
# Load dataset
train_loader = get_cremi_loaders(
config=join(PROJECT_DIRECTORY, 'Configurations', CONFIG_FILENAME))
# Build model
dense_unet = DUNet(1, 1)
# Build trainer
trainer = Trainer(model=dense_unet) \
.build_optimizer('Adam') \
.build_criterion('SorensenDiceLoss') \
.build_logger(TensorboardLogger(send_image_at_batch_indices=0,
send_image_at_channel_indices='all',
log_images_every=(20, 'iterations')),
log_directory=join(PROJECT_DIRECTORY, 'Logs')) \
.save_every((1000, 'iterations'), to_directory=join(PROJECT_DIRECTORY, 'Weights')) \
.set_max_num_iterations(1000000) \
.cuda()
# Bind loader to trainer
trainer.bind_loader('train', train_loader)
# Go!
trainer.fit()
def train(net, num_epochs, dataset):
data_loader = torch.utils.data.DataLoader(dataset,
shuffle=True,
batch_size=100)
net.train(mode=True)
for i in range(num_epochs):
lr = 0.002 / (1 + 0.2 * i)
trainer = Trainer(net) \
.build_criterion(SeqCrossEntropyLoss) \
.build_optimizer('Adam', lr=lr) \
.set_max_num_epochs(1) \
.save_every((10, 'iterations')) \
.save_to_directory('net/')
trainer.bind_loader('train', data_loader, num_inputs=3)
if torch.cuda.is_available():
trainer.cuda()
trainer.fit()
def get_trainer(self, input_channels):
# Build model
net = self._make_test_model(input_channels)
# Build trainer
trainer = Trainer(net)\
.build_logger(TensorboardLogger(send_image_at_batch_indices=0,
send_image_at_channel_indices='all',
log_images_every=(20, 'iterations')),
log_directory=self.LOG_DIRECTORY)\
.build_criterion('CrossEntropyLoss')\
.build_metric('CategoricalError')\
.build_optimizer('Adam')\
.validate_every((1, 'epochs'))\
.save_every((2, 'epochs'), to_directory=self.SAVE_DIRECTORY)\
.save_at_best_validation_score()\
.set_max_num_epochs(2)\
.set_precision(self.PRECISION)
# Bind loaders
train_loader, test_loader = self.get_random_dataloaders(
input_channels=input_channels)
trainer.bind_loader('train',
train_loader).bind_loader('validate', test_loader)
return trainer
class TestTensorboard(unittest.TestCase):
ROOT_DIR = os.path.dirname(__file__)
PRECISION = 'half'
DOWNLOAD_CIFAR = True
@staticmethod
def _make_test_model():
toy_net = nn.Sequential(nn.Conv2d(3, 128, 3, 1, 1),
nn.ELU(),
nn.MaxPool2d(2),
nn.Conv2d(128, 128, 3, 1, 1),
nn.ELU(),
nn.MaxPool2d(2),
nn.Conv2d(128, 256, 3, 1, 1),
nn.ELU(),
nn.AdaptiveMaxPool2d((1, 1)),
AsMatrix(),
nn.Linear(256, 10),
nn.Softmax())
return toy_net
def setUp(self):
# Build model
net = self._make_test_model()
# Build trainer
self.trainer = Trainer(net)\
.build_logger(TensorboardLogger(send_image_at_batch_indices=0,
send_image_at_channel_indices='all',
log_images_every=(20, 'iterations')),
log_directory=os.path.join(self.ROOT_DIR, 'logs'))\
.build_criterion('CrossEntropyLoss')\
.build_metric('CategoricalError')\
.build_optimizer('Adam')\
.validate_every((1, 'epochs'))\
.save_every((2, 'epochs'), to_directory=os.path.join(self.ROOT_DIR, 'saves'))\
.save_at_best_validation_score()\
.set_max_num_epochs(2)\
.cuda().set_precision(self.PRECISION)
# Load CIFAR10 data
train_loader, test_loader = \
get_cifar10_loaders(root_directory=os.path.join(self.ROOT_DIR, 'data'),
download=self.DOWNLOAD_CIFAR)
# Bind loaders
self.trainer.bind_loader('train', train_loader).bind_loader('validate', test_loader)
def test_tensorboard(self):
# Set up if required
if not hasattr(self, 'trainer'):
self.setUp()
# Train
self.trainer.fit()
# Print info for check
self.trainer.print("Inspect logs at: {}".format(self.trainer.log_directory))
def test_serialization(self):
if not hasattr(self, 'trainer'):
self.setUp()
# Serialize
self.trainer.save()
# Unserialize
trainer = Trainer().load(os.path.join(self.ROOT_DIR, 'saves'))
train_loader, test_loader = \
get_cifar10_loaders(root_directory=os.path.join(self.ROOT_DIR, 'data'),
download=self.DOWNLOAD_CIFAR)
trainer.bind_loader('train', train_loader).bind_loader('validate', test_loader)
trainer.fit()
trainer.print("Inspect logs at: {}".format(self.trainer.log_directory))
def train_model(args):
model = fm.model_fn(args)
print('Done initializing model')
# labels_all_dict = np.load('labels_all_dict.npy')
labels_all_dict = np.load('labels_all_dict_10s_subset_500.npy')
if args.no_val:
file_names_train = np.load(
'file_names_train_notest_hop_subset_500.npy')
# file_names_train = np.load('file_names_train_notest.npy')
train_dataloader = fd.create_dataloader(file_names_train,
args.data_directory,
labels_all_dict.item(), args)
else:
file_names_train = np.load('file_names_train.npy')
file_names_test = np.load('file_names_test.npy')
train_dataloader = fd.create_dataloader(file_names_train,
args.data_directory,
labels_all_dict.item(), args)
dev_dataloader = fd.create_dataloader(file_names_test,
args.data_directory,
labels_all_dict.item(), args)
# train_dataloader = fd.create_dataloader(train_feats, train_labels, args)
# dev_dataloader = fd.create_dataloader(dev_feats, dev_labels, args)
test_dataloader_flag = False
if test_dataloader_flag:
it = 0
for x, y, z in train_dataloader:
print('x.shape: {}'.format(x.shape))
print('y.shape: {}'.format(y.shape))
print('z.shape: {}'.format(z.shape))
print('x: {}'.format(x))
print('y: {}'.format(y))
print('z: {}'.format(z))
it = it + 1
print(it)
print('Done Creating Data Loaders')
if args.load_model:
(model, trainer) = load_model(args)
trainer \
.bind_loader('train', train_dataloader, num_inputs=2, num_targets=1)
if not args.no_val:
trainer.bind_loader('validate',
dev_dataloader,
num_inputs=2,
num_targets=1)
else:
# Build trainer
loss_fn = cust_CrossEntropyLoss()
trainer = Trainer(model) \
.build_criterion(loss_fn) \
.build_metric('CategoricalError') \
.build_optimizer(torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)) \
.save_every((1, 'epochs')) \
.save_to_directory(args.save_directory) \
.set_max_num_epochs(args.epochs) \
.build_logger(TensorboardLogger(log_scalars_every=(1, 'iteration'),
log_images_every='never'),
log_directory=args.save_directory)
if not args.no_val:
trainer \
.save_at_best_validation_score(True) \
.validate_every((1, 'epochs'))
trainer \
.bind_loader('train', train_dataloader, num_inputs=2, num_targets=1)
if not args.no_val:
trainer.bind_loader('validate',
dev_dataloader,
num_inputs=2,
num_targets=1)
# .build_optimizer('Adam') \
# .save_at_best_validation_score(True) \
# .build_metric(LevenshteinMetric) \
# .evaluate_metric_every('300 iterations') \
if args.cuda:
print('Switch trainer to CUDA')
trainer.cuda()
trainer.fit()
print('Done Fitting Trainer')
return model
trainer = Trainer(model)
trainer.build_criterion('CrossEntropyLoss')
trainer.build_metric('CategoricalError')
trainer.build_optimizer('Adam')
trainer.validate_every((2, 'epochs'))
trainer.save_every((5, 'epochs'))
trainer.save_to_directory(SAVE_DIRECTORY)
trainer.set_max_num_epochs(10)
trainer.build_logger(logger, log_directory=LOG_DIRECTORY)
trainer.set_log_directory(LOG_DIRECTORY)
##################################################
# Bind loaders
trainer.bind_loader('train', train_loader)
trainer.bind_loader('validate', validate_loader)
##################################################
from inferno.trainers.callbacks.base import Callback
class SawtoothLearningrate(Callback):
"""oscillating learning rate"""
def __init__(self, min_value, max_value, frequency):
super(SawtoothLearningrate, self).__init__()
self.min_value = min_value
self.max_value = max_value
self.frequency = frequency
max_num_iterations = 10000
trainer = Trainer(model) \
.build_criterion('NLLLoss') \
.build_metric('CategoricalError') \
.build_optimizer('Adam') \
.save_every((1, 'epochs')) \
.save_to_directory(str(exp.save_directory))\
.validate_every((2, 'epochs'))\
.set_max_num_epochs(100) \
.build_logger(TensorboardLogger(log_scalars_every=(1, 'iterations'),
log_images_every='never'),
log_directory=str(exp.log_directory))
# .save_every((2000, 'iterations'), to_directory=str(exp.save_directory), checkpoint_filename='latest') \
# Bind loaders
trainer.bind_loader('train', train_loader)
trainer.bind_loader('validate', test_loader)
trainer.cuda()
# Go!
trainer.fit()
# TODO: train different max pool sizes
# TODO: train different kernel numbers
# TODO: train different conv layers
# TODO: train with different number of training images
# TODO: report run time
