def test_simple_perceptron():
# Loading dataset
trainset, validset, testset = load_mnist()
# Creating model
nb_classes = 10
model = Perceptron(trainset.input_size, nb_classes)
model.initialize() # By default, uniform initialization.
# Building optimizer
loss = NLL(model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.1))
# Use mini batches of 100 examples.
batch_scheduler = MiniBatchScheduler(trainset, 100)
# Build trainer and add some tasks.
trainer = Trainer(optimizer, batch_scheduler)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print NLL mean/stderror.
nll = views.LossView(loss=NLL(model, validset),
batch_scheduler=FullBatchScheduler(validset))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.1%} ± {1:.1%}", nll.mean,
nll.stderror))
# Print mean/stderror of classification errors.
classif_error = views.LossView(
loss=ClassificationError(model, validset),
batch_scheduler=FullBatchScheduler(validset))
trainer.append_task(
tasks.Print("Validset - Classif error: {0:.1%} ± {1:.1%}",
classif_error.mean, classif_error.stderror))
# Train for 10 epochs (stopping criteria should be added at the end).
trainer.append_task(stopping_criteria.MaxEpochStopping(10))
trainer.train()
def _build_trainer(nb_epochs):
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(nb_epochs))
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
use_mask_as_input=use_mask_as_input)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize(initer.UniformInitializer(random_seed=1234))
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size)
trainer = Trainer(optimizer, batch_scheduler)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
nll = views.LossView(
loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(
validset, batch_size=len(validset), keep_mask=True))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}",
nll.mean, nll.stderror))
trainer.append_task(stopping_criteria.MaxEpochStopping(nb_epochs))
return trainer, nll, logger
def estimate_NLL(model, dataset, seed=1234, batch_size=None):
if batch_size is None:
batch_size = len(dataset)
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(model, dataset)
status = Status()
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
dataset,
batch_size=len(dataset),
use_mask_as_input=model.nb_channels == 2,
keep_mask=True,
seed=seed)
nll = views.LossView(loss=loss, batch_scheduler=batch_scheduler)
# Try different size of batch size.
while batch_size >= 1:
print("Estimating NLL using batch size of {}".format(batch_size))
try:
batch_scheduler.batch_size = min(batch_size, len(dataset))
return {
"mean": float(nll.mean.view(status)),
"stderror": float(nll.stderror.view(status))
}
except MemoryError as e:
# Probably not enough memory on GPU
#print("\n".join([l for l in str(e).split("\n") if "allocating" in l]))
pass
except RuntimeError as e:
# Probably RuntimeError: BaseGpuCorrMM: Failed to allocate output of
if "allocate" not in str(e):
raise e
print(
"*An error occured while estimating NLL. Will try a smaller batch size."
)
batch_size = batch_size // 2
raise RuntimeError(
"Cannot find a suitable batch size to estimate NLL. Try using CPU instead or a GPU with more memory."
)
def compute_loss_errors(streamlines, model, hyperparams):
# Create dummy dataset for these new streamlines.
tracto_data = neurotools.TractographyData(None, None, None)
tracto_data.add(streamlines, bundle_name="Generated")
tracto_data.subject_id = 0
dataset = datasets.TractographyDataset([tracto_data],
"Generated",
keep_on_cpu=True)
# Override K for gru_multistep
if 'k' in hyperparams:
hyperparams['k'] = 1
batch_scheduler = batch_scheduler_factory(hyperparams,
dataset,
train_mode=False,
batch_size_override=1000,
use_data_augment=False)
loss = loss_factory(hyperparams, model, dataset)
loss_view = views.LossView(loss=loss, batch_scheduler=batch_scheduler)
return loss_view.losses.view()
def _build_trainer(nb_epochs, optimizer_cls):
print(
"Will build a trainer is going to train a Perceptron for {0} epochs."
.format(nb_epochs))
print("Building model")
model = Perceptron(trainset.input_size, nb_classes)
model.initialize(initer.UniformInitializer(random_seed=1234))
print("Building optimizer")
loss = NLL(model, trainset)
optimizer = optimizer_cls(loss=loss)
print("Optimizer: {}".format(type(optimizer).__name__))
#optimizer = SGD(loss=loss)
#optimizer.append_direction_modifier(ConstantLearningRate(0.1))
# Use mini batches of 100 examples.
batch_scheduler = MiniBatchScheduler(trainset, 100)
print("Building trainer")
trainer = Trainer(optimizer, batch_scheduler)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
# Print NLL mean/stderror.
nll = views.LossView(loss=NLL(model, validset),
batch_scheduler=FullBatchScheduler(validset))
logger = tasks.Logger(loss_monitor, avg_loss, nll.mean)
trainer.append_task(logger, avg_loss)
# Train for `nb_epochs` epochs (stopping criteria should be added at the end).
trainer.append_task(stopping_criteria.MaxEpochStopping(nb_epochs))
return trainer, nll, logger
def test_simple_convnade():
nb_kernels = 8
kernel_shape = (2, 2)
hidden_activation = "sigmoid"
consider_mask_as_channel = True
batch_size = 1024
ordering_seed = 1234
max_epoch = 3
nb_orderings = 1
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(max_epoch))
with Timer("Loading/processing binarized MNIST"):
trainset, validset, testset = load_binarized_mnist()
# Extract the center patch (4x4 pixels) of each image.
indices_to_keep = [
348, 349, 350, 351, 376, 377, 378, 379, 404, 405, 406, 407, 432,
433, 434, 435
]
trainset = Dataset(trainset.inputs.get_value()[:, indices_to_keep],
trainset.inputs.get_value()[:, indices_to_keep],
name="trainset")
validset = Dataset(validset.inputs.get_value()[:, indices_to_keep],
validset.inputs.get_value()[:, indices_to_keep],
name="validset")
testset = Dataset(testset.inputs.get_value()[:, indices_to_keep],
testset.inputs.get_value()[:, indices_to_keep],
name="testset")
image_shape = (4, 4)
nb_channels = 1
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
consider_mask_as_channel=True)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize() # By default, uniform initialization.
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size)
trainer = Trainer(optimizer, batch_scheduler)
trainer.append_task(stopping_criteria.MaxEpochStopping(max_epoch))
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
nll = views.LossView(
loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(
validset, batch_size=len(validset)))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}",
nll.mean, nll.stderror))
trainer.build_theano_graph()
with Timer("Training"):
trainer.train()
with Timer("Checking the probs for all possible inputs sum to 1"):
# rng = np.random.RandomState(ordering_seed)
D = np.prod(image_shape)
batch_scheduler = BatchSchedulerWithAutoregressiveMasks(
validset,
batch_size=len(validset),
batch_id=0,
ordering_id=0,
concatenate_mask=model.nb_channels == 2,
seed=42)
nll = views.LossView(
loss=NllUsingBinaryCrossEntropyWithAutoRegressiveMask(
model, validset, batch_scheduler.mod),
batch_scheduler=batch_scheduler)
nlls_xod_given_xoltd = nll.losses.view(Status())
nlls = np.sum(nlls_xod_given_xoltd.reshape(-1, len(validset)), axis=0)
nll_validset = np.mean(nlls)
print("Sum of NLL for validset:", nll_validset)
inputs = cartesian([[0, 1]] * int(D), dtype=np.float32)
dataset = ReconstructionDataset(inputs)
batch_scheduler = BatchSchedulerWithAutoregressiveMasks(
dataset,
batch_size=len(dataset),
batch_id=0,
ordering_id=0,
concatenate_mask=model.nb_channels == 2,
seed=42)
nll = views.LossView(
loss=NllUsingBinaryCrossEntropyWithAutoRegressiveMask(
model, dataset, batch_scheduler.mod),
batch_scheduler=batch_scheduler)
nlls_xod_given_xoltd = nll.losses.view(Status())
nlls = np.sum(nlls_xod_given_xoltd.reshape(-1, len(dataset)), axis=0)
p_x = np.exp(np.logaddexp.reduce(-nlls))
print("Sum of p(x) for all x:", p_x)
assert_almost_equal(p_x, 1., decimal=5)
def main():
parser = build_parser()
args = parser.parse_args()
print(args)
if min(args.keep_top) < 0:
parser.error("--keep-top must be between in [0, 1].")
# Get experiment folder
experiment_path = args.name
if not os.path.isdir(experiment_path):
# If not a directory, it must be the name of the experiment.
experiment_path = pjoin(".", "experiments", args.name)
if not os.path.isdir(experiment_path):
parser.error('Cannot find experiment: {0}!'.format(args.name))
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "hyperparams.json"))
except FileNotFoundError:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "..", "hyperparams.json"))
# Use this for hyperparams added in a new version, but nonexistent from older versions
retrocompatibility_defaults = {
'feed_previous_direction': False,
'predict_offset': False,
'normalize': False,
'keep_step_size': False,
'sort_streamlines': False
}
for new_hyperparams, default_value in retrocompatibility_defaults.items():
if new_hyperparams not in hyperparams:
hyperparams[new_hyperparams] = default_value
with Timer("Loading signal data and tractogram", newline=True):
volume_manager = VolumeManager()
dataset = datasets.load_tractography_dataset_from_dwi_and_tractogram(
args.signal,
args.tractogram,
volume_manager,
use_sh_coeffs=hyperparams['use_sh_coeffs'],
bvals=args.bvals,
bvecs=args.bvecs,
step_size=args.step_size)
print("Dataset size:", len(dataset))
if vizu_available and args.vizu:
vizu.check_dataset_integrity(dataset, subset=0.2)
with Timer("Loading model"):
loss_type = args.loss_type
model = None
if hyperparams['model'] == 'gru_regression':
from learn2track.models import GRU_Regression
model = GRU_Regression.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_mixture':
from learn2track.models import GRU_Mixture
model = GRU_Mixture.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_multistep':
from learn2track.models import GRU_Multistep_Gaussian
model = GRU_Multistep_Gaussian.create(
experiment_path, volume_manager=volume_manager)
model.k = 1
model.m = 1
elif hyperparams['model'] == 'ffnn_regression':
from learn2track.models import FFNN_Regression
model = FFNN_Regression.create(experiment_path,
volume_manager=volume_manager)
if loss_type in ['l2_sum', 'l2_mean']:
loss_type = "expected_value"
else:
raise NameError("Unknown model: {}".format(hyperparams['model']))
with Timer("Building evaluation function"):
# Override K for gru_multistep
if 'k' in hyperparams:
hyperparams['k'] = 1
batch_scheduler = batch_scheduler_factory(
hyperparams,
dataset,
use_data_augment=
False, # Otherwise it doubles the number of losses :-/
train_mode=False,
batch_size_override=args.batch_size)
loss = loss_factory(hyperparams, model, dataset, loss_type=loss_type)
l2_error = views.LossView(loss=loss, batch_scheduler=batch_scheduler)
with Timer("Scoring...", newline=True):
dummy_status = Status() # Forces recomputing results
losses = l2_error.losses.view(dummy_status)
if hyperparams['model'] == 'ffnn_regression':
_losses = dataset.streamlines.copy()
_losses._data = losses.copy()
_losses._lengths -= 1
_losses._offsets -= np.arange(len(dataset.streamlines))
if args.loss_type == 'l2_sum':
losses = np.asarray([l.sum() for l in _losses])
elif args.loss_type == 'l2_mean':
losses = np.asarray([l.mean() for l in _losses])
mean = float(l2_error.mean.view(dummy_status))
stderror = float(l2_error.stderror.view(dummy_status))
print("Loss: {:.4f} ± {:.4f}".format(mean, stderror))
print("Min: {:.4f}".format(losses.min()))
print("Max: {:.4f}".format(losses.max()))
print("Percentiles: {}".format(
np.percentile(losses, [0, 25, 50, 75, 100])))
with Timer("Saving streamlines"):
nii = dataset.subjects[0].signal
tractogram = nib.streamlines.Tractogram(
dataset.streamlines[batch_scheduler.indices],
affine_to_rasmm=nii.affine)
tractogram.data_per_streamline['loss'] = losses
header = {}
header[Field.VOXEL_TO_RASMM] = nii.affine.copy()
header[Field.VOXEL_SIZES] = nii.header.get_zooms()[:3]
header[Field.DIMENSIONS] = nii.shape[:3]
header[Field.VOXEL_ORDER] = "".join(aff2axcodes(nii.affine))
nib.streamlines.save(tractogram.copy(), args.out, header=header)
if len(args.keep_top) > 0:
for keep_top in args.keep_top:
with Timer("Saving top {}% streamlines".format(keep_top)):
idx = np.argsort(losses)
idx = idx[:int(keep_top * len(losses))]
print("Keeping {}/{} streamlines".format(
len(idx), len(losses)))
sub_tractogram = tractogram[idx]
out_filename = args.out[:-4] + "_top{}".format(
keep_top) + ".tck"
nib.streamlines.save(sub_tractogram, out_filename)
def main():
parser = buildArgsParser()
args = parser.parse_args()
# Extract experiments hyperparameters
hyperparams = dict(vars(args))
# Remove hyperparams that should not be part of the hash
del hyperparams['max_epoch']
del hyperparams['keep']
del hyperparams['force']
del hyperparams['name']
# Get/generate experiment name
experiment_name = args.name
if experiment_name is None:
experiment_name = utils.generate_uid_from_string(repr(hyperparams))
# Create experiment folder
experiment_path = pjoin(".", "experiments", experiment_name)
resuming = False
if os.path.isdir(experiment_path) and not args.force:
resuming = True
print("### Resuming experiment ({0}). ###\n".format(experiment_name))
# Check if provided hyperparams match those in the experiment folder
hyperparams_loaded = utils.load_dict_from_json_file(pjoin(experiment_path, "hyperparams.json"))
if hyperparams != hyperparams_loaded:
print("{\n" + "\n".join(["{}: {}".format(k, hyperparams[k]) for k in sorted(hyperparams.keys())]) + "\n}")
print("{\n" + "\n".join(["{}: {}".format(k, hyperparams_loaded[k]) for k in sorted(hyperparams_loaded.keys())]) + "\n}")
print("The arguments provided are different than the one saved. Use --force if you are certain.\nQuitting.")
sys.exit(1)
else:
if os.path.isdir(experiment_path):
shutil.rmtree(experiment_path)
os.makedirs(experiment_path)
utils.save_dict_to_json_file(pjoin(experiment_path, "hyperparams.json"), hyperparams)
with Timer("Loading dataset"):
trainset, validset, testset = datasets.load(args.dataset)
image_shape = (28, 28)
nb_channels = 1 + (args.use_mask_as_input is True)
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(trainset, args.batch_size,
use_mask_as_input=args.use_mask_as_input,
seed=args.ordering_seed)
print("{} updates per epoch.".format(len(batch_scheduler)))
with Timer("Building model"):
if args.use_lasagne:
if args.with_residual:
model = DeepConvNadeWithResidualUsingLasagne(image_shape=image_shape,
nb_channels=nb_channels,
convnet_blueprint=args.convnet_blueprint,
fullnet_blueprint=args.fullnet_blueprint,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input)
else:
model = DeepConvNadeUsingLasagne(image_shape=image_shape,
nb_channels=nb_channels,
convnet_blueprint=args.convnet_blueprint,
fullnet_blueprint=args.fullnet_blueprint,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input,
use_batch_norm=args.batch_norm)
elif args.with_residual:
model = DeepConvNADEWithResidual(image_shape=image_shape,
nb_channels=nb_channels,
convnet_blueprint=args.convnet_blueprint,
fullnet_blueprint=args.fullnet_blueprint,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input)
else:
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
hidden_activation=args.hidden_activation,
use_mask_as_input=args.use_mask_as_input)
if args.blueprints_seed is not None:
convnet_blueprint, fullnet_blueprint = generate_blueprints(args.blueprint_seed, image_shape[0])
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
else:
if args.convnet_blueprint is not None:
builder.build_convnet_from_blueprint(args.convnet_blueprint)
if args.fullnet_blueprint is not None:
builder.build_fullnet_from_blueprint(args.fullnet_blueprint)
model = builder.build()
# print(str(model.convnet))
# print(str(model.fullnet))
model.initialize(weigths_initializer_factory(args.weights_initialization,
seed=args.initialization_seed))
print(str(model))
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(model, trainset)
optimizer = optimizer_factory(hyperparams, loss)
with Timer("Building trainer"):
trainer = Trainer(optimizer, batch_scheduler)
if args.max_epoch is not None:
trainer.append_task(stopping_criteria.MaxEpochStopping(args.max_epoch))
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
model.deterministic = True # For batch normalization, see https://github.com/Lasagne/Lasagne/blob/master/lasagne/layers/normalization.py#L198
nll = views.LossView(loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(validset, batch_size=0.1*len(validset),
use_mask_as_input=args.use_mask_as_input,
keep_mask=True,
seed=args.ordering_seed+1))
# trainer.append_task(tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}", nll.mean, nll.stderror, each_k_update=100))
trainer.append_task(tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}", nll.mean, nll.stderror))
# direction_norm = views.MonitorVariable(T.sqrt(sum(map(lambda d: T.sqr(d).sum(), loss.gradients.values()))))
# trainer.append_task(tasks.Print("||d|| : {0:.4f}", direction_norm, each_k_update=50))
# Save training progression
def save_model(*args):
trainer.save(experiment_path)
trainer.append_task(stopping_criteria.EarlyStopping(nll.mean, lookahead=args.lookahead, eps=args.lookahead_eps, callback=save_model))
trainer.build_theano_graph()
if resuming:
with Timer("Loading"):
trainer.load(experiment_path)
with Timer("Training"):
trainer.train()
trainer.save(experiment_path)
model.save(experiment_path)
def main():
parser = build_argparser()
args = parser.parse_args()
print(args)
print("Using Theano v.{}".format(theano.version.short_version))
hyperparams_to_exclude = ['max_epoch', 'force', 'name', 'view', 'shuffle_streamlines']
# Use this for hyperparams added in a new version, but nonexistent from older versions
retrocompatibility_defaults = {'feed_previous_direction': False,
'predict_offset': False,
'normalize': False,
'sort_streamlines': False,
'keep_step_size': False,
'use_layer_normalization': False,
'drop_prob': 0.,
'use_zoneout': False,
'skip_connections': False}
experiment_path, hyperparams, resuming = utils.maybe_create_experiment_folder(args, exclude=hyperparams_to_exclude,
retrocompatibility_defaults=retrocompatibility_defaults)
# Log the command currently running.
with open(pjoin(experiment_path, 'cmd.txt'), 'a') as f:
f.write(" ".join(sys.argv) + "\n")
print("Resuming:" if resuming else "Creating:", experiment_path)
with Timer("Loading dataset", newline=True):
trainset_volume_manager = VolumeManager()
validset_volume_manager = VolumeManager()
trainset = datasets.load_tractography_dataset(args.train_subjects, trainset_volume_manager, name="trainset",
use_sh_coeffs=args.use_sh_coeffs)
validset = datasets.load_tractography_dataset(args.valid_subjects, validset_volume_manager, name="validset",
use_sh_coeffs=args.use_sh_coeffs)
print("Dataset sizes:", len(trainset), " |", len(validset))
batch_scheduler = batch_scheduler_factory(hyperparams, dataset=trainset, train_mode=True)
print("An epoch will be composed of {} updates.".format(batch_scheduler.nb_updates_per_epoch))
print(trainset_volume_manager.data_dimension, args.hidden_sizes, batch_scheduler.target_size)
with Timer("Creating model"):
input_size = trainset_volume_manager.data_dimension
if hyperparams['feed_previous_direction']:
input_size += 3
model = model_factory(hyperparams,
input_size=input_size,
output_size=batch_scheduler.target_size,
volume_manager=trainset_volume_manager)
model.initialize(weigths_initializer_factory(args.weights_initialization,
seed=args.initialization_seed))
with Timer("Building optimizer"):
loss = loss_factory(hyperparams, model, trainset)
if args.clip_gradient is not None:
loss.append_gradient_modifier(DirectionClipping(threshold=args.clip_gradient))
optimizer = optimizer_factory(hyperparams, loss)
with Timer("Building trainer"):
trainer = Trainer(optimizer, batch_scheduler)
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
trainer.append_task(avg_loss)
# Print average training loss.
trainer.append_task(tasks.Print("Avg. training loss: : {}", avg_loss))
# if args.learn_to_stop:
# l2err_monitor = views.MonitorVariable(T.mean(loss.mean_sqr_error))
# avg_l2err = tasks.AveragePerEpoch(l2err_monitor)
# trainer.append_task(avg_l2err)
#
# crossentropy_monitor = views.MonitorVariable(T.mean(loss.cross_entropy))
# avg_crossentropy = tasks.AveragePerEpoch(crossentropy_monitor)
# trainer.append_task(avg_crossentropy)
#
# trainer.append_task(tasks.Print("Avg. training L2 err: : {}", avg_l2err))
# trainer.append_task(tasks.Print("Avg. training stopping: : {}", avg_crossentropy))
# trainer.append_task(tasks.Print("L2 err : {0:.4f}", l2err_monitor, each_k_update=100))
# trainer.append_task(tasks.Print("stopping : {0:.4f}", crossentropy_monitor, each_k_update=100))
# Print NLL mean/stderror.
# train_loss = L2DistanceForSequences(model, trainset)
# train_batch_scheduler = StreamlinesBatchScheduler(trainset, batch_size=1000,
# noisy_streamlines_sigma=None,
# nb_updates_per_epoch=None,
# seed=1234)
# train_error = views.LossView(loss=train_loss, batch_scheduler=train_batch_scheduler)
# trainer.append_task(tasks.Print("Trainset - Error : {0:.2f} | {1:.2f}", train_error.sum, train_error.mean))
# HACK: To make sure all subjects in the volume_manager are used in a batch, we have to split the trainset/validset in 2 volume managers
model.volume_manager = validset_volume_manager
model.drop_prob = 0. # Do not use dropout/zoneout for evaluation
valid_loss = loss_factory(hyperparams, model, validset)
valid_batch_scheduler = batch_scheduler_factory(hyperparams,
dataset=validset,
train_mode=False)
valid_error = views.LossView(loss=valid_loss, batch_scheduler=valid_batch_scheduler)
trainer.append_task(tasks.Print("Validset - Error : {0:.2f} | {1:.2f}", valid_error.sum, valid_error.mean))
if hyperparams['model'] == 'ffnn_regression':
valid_batch_scheduler2 = batch_scheduler_factory(hyperparams,
dataset=validset,
train_mode=False)
valid_l2 = loss_factory(hyperparams, model, validset, loss_type="expected_value")
valid_l2_error = views.LossView(loss=valid_l2, batch_scheduler=valid_batch_scheduler2)
trainer.append_task(tasks.Print("Validset - {}".format(valid_l2.__class__.__name__) + "\t: {0:.2f} | {1:.2f}", valid_l2_error.sum, valid_l2_error.mean))
# HACK: Restore trainset volume manager
model.volume_manager = trainset_volume_manager
model.drop_prob = hyperparams['drop_prob'] # Restore dropout
lookahead_loss = valid_error.sum
direction_norm = views.MonitorVariable(T.sqrt(sum(map(lambda d: T.sqr(d).sum(), loss.gradients.values()))))
# trainer.append_task(tasks.Print("||d|| : {0:.4f}", direction_norm))
# logger = tasks.Logger(train_error.mean, valid_error.mean, valid_error.sum, direction_norm)
logger = tasks.Logger(valid_error.mean, valid_error.sum, direction_norm)
trainer.append_task(logger)
if args.view:
import pylab as plt
def _plot(*args, **kwargs):
plt.figure(1)
plt.clf()
plt.show(False)
plt.subplot(121)
plt.plot(np.array(logger.get_variable_history(0)).flatten(), label="Train")
plt.plot(np.array(logger.get_variable_history(1)).flatten(), label="Valid")
plt.legend()
plt.subplot(122)
plt.plot(np.array(logger.get_variable_history(3)).flatten(), label="||d'||")
plt.draw()
trainer.append_task(tasks.Callback(_plot))
# Callback function to stop training if NaN is detected.
def detect_nan(obj, status):
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Stopping training now.")
sys.exit()
trainer.append_task(tasks.Callback(detect_nan, each_k_update=1))
# Callback function to save training progression.
def save_training(obj, status):
trainer.save(experiment_path)
trainer.append_task(tasks.Callback(save_training))
# Early stopping with a callback for saving every time model improves.
def save_improvement(obj, status):
""" Save best model and training progression. """
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Not saving the model. Crashing now.")
sys.exit()
print("*** Best epoch: {0} ***\n".format(obj.best_epoch))
model.save(experiment_path)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
trainer.append_task(tasks.PrintTime(each_k_update=100)) # Profiling
# Add stopping criteria
trainer.append_task(stopping_criteria.MaxEpochStopping(args.max_epoch))
early_stopping = stopping_criteria.EarlyStopping(lookahead_loss, lookahead=args.lookahead, eps=args.lookahead_eps, callback=save_improvement)
trainer.append_task(early_stopping)
with Timer("Compiling Theano graph"):
trainer.build_theano_graph()
if resuming:
if not os.path.isdir(pjoin(experiment_path, 'training')):
print("No 'training/' folder. Assuming it failed before"
" the end of the first epoch. Starting a new training.")
else:
with Timer("Loading"):
trainer.load(experiment_path)
with Timer("Training"):
trainer.train()
def test_simple_convnade():
nb_kernels = 8
kernel_shape = (2, 2)
hidden_activation = "sigmoid"
use_mask_as_input = True
batch_size = 1024
ordering_seed = 1234
max_epoch = 3
nb_orderings = 1
print("Will train Convoluational Deep NADE for a total of {0} epochs.".
format(max_epoch))
with Timer("Loading/processing binarized MNIST"):
trainset, validset, testset = load_binarized_mnist()
# Extract the center patch (4x4 pixels) of each image.
indices_to_keep = [
348, 349, 350, 351, 376, 377, 378, 379, 404, 405, 406, 407, 432,
433, 434, 435
]
trainset = Dataset(trainset.inputs.get_value()[:, indices_to_keep],
trainset.inputs.get_value()[:, indices_to_keep],
name="trainset")
validset = Dataset(validset.inputs.get_value()[:, indices_to_keep],
validset.inputs.get_value()[:, indices_to_keep],
name="validset")
testset = Dataset(testset.inputs.get_value()[:, indices_to_keep],
testset.inputs.get_value()[:, indices_to_keep],
name="testset")
image_shape = (4, 4)
nb_channels = 1
with Timer("Building model"):
builder = DeepConvNADEBuilder(image_shape=image_shape,
nb_channels=nb_channels,
use_mask_as_input=use_mask_as_input)
convnet_blueprint = "64@2x2(valid) -> 1@2x2(full)"
fullnet_blueprint = "5 -> 16"
print("Convnet:", convnet_blueprint)
print("Fullnet:", fullnet_blueprint)
builder.build_convnet_from_blueprint(convnet_blueprint)
builder.build_fullnet_from_blueprint(fullnet_blueprint)
model = builder.build()
model.initialize() # By default, uniform initialization.
with Timer("Building optimizer"):
loss = BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, trainset)
optimizer = SGD(loss=loss)
optimizer.append_direction_modifier(ConstantLearningRate(0.001))
with Timer("Building trainer"):
batch_scheduler = MiniBatchSchedulerWithAutoregressiveMask(
trainset, batch_size)
trainer = Trainer(optimizer, batch_scheduler)
trainer.append_task(stopping_criteria.MaxEpochStopping(max_epoch))
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
accum = tasks.Accumulator(loss_monitor)
logger = tasks.Logger(loss_monitor, avg_loss)
trainer.append_task(logger, avg_loss, accum)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# Print NLL mean/stderror.
nll = views.LossView(
loss=BinaryCrossEntropyEstimateWithAutoRegressiveMask(
model, validset),
batch_scheduler=MiniBatchSchedulerWithAutoregressiveMask(
validset, batch_size=len(validset)))
trainer.append_task(
tasks.Print("Validset - NLL : {0:.2f} ± {1:.2f}",
nll.mean, nll.stderror))
trainer.build_theano_graph()
with Timer("Training"):
trainer.train()
with Timer("Checking the probs for all possible inputs sum to 1"):
rng = np.random.RandomState(ordering_seed)
D = np.prod(image_shape)
inputs = cartesian([[0, 1]] * int(D), dtype=np.float32)
ordering = np.arange(D, dtype=np.int32)
rng.shuffle(ordering)
symb_input = T.vector("input")
symb_input.tag.test_value = inputs[-len(inputs) // 4]
symb_ordering = T.ivector("ordering")
symb_ordering.tag.test_value = ordering
nll_of_x_given_o = theano.function([symb_input, symb_ordering],
model.nll_of_x_given_o(
symb_input, symb_ordering),
name="nll_of_x_given_o")
#theano.printing.pydotprint(nll_of_x_given_o, '{0}_nll_of_x_given_o_{1}'.format(model.__class__.__name__, theano.config.device), with_ids=True)
for i in range(nb_orderings):
print("Ordering:", ordering)
ordering = np.arange(D, dtype=np.int32)
rng.shuffle(ordering)
nlls = []
for no, input in enumerate(inputs):
print("{}/{}".format(no, len(inputs)), end='\r')
nlls.append(nll_of_x_given_o(input, ordering))
print("{}/{} Done".format(len(inputs), len(inputs)))
p_x = np.exp(np.logaddexp.reduce(-np.array(nlls)))
print("Sum of p(x) for all x:", p_x)
assert_almost_equal(p_x, 1., decimal=5)
def main():
parser = build_parser()
args = parser.parse_args()
print(args)
# Get experiment folder
experiment_path = args.name
if not os.path.isdir(experiment_path):
# If not a directory, it must be the name of the experiment.
experiment_path = pjoin(".", "experiments", args.name)
if not os.path.isdir(experiment_path):
parser.error('Cannot find experiment: {0}!'.format(args.name))
# Load experiments hyperparameters
try:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "hyperparams.json"))
except FileNotFoundError:
hyperparams = smartutils.load_dict_from_json_file(
pjoin(experiment_path, "..", "hyperparams.json"))
# Use this for hyperparams added in a new version, but nonexistent from older versions
retrocompatibility_defaults = {
'feed_previous_direction': False,
'predict_offset': False,
'normalize': False,
'keep_step_size': False,
'sort_streamlines': False,
'use_layer_normalization': False,
'drop_prob': 0.,
'use_zoneout': False
}
for new_hyperparams, default_value in retrocompatibility_defaults.items():
if new_hyperparams not in hyperparams:
hyperparams[new_hyperparams] = default_value
with Timer("Loading dataset", newline=True):
volume_manager = VolumeManager()
dataset = datasets.load_tractography_dataset(
args.subjects,
volume_manager,
name="dataset",
use_sh_coeffs=hyperparams['use_sh_coeffs'])
print("Dataset size:", len(dataset))
with Timer("Loading model"):
model = None
if hyperparams['model'] == 'gru_regression':
from learn2track.models import GRU_Regression
model = GRU_Regression.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_gaussian':
from learn2track.models import GRU_Gaussian
model = GRU_Gaussian.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_mixture':
from learn2track.models import GRU_Mixture
model = GRU_Mixture.create(experiment_path,
volume_manager=volume_manager)
elif hyperparams['model'] == 'gru_multistep':
from learn2track.models import GRU_Multistep_Gaussian
model = GRU_Multistep_Gaussian.create(
experiment_path, volume_manager=volume_manager)
model.k = 1
model.m = 1
elif hyperparams['model'] == 'ffnn_regression':
from learn2track.models import FFNN_Regression
model = FFNN_Regression.create(experiment_path,
volume_manager=volume_manager)
else:
raise NameError("Unknown model: {}".format(hyperparams['model']))
model.drop_prob = 0. # Make sure dropout/zoneout is not used when testing
with Timer("Building evaluation function"):
# Override K for gru_multistep
if 'k' in hyperparams:
hyperparams['k'] = 1
batch_scheduler = batch_scheduler_factory(
hyperparams,
dataset,
train_mode=False,
batch_size_override=args.batch_size)
loss = loss_factory(hyperparams,
model,
dataset,
loss_type=args.loss_type)
l2_error = views.LossView(loss=loss, batch_scheduler=batch_scheduler)
with Timer("Evaluating...", newline=True):
results_file = pjoin(experiment_path, "results.json")
results = {}
if os.path.isfile(results_file) and not args.force:
print(
"Loading saved results... (use --force to re-run evaluation)")
results = smartutils.load_dict_from_json_file(results_file)
tag = ""
if args.loss_type == 'expected_value' or hyperparams[
'model'] == 'gru_regression':
tag = "_EV_L2_error"
elif args.loss_type == 'maximum_component':
tag = "_MC_L2_error"
elif hyperparams['model'] in [
'gru_gaussian', 'gru_mixture', 'gru_multistep'
]:
tag = "_NLL"
entry = args.dataset_name + tag
if entry not in results or args.force:
with Timer("Evaluating {}".format(entry)):
dummy_status = Status() # Forces recomputing results
results[entry] = {
'mean': float(l2_error.mean.view(dummy_status)),
'stderror': float(l2_error.stderror.view(dummy_status))
}
smartutils.save_dict_to_json_file(
results_file, results) # Update results file.
print("{}: {:.4f} ± {:.4f}".format(entry, results[entry]['mean'],
results[entry]['stderror']))
def main():
parser = build_argparser()
args = parser.parse_args()
print(args)
print("Using Theano v.{}".format(theano.version.short_version))
hyperparams_to_exclude = ['max_epoch', 'force', 'name', 'view']
# Use this for hyperparams added in a new version, but nonexistent from older versions
retrocompatibility_defaults = {'use_layer_normalization': False}
experiment_path, hyperparams, resuming = utils.maybe_create_experiment_folder(
args,
exclude=hyperparams_to_exclude,
retrocompatibility_defaults=retrocompatibility_defaults)
# Log the command currently running.
with open(pjoin(experiment_path, 'cmd.txt'), 'a') as f:
f.write(" ".join(sys.argv) + "\n")
print("Resuming:" if resuming else "Creating:", experiment_path)
with Timer("Loading dataset", newline=True):
trainset_volume_manager = VolumeManager()
validset_volume_manager = VolumeManager()
trainset = datasets.load_mask_classifier_dataset(
args.train_subjects,
trainset_volume_manager,
name="trainset",
use_sh_coeffs=args.use_sh_coeffs)
validset = datasets.load_mask_classifier_dataset(
args.valid_subjects,
validset_volume_manager,
name="validset",
use_sh_coeffs=args.use_sh_coeffs)
print("Dataset sizes:", len(trainset), " |", len(validset))
batch_scheduler = MaskClassifierBatchScheduler(
trainset, hyperparams['batch_size'], seed=hyperparams['seed'])
print("An epoch will be composed of {} updates.".format(
batch_scheduler.nb_updates_per_epoch))
print(trainset_volume_manager.data_dimension, args.hidden_sizes,
batch_scheduler.target_size)
with Timer("Creating model"):
input_size = trainset_volume_manager.data_dimension
model = FFNN_Classification(trainset_volume_manager, input_size,
hyperparams['hidden_sizes'])
model.initialize(
weigths_initializer_factory(args.weights_initialization,
seed=args.initialization_seed))
with Timer("Building optimizer"):
loss = BinaryCrossEntropy(model, trainset)
if args.clip_gradient is not None:
loss.append_gradient_modifier(
DirectionClipping(threshold=args.clip_gradient))
optimizer = optimizer_factory(hyperparams, loss)
with Timer("Building trainer"):
trainer = Trainer(optimizer, batch_scheduler)
# Log training error
loss_monitor = views.MonitorVariable(loss.loss)
avg_loss = tasks.AveragePerEpoch(loss_monitor)
trainer.append_task(avg_loss)
# Print average training loss.
trainer.append_task(
tasks.Print("Avg. training loss: : {}", avg_loss))
# HACK: To make sure all subjects in the volume_manager are used in a batch, we have to split the trainset/validset in 2 volume managers
model.volume_manager = validset_volume_manager
valid_loss = BinaryCrossEntropy(model, validset)
valid_batch_scheduler = MaskClassifierBatchScheduler(
validset, hyperparams['batch_size'], seed=hyperparams['seed'])
valid_error = views.LossView(loss=valid_loss,
batch_scheduler=valid_batch_scheduler)
trainer.append_task(
tasks.Print("Validset - Error : {0:.2f} | {1:.2f}",
valid_error.sum, valid_error.mean))
# HACK: Restore trainset volume manager
model.volume_manager = trainset_volume_manager
lookahead_loss = valid_error.sum
direction_norm = views.MonitorVariable(
T.sqrt(sum(map(lambda d: T.sqr(d).sum(),
loss.gradients.values()))))
# trainer.append_task(tasks.Print("||d|| : {0:.4f}", direction_norm))
# logger = tasks.Logger(train_error.mean, valid_error.mean, valid_error.sum, direction_norm)
logger = tasks.Logger(valid_error.mean, valid_error.sum,
direction_norm)
trainer.append_task(logger)
# Callback function to stop training if NaN is detected.
def detect_nan(obj, status):
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Stopping training now.")
sys.exit()
trainer.append_task(tasks.Callback(detect_nan, each_k_update=1))
# Callback function to save training progression.
def save_training(obj, status):
trainer.save(experiment_path)
trainer.append_task(tasks.Callback(save_training))
# Early stopping with a callback for saving every time model improves.
def save_improvement(obj, status):
""" Save best model and training progression. """
if np.isnan(model.parameters[0].get_value().sum()):
print("NaN detected! Not saving the model. Crashing now.")
sys.exit()
print("*** Best epoch: {0} ***\n".format(obj.best_epoch))
model.save(experiment_path)
# Print time for one epoch
trainer.append_task(tasks.PrintEpochDuration())
trainer.append_task(tasks.PrintTrainingDuration())
trainer.append_task(tasks.PrintTime(each_k_update=100)) # Profiling
# Add stopping criteria
trainer.append_task(stopping_criteria.MaxEpochStopping(args.max_epoch))
early_stopping = stopping_criteria.EarlyStopping(
lookahead_loss,
lookahead=args.lookahead,
eps=args.lookahead_eps,
callback=save_improvement)
trainer.append_task(early_stopping)
with Timer("Compiling Theano graph"):
trainer.build_theano_graph()
if resuming:
if not os.path.isdir(pjoin(experiment_path, 'training')):
print("No 'training/' folder. Assuming it failed before"
" the end of the first epoch. Starting a new training.")
else:
with Timer("Loading"):
trainer.load(experiment_path)
with Timer("Training"):
trainer.train()
