def test_gru_mixture_fprop():
hidden_sizes = 50
with Timer("Creating dataset", newline=True):
volume_manager = neurotools.VolumeManager()
trainset = make_dummy_dataset(volume_manager)
print("Dataset sizes:", len(trainset))
batch_scheduler = batch_schedulers.TractographyBatchScheduler(
trainset, batch_size=16, noisy_streamlines_sigma=None, seed=1234)
print("An epoch will be composed of {} updates.".format(
batch_scheduler.nb_updates_per_epoch))
print(volume_manager.data_dimension, hidden_sizes,
batch_scheduler.target_size)
with Timer("Creating model"):
hyperparams = {
'model': 'gru_mixture',
'n_gaussians': 2,
'SGD': "1e-2",
'hidden_sizes': hidden_sizes,
'learn_to_stop': False,
'normalize': False,
'feed_previous_direction': False
}
model = factories.model_factory(
hyperparams,
input_size=volume_manager.data_dimension,
output_size=batch_scheduler.target_size,
volume_manager=volume_manager)
model.initialize(
factories.weigths_initializer_factory("orthogonal", seed=1234))
# Test fprop with missing streamlines from one subject in a batch
output = model.get_output(trainset.symb_inputs)
fct = theano.function([trainset.symb_inputs],
output,
updates=model.graph_updates)
batch_inputs, batch_targets, batch_mask = batch_scheduler._next_batch(2)
out = fct(batch_inputs)
with Timer("Building optimizer"):
loss = factories.loss_factory(hyperparams, model, trainset)
optimizer = factories.optimizer_factory(hyperparams, loss)
fct_loss = theano.function(
[trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
loss.loss,
updates=model.graph_updates)
loss_value = fct_loss(batch_inputs, batch_targets, batch_mask)
print("Loss:", loss_value)
fct_optim = theano.function(
[trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
list(optimizer.directions.values()),
updates=model.graph_updates)
dirs = fct_optim(batch_inputs, batch_targets, batch_mask)
def test_gru_regression_fprop():
hidden_sizes = 50
with Timer("Creating dataset", newline=True):
volume_manager = neurotools.VolumeManager()
trainset = make_dummy_dataset(volume_manager)
print("Dataset sizes:", len(trainset))
batch_scheduler = batch_schedulers.TractographyBatchScheduler(trainset,
batch_size=16,
noisy_streamlines_sigma=None,
seed=1234)
print ("An epoch will be composed of {} updates.".format(batch_scheduler.nb_updates_per_epoch))
print (volume_manager.data_dimension, hidden_sizes, batch_scheduler.target_size)
with Timer("Creating model"):
hyperparams = {'model': 'gru_regression',
'SGD': "1e-2",
'hidden_sizes': hidden_sizes,
'learn_to_stop': False,
'normalize': False}
model = factories.model_factory(hyperparams,
input_size=volume_manager.data_dimension,
output_size=batch_scheduler.target_size,
volume_manager=volume_manager)
model.initialize(factories.weigths_initializer_factory("orthogonal", seed=1234))
# Test fprop with missing streamlines from one subject in a batch
output = model.get_output(trainset.symb_inputs)
fct = theano.function([trainset.symb_inputs], output, updates=model.graph_updates)
batch_inputs, batch_targets, batch_mask = batch_scheduler._next_batch(2)
out = fct(batch_inputs)
with Timer("Building optimizer"):
loss = factories.loss_factory(hyperparams, model, trainset)
optimizer = factories.optimizer_factory(hyperparams, loss)
fct_loss = theano.function([trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
loss.loss,
updates=model.graph_updates)
loss_value = fct_loss(batch_inputs, batch_targets, batch_mask)
print("Loss:", loss_value)
fct_optim = theano.function([trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
list(optimizer.directions.values()),
updates=model.graph_updates)
dirs = fct_optim(batch_inputs, batch_targets, batch_mask)
def test_gru_multistep_fprop_k3():
hidden_sizes = 50
hyperparams = {
'model': 'gru_multistep',
'k': 3,
'm': 3,
'batch_size': 16,
'SGD': "1e-2",
'hidden_sizes': hidden_sizes,
'learn_to_stop': False,
'normalize': False,
'noisy_streamlines_sigma': None,
'shuffle_streamlines': True,
'seed': 1234
}
with Timer("Creating dataset", newline=True):
volume_manager = neurotools.VolumeManager()
trainset = make_dummy_dataset(volume_manager)
print("Dataset sizes:", len(trainset))
batch_scheduler = factories.batch_scheduler_factory(hyperparams,
trainset,
train_mode=True)
print("An epoch will be composed of {} updates.".format(
batch_scheduler.nb_updates_per_epoch))
print(volume_manager.data_dimension, hidden_sizes,
batch_scheduler.target_size)
with Timer("Creating model"):
model = factories.model_factory(
hyperparams,
input_size=volume_manager.data_dimension,
output_size=batch_scheduler.target_size,
volume_manager=volume_manager)
model.initialize(
factories.weigths_initializer_factory("orthogonal", seed=1234))
# Test fprop
output = model.get_output(trainset.symb_inputs)
fct = theano.function([trainset.symb_inputs],
output,
updates=model.graph_updates)
batch_inputs, batch_targets, batch_mask = batch_scheduler._next_batch(2)
out = fct(batch_inputs)
with Timer("Building optimizer"):
loss = factories.loss_factory(hyperparams, model, trainset)
optimizer = factories.optimizer_factory(hyperparams, loss)
fct_loss = theano.function(
[trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
loss.loss,
updates=model.graph_updates)
loss_value = fct_loss(batch_inputs, batch_targets, batch_mask)
print("Loss:", loss_value)
fct_optim = theano.function(
[trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
list(optimizer.directions.values()),
updates=model.graph_updates)
dirs = fct_optim(batch_inputs, batch_targets, batch_mask)
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,
'normalize': 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))
if args.view:
tsne_view(trainset, trainset_volume_manager)
sys.exit(0)
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
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))
# 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)
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 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 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()
def test_gru_mixture_fprop_neighborhood():
hyperparams = {
'model': 'gru_mixture',
'SGD': "1e-2",
'hidden_sizes': 50,
'batch_size': 16,
'learn_to_stop': False,
'normalize': True,
'activation': 'tanh',
'feed_previous_direction': False,
'predict_offset': False,
'use_layer_normalization': False,
'drop_prob': 0.,
'use_zoneout': False,
'skip_connections': False,
'seed': 1234,
'noisy_streamlines_sigma': None,
'keep_step_size': True,
'sort_streamlines': False,
'n_gaussians': 2,
'neighborhood_radius': 0.5
}
with Timer("Creating dataset", newline=True):
volume_manager = neurotools.VolumeManager()
trainset = make_dummy_dataset(volume_manager)
print("Dataset sizes:", len(trainset))
batch_scheduler = factories.batch_scheduler_factory(hyperparams,
dataset=trainset)
print("An epoch will be composed of {} updates.".format(
batch_scheduler.nb_updates_per_epoch))
print(volume_manager.data_dimension, hyperparams['hidden_sizes'],
batch_scheduler.target_size)
with Timer("Creating model"):
model = factories.model_factory(
hyperparams,
input_size=volume_manager.data_dimension,
output_size=batch_scheduler.target_size,
volume_manager=volume_manager)
model.initialize(
factories.weigths_initializer_factory("orthogonal", seed=1234))
print("Input size: {}".format(model.model_input_size))
# Test fprop with missing streamlines from one subject in a batch
output = model.get_output(trainset.symb_inputs)
fct = theano.function([trainset.symb_inputs],
output,
updates=model.graph_updates)
batch_inputs, batch_targets, batch_mask = batch_scheduler._next_batch(2)
out = fct(batch_inputs)
with Timer("Building optimizer"):
loss = factories.loss_factory(hyperparams, model, trainset)
optimizer = factories.optimizer_factory(hyperparams, loss)
fct_loss = theano.function(
[trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
loss.loss,
updates=model.graph_updates)
loss_value = fct_loss(batch_inputs, batch_targets, batch_mask)
print("Loss:", loss_value)
fct_optim = theano.function(
[trainset.symb_inputs, trainset.symb_targets, trainset.symb_mask],
list(optimizer.directions.values()),
updates=model.graph_updates)
dirs = fct_optim(batch_inputs, batch_targets, batch_mask)
return True