def test_gru_multistep_fprop_k3_multidataset():
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)
validset = 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 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_ffnn_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.SingleInputTractographyBatchScheduler(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': 'ffnn_regression',
'SGD': "1e-2",
'hidden_sizes': hidden_sizes,
'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))
output = model.get_output(trainset.symb_inputs)
fct = theano.function([trainset.symb_inputs], output, updates=model.graph_updates)
batch_inputs, batch_targets = 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],
loss.loss,
updates=model.graph_updates)
loss_value = fct_loss(batch_inputs, batch_targets)
print("Loss:", loss_value)
fct_optim = theano.function([trainset.symb_inputs, trainset.symb_targets],
list(optimizer.directions.values()),
updates=model.graph_updates)
dirs = fct_optim(batch_inputs, batch_targets)
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 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
