def cnn_run_dropout_maxout(data_path, num_rows, num_cols, num_channels,
input_path, pred_path):
t = time.time()
sub_window = gen_center_sub_window(76, num_cols)
trn = SarDataset(ds[0][0], ds[0][1], sub_window)
vld = SarDataset(ds[1][0], ds[1][1], sub_window)
tst = SarDataset(ds[2][0], ds[2][1], sub_window)
print 'Take {}s to read data'.format(time.time() - t)
t = time.time()
batch_size = 100
h1 = maxout.Maxout(layer_name='h2', num_units=1, num_pieces=100, irange=.1)
hidden_layer = mlp.ConvRectifiedLinear(layer_name='h2',
output_channels=8,
irange=0.05,
kernel_shape=[5, 5],
pool_shape=[2, 2],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
hidden_layer2 = mlp.ConvRectifiedLinear(layer_name='h3',
output_channels=8,
irange=0.05,
kernel_shape=[5, 5],
pool_shape=[2, 2],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
#output_layer = mlp.Softplus(dim=1,layer_name='output',irange=0.1)
output_layer = mlp.Linear(dim=1, layer_name='output', irange=0.05)
trainer = sgd.SGD(learning_rate=0.001,
batch_size=100,
termination_criterion=EpochCounter(2000),
cost=dropout.Dropout(),
train_iteration_mode='even_shuffled_sequential',
monitor_iteration_mode='even_shuffled_sequential',
monitoring_dataset={
'test': tst,
'valid': vld,
'train': trn
})
layers = [hidden_layer, hidden_layer2, output_layer]
input_space = space.Conv2DSpace(shape=[num_rows, num_cols],
num_channels=num_channels)
ann = mlp.MLP(layers, input_space=input_space, batch_size=batch_size)
watcher = best_params.MonitorBasedSaveBest(channel_name='valid_objective',
save_path='sar_cnn_mlp.pkl')
experiment = Train(dataset=trn,
model=ann,
algorithm=trainer,
extensions=[watcher])
print 'Take {}s to compile code'.format(time.time() - t)
t = time.time()
experiment.main_loop()
print 'Training time: {}s'.format(time.time() - t)
serial.save('cnn_hhv_{0}_{1}.pkl'.format(num_rows, num_cols),
ann,
on_overwrite='backup')
#read hh and hv into a 3D numpy
image = read_hhv(input_path)
return ann, sar_predict(ann, image, pred_path)
def cnn_transform_ensemble():
import os
from datetime import datetime
print str(datetime.now())
t0 = time.time()
kwargs = get_default_configure()
kwargs['num_rows'] = 41
kwargs['num_cols'] = 41
import pprint
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(kwargs)
i = -1
while under_sample_water.which_fold != i:
if i == -1:
i = under_sample_water.which_fold
kwargs['save_path'] = str(under_sample_water.which_fold) + '.pkl'
t1 = time.time()
ann = cnn_train_tranformer(**kwargs)
serial.save(kwargs['predict_path'] + 'f' + kwargs['save_path'],
ann,
on_overwrite='backup')
print 'saved to: ' + kwargs['save_path']
print 'Traing done. Take {}h'.format((time.time() - t1) / 3600)
break
utils.sms_notice('Training finished. Taking {}h in total.'.format(
(time.time() - t0) / 3600))
print 'Traing done. Take {}h'.format((time.time() - t0) / 3600)
# sum of all predictions
predict_batch(predict_path)
def cnn_ensemble_leave_one_out():
import os
from datetime import datetime
print str(datetime.now())
t0 = time.time()
which_fold = 0
while 1:
print which_fold
try:
kwargs = get_default_configure_leave_one_out(which_fold=which_fold)
except:
break
kwargs['num_rows'] = 40
kwargs['num_cols'] = 40
pp = pprint.PrettyPrinter(indent=4)
pp.pprint(kwargs)
kwargs['save_path'] = str(which_fold) + '.pkl'
t1 = time.time()
ann = cnn_train(**kwargs)
serial.save(kwargs['predict_path'] + 'f' + kwargs['save_path'],
ann,
on_overwrite='backup')
print 'saved to: ' + kwargs['save_path']
print 'Traing done. Take {}h'.format((time.time() - t1) / 3600)
which_fold += 1
utils.sms_notice('Training finished. Taking {}h in total.'.format(
(time.time() - t0) / 3600))
print 'Traing done. Take {}h'.format((time.time() - t0) / 3600)
# sum of all predictions
#predict_batch()
evaluate_sets(kwargs['predict_path'])
def train_batch(self, dataset, batch_size):
x = dataset.get_batch_design(batch_size, include_labels=False)
if self.flags['truncate_v']:
x = numpy.clip(x, -self.truncation_bound['v'], self.truncation_bound['v'])
try:
self.batch_train_func(x)
self.enforce_constraints()
except:
import pdb; pdb.set_trace()
# accounting...
self.examples_seen += self.batch_size
self.batches_seen += 1
# save to different path each epoch
if self.my_save_path and \
(self.batches_seen in self.save_at or
self.batches_seen % self.save_every == 0):
fname = self.my_save_path + '_e%i.pkl' % self.batches_seen
print 'Saving to %s ...' % fname,
serial.save(fname, self)
print 'done'
return self.batches_seen < self.max_updates
def save_weights(self):
"""
Saves all weights in a .txt, .npy or .mat file depending on the ending of the 'weight_path'.
If the path ends in .pkl, the entire model is stored.
"""
model = serial.load(self.model_path)
weight_dict = {}
for layer in model.layers:
try:
weight_dict[layer.layer_name] = layer.get_weights()
except:
layer_weights = layer.get_weights_topo()
weight_dict[layer.layer_name] = layer_weights# without reshaping since it the input/output vector would need to reshaped in the same way which might lead to problems
if self.weight_path[-4:] == '.pkl':
print 'saving model ', self.weight_path
serial.save(self.weight_path, model)
elif self.weight_path[-4:] == '.mat':
scipy.io.savemat(self.weight_path[:-4]+'.mat', weight_dict)
elif self.weight_path[-4:] == '.npy':
np.save(self.weight_path[:-4], weight_dict)
else:
raise Exception('Only ".mat", ".pkl" and ".npy" files are supported as data formats.')
def on_monitor(self, model, dataset, algorithm):
"""
Make sure Polyak-averaged model gets monitored.
Save the model if necessary.
Parameters
----------
model : a Model instance
dataset : Dataset
algorithm : WRITEME
"""
if self._count == self.start:
self._worker = _PolyakWorker(model)
algorithm.update_callbacks.append(self._worker)
#HACK
try:
model.add_polyak_channels(self._worker.param_to_mean,
algorithm.monitoring_dataset)
except AttributeError:
pass
elif self.save_path is not None and self._count > self.start and \
self._count % self.save_freq == 0:
saved_params = OrderedDict()
for param in model.get_params():
saved_params[param] = param.get_value()
param.set_value(self._worker.param_to_mean[param].get_value())
serial.save(self.save_path, model)
for param in model.get_params():
param.set_value(saved_params[param])
self._count += 1
def on_monitor(self, model, dataset, algorithm):
"""
Save the model if we are on a save epoch.
Parameters
----------
model : pylearn2.models.model.Model
model.monitor must contain a channel with name given by self.channel_name
dataset : pylearn2.datasets.dataset.Dataset
not used
algorithm : TrainingAlgorithm
not used
"""
#monitor = model.monitor
#channels = monitor.channels
#channel = channels[self.channel_name]
#val_record = channel.val_record
#epoch = len(val_record)
epoch = model.monitor.get_epochs_seen()
save_file = '%s_%d.pkl' % (self.save_prefix, epoch)
if np.mod(epoch, self.interval) == 0:
print('Saving model to %s' % save_file)
serial.save(save_file, model, on_overwrite='backup')
def train_batch(self, dataset, batch_size):
(x, y) = dataset.get_random_framepair_batch(batch_size)
if self.flags['truncate_v']:
x = numpy.clip(x, -self.truncation_bound['v'],
self.truncation_bound['v'])
try:
self.batch_train_func(x.astype(floatX))
self.enforce_constraints()
except:
import pdb
pdb.set_trace()
# accounting...
self.examples_seen += self.batch_size
self.batches_seen += 1
# save to different path each epoch
if self.my_save_path and \
(self.batches_seen in self.save_at or
self.batches_seen % self.save_every == 0):
fname = self.my_save_path + '_e%i.pkl' % self.batches_seen
print 'Saving to %s ...' % fname,
serial.save(fname, self)
print 'done'
return self.batches_seen < self.max_updates
def main(train_path,
out_path,
split,
**kwargs):
print 'loading training features'
train_X = get_features(train_path, split)
#assert train_X.flags.c_contiguous
gc.collect()
assert str(train_X.dtype) == 'float32'
assert train_X.shape[0] == 120
report = Report(train_path, split)
train_X_omnivore, train_y, fold_indices = get_training_subset(train_X, 'omnivore')
model = train(fold_indices, train_X_omnivore, train_y, report, **kwargs)
serial.save(out_path+'.omnivore.model.pkl', model)
report.write(out_path+'.omnivore.validation_report.txt')
report = Report(train_path, split)
train_X_fruit, train_y, fold_indices = get_training_subset(train_X, 'fruit')
model = train(fold_indices, train_X_fruit, train_y, report, **kwargs)
serial.save(out_path+'.fruit.model.pkl', model)
report.write(out_path+'.fruit.validation_report.txt')
def cnn_ensemble_leave_one_out():
import os
from datetime import datetime
print str(datetime.now())
t0 = time.time()
which_fold = 0
while 1:
print which_fold
try:
kwargs = get_default_configure_leave_one_out(which_fold=which_fold)
except:
break
kwargs['num_rows']=40
kwargs['num_cols']=40
pp=pprint.PrettyPrinter(indent=4)
pp.pprint(kwargs)
kwargs['save_path'] = str(which_fold)+'.pkl'
t1 = time.time()
ann = cnn_train(**kwargs)
serial.save(kwargs['predict_path']+'f'+kwargs['save_path'],ann,on_overwrite='backup')
print 'saved to: '+kwargs['save_path']
print 'Traing done. Take {}h'.format((time.time()-t1)/3600)
which_fold += 1
utils.sms_notice('Training finished. Taking {}h in total.'.format((time.time()-t0)/3600))
print 'Traing done. Take {}h'.format((time.time()-t0)/3600)
# sum of all predictions
#predict_batch()
evaluate_sets(kwargs['predict_path'])
def on_monitor(self, model, dataset, algorithm):
"""
Make sure Polyak-averaged model gets monitored.
Save the model if necessary.
Parameters
----------
model : a Model instance
dataset : Dataset
algorithm : WRITEME
"""
if self._count == self.start:
self._worker = _PolyakWorker(model)
algorithm.update_callbacks.append(self._worker)
#HACK
try:
model.add_polyak_channels(self._worker.param_to_mean,
algorithm.monitoring_dataset)
except AttributeError:
pass
elif self.save_path is not None and self._count > self.start and \
self._count % self.save_freq == 0:
saved_params = OrderedDict()
for param in model.get_params():
saved_params[param] = param.get_value()
param.set_value(self._worker.param_to_mean[param].get_value())
serial.save(self.save_path, model)
for param in model.get_params():
param.set_value(saved_params[param])
self._count += 1
def cnn_transform_ensemble():
import os
from datetime import datetime
print str(datetime.now())
t0 = time.time()
kwargs = get_default_configure()
kwargs['num_rows']=41
kwargs['num_cols']=41
import pprint
pp=pprint.PrettyPrinter(indent=4)
pp.pprint(kwargs)
i = -1
while under_sample_water.which_fold != i:
if i == -1:
i = under_sample_water.which_fold
kwargs['save_path'] = str(under_sample_water.which_fold)+'.pkl'
t1 = time.time()
ann = cnn_train_tranformer(**kwargs)
serial.save(kwargs['predict_path']+'f'+kwargs['save_path'],ann,on_overwrite='backup')
print 'saved to: '+kwargs['save_path']
print 'Traing done. Take {}h'.format((time.time()-t1)/3600)
break
utils.sms_notice('Training finished. Taking {}h in total.'.format((time.time()-t0)/3600))
print 'Traing done. Take {}h'.format((time.time()-t0)/3600)
# sum of all predictions
predict_batch(predict_path)
def main(train_path,
out_path,
dataset,
standardize,
C,
**kwargs):
stl10 = dataset == 'stl10'
cifar10 = dataset == 'cifar10'
cifar100 = dataset == 'cifar100'
assert stl10 + cifar10 + cifar100 == 1
print('getting labels and oflds')
train_y, fold_indices = get_labels_and_fold_indices(cifar10, cifar100, stl10)
gc.collect()
assert train_y is not None
print('loading training features')
train_X = get_features(train_path, split = False, standardize = standardize)
assert str(train_X.dtype) == 'float32'
if stl10:
assert train_X.shape[0] == 5000
if cifar10 or cifar100:
assert train_X.shape[0] == 50000
assert train_y.shape == (50000,)
print('training model')
model = train(train_X, train_y, C)
print('saving model')
serial.save(out_path, model)
def main(train_path, out_path, split, **kwargs):
print 'loading training features'
train_X = get_features(train_path, split)
#assert train_X.flags.c_contiguous
gc.collect()
assert str(train_X.dtype) == 'float32'
assert train_X.shape[0] == 120
report = Report(train_path, split)
train_X_omnivore, train_y, fold_indices = get_training_subset(
train_X, 'omnivore')
model = train(fold_indices, train_X_omnivore, train_y, report, **kwargs)
serial.save(out_path + '.omnivore.model.pkl', model)
report.write(out_path + '.omnivore.validation_report.txt')
report = Report(train_path, split)
train_X_fruit, train_y, fold_indices = get_training_subset(
train_X, 'fruit')
model = train(fold_indices, train_X_fruit, train_y, report, **kwargs)
serial.save(out_path + '.fruit.model.pkl', model)
report.write(out_path + '.fruit.validation_report.txt')
def save_weights(self):
"""
Saves all weights in a .txt, .npy or .mat file depending on the ending of the 'weight_path'.
If the path ends in .pkl, the entire model is stored.
"""
model = serial.load(self.model_path)
weight_dict = {}
for layer in model.layers:
try:
weight_dict[layer.layer_name] = layer.get_weights()
except:
layer_weights = layer.get_weights_topo()
weight_dict[
layer.
layer_name] = layer_weights # without reshaping since it the input/output vector would need to reshaped in the same way which might lead to problems
if self.weight_path[-4:] == '.pkl':
print 'saving model ', self.weight_path
serial.save(self.weight_path, model)
elif self.weight_path[-4:] == '.mat':
scipy.io.savemat(self.weight_path[:-4] + '.mat', weight_dict)
elif self.weight_path[-4:] == '.npy':
np.save(self.weight_path[:-4], weight_dict)
else:
raise Exception(
'Only ".mat", ".pkl" and ".npy" files are supported as data formats.'
)
def on_monitor(self, model, dataset, algorithm):
"""
Looks whether the model performs better than earlier. If it's the
case, saves the model.
Parameters
----------
model : pylearn2.models.model.Model
model.monitor must contain a channel with name given by
self.channel_name
dataset : pylearn2.datasets.dataset.Dataset
Not used
algorithm : TrainingAlgorithm
Not used
"""
monitor = model.monitor
channels = monitor.channels
channel = channels[self.channel_name]
val_record = channel.val_record
new_cost = val_record[-1]
if self.coeff * new_cost < self.coeff * self.best_cost:
self.best_cost = new_cost
# Update the tag of the model object before saving it.
self._update_tag(model)
if self.store_best_model:
self.best_model = deepcopy(model)
if self.save_path is not None:
with log_timing(log, 'Saving to ' + self.save_path):
serial.save(self.save_path, model, on_overwrite='backup')
def on_monitor(self, model, dataset, algorithm):
"""
Looks whether the model performs better than earlier. If it's the
case, saves the model.
Parameters
----------
model : pylearn2.models.model.Model
model.monitor must contain a channel with name given by \
self.channel_name
dataset : pylearn2.datasets.dataset.Dataset
Not used
algorithm : TrainingAlgorithm
Not used
"""
monitor = model.monitor
channels = monitor.channels
channel = channels[self.channel_name]
val_record = channel.val_record
new_cost = self.coeff * val_record[-1]
if new_cost < self.best_cost:
self.best_cost = new_cost
serial.save(self.save_path, model, on_overwrite = 'backup')
def on_monitor(self, model, dataset, algorithm):
"""
Looks whether the model performs better than earlier. If it's the
case, saves the model.
Parameters
----------
model : pylearn2.models.model.Model
model.monitor must contain a channel with name given by self.channel_name
dataset : pylearn2.datasets.dataset.Dataset
not used
algorithm : TrainingAlgorithm
not used
"""
monitor = model.monitor
channels = monitor.channels
channel = channels[self.channel_name]
val_record = channel.val_record
new_cost = self.coeff * val_record[-1]
if new_cost < self.best_cost:
self.best_cost = new_cost
serial.save(self.save_path, model, on_overwrite = 'backup')
# XXX: [Kien] Save best filters.
pv = get_weights_report.get_weights_report(model = model,
dataset = dataset)
pv.save('best_filters.png')
def on_monitor(self, model, dataset, algorithm):
"""
Looks whether the model performs better than earlier. If it's the
case, saves the model.
Parameters
----------
model : pylearn2.models.model.Model
model.monitor must contain a channel with name given by
self.channel_name
dataset : pylearn2.datasets.dataset.Dataset
Not used
algorithm : TrainingAlgorithm
Not used
"""
monitor = model.monitor
channels = monitor.channels
channel = channels[self.channel_name]
val_record = channel.val_record
new_cost = self.coeff * val_record[-1]
if new_cost < self.best_cost:
self.best_cost = new_cost
# Update the tag of the model object before saving it.
self._update_tag(model)
serial.save(self.save_path, model, on_overwrite = 'backup')
def on_monitor(self, model, dataset, algorithm):
"""
Looks whether the model performs better than earlier
- or equally good (modification).
If it's the case, saves the model.
Parameters
----------
model : pylearn2.models.model.Model
model.monitor must contain a channel with name given by
self.channel_name
dataset : pylearn2.datasets.dataset.Dataset
Not used
algorithm : TrainingAlgorithm
Not used
"""
monitor = model.monitor
channels = monitor.channels
channel = channels[self.channel_name]
val_record = channel.val_record
new_cost = val_record[-1]
if self.coeff * new_cost <= self.coeff * self.best_cost and \
monitor._epochs_seen >= self.start_epoch:
self.best_cost = new_cost
# Update the tag of the model object before saving it.
self._update_tag(model)
if self.store_best_model:
self.best_model = deepcopy(model)
if self.save_path is not None:
with log_timing(log, 'Saving to ' + self.save_path):
serial.save(self.save_path, model, on_overwrite='backup')
def on_monitor(self, model, dataset, algorithm):
"""
Save the model if we are on a save epoch.
Parameters
----------
model : pylearn2.models.model.Model
model.monitor must contain a channel with name given by self.channel_name
dataset : pylearn2.datasets.dataset.Dataset
not used
algorithm : TrainingAlgorithm
not used
"""
#monitor = model.monitor
#channels = monitor.channels
#channel = channels[self.channel_name]
#val_record = channel.val_record
#epoch = len(val_record)
epoch = model.monitor.get_epochs_seen()
save_file = '%s_%d.pkl' % (self.save_prefix, epoch)
if np.mod(epoch, self.interval) == 0:
print('Saving model to %s' % save_file)
serial.save(save_file, model, on_overwrite = 'backup')
def train_batch(self, dataset, batch_size):
x = dataset.get_batch_design(batch_size, include_labels=False)
[x_spike, x_slab] = self.preproc(x)
self.batch_train_func(x_spike, x_slab)
# accounting...
self.examples_seen += self.batch_size
self.batches_seen += 1
# modify learning rate multipliers
for (k, iter) in self.lr_mults_it.iteritems():
if iter.next():
print 'self.batches_seen = ', self.batches_seen
self.lr_mults_shrd[k].set_value(iter.value)
print 'lr_mults_shrd[%s] = %f' % (k, iter.value)
self.enforce_constraints()
# save to different path each epoch
if self.my_save_path and \
(self.batches_seen in self.save_at or
self.batches_seen % self.save_every == 0):
fname = self.my_save_path + '_e%i.pkl' % self.batches_seen
print 'Saving to %s ...' % fname,
serial.save(fname, self)
print 'done'
return self.batches_seen < self.max_updates
def on_monitor(self, model, dataset, algorithm):
# this shall never happen but better safe than sorry
if self.predictor is None:
self.setup(model, dataset, algorithm)
# obtaining validating set # TODO: finally we want to have train-validation-test set. Or sth.
valid_x = algorithm.monitoring_dataset['valid'].X
valid_y = algorithm.monitoring_dataset['valid'].y
predictions = self.predictor.get_predictions(valid_x)
threshold, score = self.compute_optimal_threshold_and_score(valid_y, predictions)
self.threshold_list.append(threshold)
self.score_list.append(score)
if self.saving_path is not None and self.save:
if max(self.score_list) == score:
try:
# Make sure that saving does not serialize the dataset
dataset._serialization_guard = SerializationGuard()
save_path = self.saving_path
serial.save(save_path, model,
on_overwrite='backup')
finally:
dataset._serialization_guard = None
print "F1Score1Threshold score", score, "\ncorresponding threshold:", threshold
def on_monitor(self, model, dataset, algorithm):
import numpy as np
# this shall never happen but better safe than sorry
if self.predictor is None:
self.setup(model, dataset, algorithm)
# obtaining validating set #
valid_x = algorithm.monitoring_dataset['valid'].X
valid_y = algorithm.monitoring_dataset['valid'].y
y_pred = self.predictor.get_predictions(valid_x)
y_classes = [np.argmax(pred) for pred in y_pred]
score = f1_score(y_true=valid_y, y_pred=y_classes)
self.score_list.append(score)
if self.saving_path is not None and self.save:
if max(self.score_list) == score:
try:
# Make sure that saving does not serialize the dataset
dataset._serialization_guard = SerializationGuard()
save_path = self.saving_path
serial.save(save_path, model,
on_overwrite='backup')
finally:
dataset._serialization_guard = None
print "F1 score:", score
def main(train_path, out_path, dataset, standardize, C, **kwargs):
stl10 = dataset == 'stl10'
cifar10 = dataset == 'cifar10'
cifar100 = dataset == 'cifar100'
assert stl10 + cifar10 + cifar100 == 1
print 'getting labels and oflds'
train_y, fold_indices = get_labels_and_fold_indices(
cifar10, cifar100, stl10)
gc.collect()
assert train_y is not None
print 'loading training features'
train_X = get_features(train_path, split=False, standardize=standardize)
assert str(train_X.dtype) == 'float32'
if stl10:
assert train_X.shape[0] == 5000
if cifar10 or cifar100:
assert train_X.shape[0] == 50000
assert train_y.shape == (50000, )
print 'training model'
model = train(train_X, train_y, C)
print 'saving model'
serial.save(out_path, model)
def save(self):
"""
Call on_save for Train and TrainCV extensions and serialize trained
models if save_path is set.
"""
# Train extensions
for trainer in self.trainers:
for extension in trainer.extensions:
extension.on_save(trainer.model, trainer.dataset,
trainer.algorithm)
# TrainCV extensions
for extension in self.cv_extensions:
extension.on_save(self.trainers)
# serialize trained models
if self.save_path is not None:
models = [trainer.model for trainer in self.trainers]
try:
for trainer in self.trainers:
trainer.dataset._serialization_guard = SerializationGuard()
if not self.allow_overwrite and os.path.exists(self.save_path):
raise IOError("Trying to overwrite file when not allowed.")
serial.save(self.save_path, models, on_overwrite='backup')
finally:
for trainer in self.trainers:
trainer.dataset._serialization_guard = None
def on_save(self, trainers):
"""
Save best model from each cross-validation fold.
Parameters
----------
trainers : list
List of Train objects belonging to the parent TrainCV object.
"""
if self.save_path is None:
return
models = []
for trainer in trainers:
for extension in trainer.extensions:
if isinstance(extension, MonitorBasedSaveBest):
models.append(extension.best_model)
break
assert len(models) == len(trainers)
try:
for trainer in trainers:
trainer.dataset._serialization_guard = SerializationGuard()
serial.save(self.save_path, models, on_overwrite='backup')
finally:
for trainer in trainers:
trainer.dataset._serialization_guard = None
def get_processed_dataset():
train_path = 'pp_cifar10_train.pkl'
test_path = 'pp_cifar10_test.pkl'
if os.path.exists(train_path) and os.path.exists(test_path):
print 'loading preprocessed data'
trainset = serial.load(train_path)
testset = serial.load(test_path)
else:
print 'loading raw data...'
trainset = cifar10.CIFAR10(which_set="train")
testset = cifar10.CIFAR10(which_set="test")
pipeline = preprocessing.Pipeline()
pipeline.items.append(preprocessing.ExtractPatchesWithPosition(patch_shape=patch_shape, patches_per_image=patches_per_image))
pipeline.items.append(preprocessing.GlobalContrastNormalization(sqrt_bias=10., use_std=True))
pipeline.items.append(preprocessing.PCA(num_components = num_components, keep_var_fraction = keep_var_fraction))
pipeline.items.append(preprocessing.ExtractPatchPairs(patches_per_image = patches_per_image, num_images = train_size, input_width = input_width))
trainset.apply_preprocessor(preprocessor=pipeline, can_fit=True)
# the pkl-ing is having issues, the dataset is maybe too big.
serial.save('pp_cifar10_train.pkl', trainset)
serial.save('pp_cifar10_test.pkl', testset)
# this path will be used for visualizing weights after training is done
trainset.yaml_src = '!pkl: "%s"' % train_path
testset.yaml_src = '!pkl: "%s"' % test_path
return trainset, testset
def on_save(self, trainers):
"""
Save best model from each cross-validation fold.
Parameters
----------
trainers : list
List of Train objects belonging to the parent TrainCV object.
"""
if self.save_path is None:
return
models = []
for trainer in trainers:
for extension in trainer.extensions:
if isinstance(extension, MonitorBasedSaveBest):
models.append(extension.best_model)
break
assert len(models) == len(trainers)
try:
for trainer in trainers:
trainer.dataset._serialization_guard = SerializationGuard()
serial.save(self.save_path, models, on_overwrite='backup')
finally:
for trainer in trainers:
trainer.dataset._serialization_guard = None
def save(self):
"""
Call on_save for Train and TrainCV extensions and serialize trained
models if save_path is set.
"""
# Train extensions
for trainer in self.trainers:
for extension in trainer.extensions:
extension.on_save(trainer.model, trainer.dataset,
trainer.algorithm)
# TrainCV extensions
for extension in self.cv_extensions:
extension.on_save(self.trainers)
# serialize trained models
if self.save_path is not None:
models = [trainer.model for trainer in self.trainers]
try:
for trainer in self.trainers:
trainer.dataset._serialization_guard = SerializationGuard()
if not self.allow_overwrite and os.path.exists(self.save_path):
raise IOError("Trying to overwrite file when not allowed.")
serial.save(self.save_path, models, on_overwrite='backup')
finally:
for trainer in self.trainers:
trainer.dataset._serialization_guard = None
def on_monitor(self, model, dataset, algorithm):
epoch = algorithm.monitor._epochs_seen;
model_file = self.save_path + self.save_prefix + str(epoch) + '.pkl';
with log_timing(log, 'saving model to {}'.format(model_file)):
serial.save(model_file, model, on_overwrite = 'backup')
def create_datasets(cls,
datasets=None,
overwrite=False,
img_dir=DATA_DIR,
output_dir=DATA_DIR):
"""Creates the requested datasets, and writes them to disk.
"""
datasets = datasets or cls.ALL_DATASETS
serial.mkdir(output_dir)
for dataset_name in list(datasets):
file_path_fn = lambda ext: os.path.join(
output_dir, '%s.%s' % (dataset_name, ext))
output_files = dict([(ext, file_path_fn(ext))
for ext in ['pkl', 'npy']])
files_missing = np.any(
[not os.path.isfile(f) for f in output_files.values()])
if overwrite or np.any(files_missing):
print("Loading the %s data" % dataset_name)
dataset = cls(which_set=dataset_name, img_dir=img_dir)
print("Saving the %s data" % dataset_name)
dataset.use_design_loc(output_files['npy'])
serial.save(output_files['pkl'], dataset)
def train_batch(self, dataset, batch_size):
x = dataset.get_batch_design(batch_size, include_labels=False)
self.batch_train_func(x)
# accounting...
self.examples_seen += self.batch_size
self.batches_seen += 1
# modify learning rate multipliers
for (k, iter) in self.lr_mults_it.iteritems():
if iter.next():
print 'self.batches_seen = ', self.batches_seen
self.lr_mults_shrd[k].set_value(iter.value)
print 'lr_mults_shrd[%s] = %f' % (k,iter.value)
self.enforce_constraints()
# save to different path each epoch
if self.my_save_path and \
(self.batches_seen in self.save_at or
self.batches_seen % self.save_every == 0):
fname = self.my_save_path + '_e%i.pkl' % self.batches_seen
print 'Saving to %s ...' % fname,
serial.save(fname, self)
print 'done'
return self.batches_seen < self.max_updates
def main(train_path, out_path, split, **kwargs):
y_fine, y_coarse, fold_indices = get_labels_and_fold_indices()
gc.collect()
print 'loading training features'
train_X = get_features(train_path, split)
#assert train_X.flags.c_contiguous
gc.collect()
assert str(train_X.dtype) == 'float32'
assert train_X.shape[0] == 120
assert y_fine.shape == (120, )
assert y_coarse.shape == (120, )
report = Report(train_path, split)
gc.collect()
print 'making omnivore classifiers'
omnivore_classifiers = get_classifiers('omnivore', train_X, y_fine,
y_coarse, fold_indices)
print 'making fruit classifiers'
fruit_classifiers = get_classifiers('fruit', train_X, y_fine, y_coarse,
fold_indices)
model = train(fold_indices, omnivore_classifiers, fruit_classifiers,
train_X, y_fine, y_coarse, report, **kwargs)
serial.save(out_path + '.model.pkl', model)
report.write(out_path + '.validation_report.txt')
def on_monitor(self, model, dataset, algorithm):
epoch = algorithm.monitor._epochs_seen
model_file = self.save_path + self.save_prefix + str(epoch) + '.pkl'
with log_timing(log, 'saving model to {}'.format(model_file)):
serial.save(model_file, model, on_overwrite='backup')
def compute_ZCA_fast(X, normalize, ZCA_filename="zca"):
zca_preprocessor = preprocessing.ZCA()
zca_preprocessor.set_matrices_save_path(ZCA_filename+".npz")
X = X.astype(np.float32)
if normalize:
X /= 255.0
zca_preprocessor.fit(X.T)
serial.save(ZCA_filename+".pkl", zca_preprocessor)
def save(self, filename):
# Delete data sets
if (hasattr(self.experiment.binary_csp, 'cnt')):
del self.experiment.binary_csp.cnt
if hasattr(self.experiment, 'test_cnt'):
del self.experiment.test_cnt
del self.experiment.cnt
serial.save(filename, self.experiment)
def save(self, filename):
# Delete data sets
if (hasattr(self.experiment.binary_csp, 'cnt')):
del self.experiment.binary_csp.cnt
if hasattr(self.experiment, 'test_cnt'):
del self.experiment.test_cnt
del self.experiment.cnt
serial.save(filename, self.experiment)
def main(train_path,
out_path,
split,
**kwargs):
y_fine, y_coarse, fold_indices = get_labels_and_fold_indices()
gc.collect()
print 'loading training features'
train_X = get_features(train_path, split)
#assert train_X.flags.c_contiguous
gc.collect()
assert str(train_X.dtype) == 'float32'
assert train_X.shape[0] == 120
assert y_fine.shape == (120,)
assert y_coarse.shape == (120,)
report = Report(train_path, split)
gc.collect()
print 'making omnivore classifiers'
omnivore_classifiers = get_classifiers('omnivore',train_X,y_fine,y_coarse,fold_indices)
print 'making fruit classifiers'
fruit_classifiers = get_classifiers('fruit',train_X,y_fine,y_coarse,fold_indices)
print 'loading cifar features'
aux_features = get_features(train_path.replace('aux','train'), False)
print 'loading cifar labels'
aux_labels = CIFAR100(which_set='train').y_coarse
print 'making masks'
mask = np.zeros( aux_labels.shape, dtype='uint8')
for label in [4,11,3,12,7,6]:
mask += (aux_labels == label)
print 'restricting classes'
aux_features = aux_features[mask,:]
aux_labels = aux_labels[mask]
print 'downsampling data'
aux_features = aux_features[0:300,:]
aux_labels = aux_labels[0:300]
print 'main train loop'
model = train(fold_indices, omnivore_classifiers, fruit_classifiers, train_X, y_fine, y_coarse,
aux_features, aux_labels, report, **kwargs)
serial.save(out_path+'.model.pkl', model)
report.write(out_path+'.validation_report.txt')
def main():
data_dir = string.preprocess('${PYLEARN2_DATA_PATH}/stl10')
print('Loading STL10-10 unlabeled and train datasets...')
downsampled_dir = data_dir + '/stl10_32x32'
data = serial.load(downsampled_dir + '/unlabeled.pkl')
supplement = serial.load(downsampled_dir + '/train.pkl')
print('Concatenating datasets...')
data.set_design_matrix(np.concatenate((data.X, supplement.X), axis=0))
del supplement
print("Preparing output directory...")
patch_dir = data_dir + '/stl10_patches_8x8'
serial.mkdir(patch_dir)
README = open(patch_dir + '/README', 'w')
README.write(textwrap.dedent("""
The .pkl files in this directory may be opened in python using
cPickle, pickle, or pylearn2.serial.load.
data.pkl contains a pylearn2 Dataset object defining an unlabeled
dataset of 2 million 6x6 approximately whitened, contrast-normalized
patches drawn uniformly at random from a downsampled (to 32x32)
version of the STL-10 train and unlabeled datasets.
preprocessor.pkl contains a pylearn2 Pipeline object that was used
to extract the patches and approximately whiten / contrast normalize
them. This object is necessary when extracting features for
supervised learning or test set classification, because the
extracted features must be computed using inputs that have been
whitened with the ZCA matrix learned and stored by this Pipeline.
They were created with the pylearn2 script make_stl10_patches.py.
All other files in this directory, including this README, were
created by the same script and are necessary for the other files
to function correctly.
"""))
README.close()
print("Preprocessing the data...")
pipeline = preprocessing.Pipeline()
pipeline.items.append(preprocessing.ExtractPatches(patch_shape=(8, 8),
num_patches=2*1000*1000))
pipeline.items.append(
preprocessing.GlobalContrastNormalization(sqrt_bias=10., use_std=True))
pipeline.items.append(preprocessing.ZCA())
data.apply_preprocessor(preprocessor=pipeline, can_fit=True)
data.use_design_loc(patch_dir + '/data.npy')
serial.save(patch_dir + '/data.pkl', data)
serial.save(patch_dir + '/preprocessor.pkl', pipeline)
def main_loop(self):
self.algorithm.setup(agent=self.agent, environment=self.environment)
i = 0
while True:
rval = self.algorithm.train()
assert rval is None
i += 1
if i % 1000 == 0:
serial.save(self.save_path, self.agent)
print 'saved!'
def save(self):
""" saves the model """
#TODO-- save state of dataset and training algorithm so training can be resumed after a crash
if self.save_path is not None:
print 'saving to ...'+self.save_path
t1 = time.time()
serial.save(self.save_path, self.model)
t2 = time.time()
print '...done. saving took ',(t2-t1),' seconds'
def __extract_sequence(self):
self.model.sequence = 1
sequence = self.model.dataset_yaml_src.split('sequence:')
if len(sequence) > 1:
self.model.sequence = int(sequence[1].split(',')[0])
serial.save(self.filename,self.model)
def __call__(self, model, dataset, algorithm):
if SAVE_MODEL is True:
save_path = 'toy_sparse_' + str(self.current_epoch) + '_epoch.pkl'
save_start = datetime.datetime.now()
serial.save(save_path, model)
save_end = datetime.datetime.now()
delta = (save_end - save_start)
print 'saving model...done. saving took', str(delta)
self.current_epoch += 1
def __extract_sequence(self):
self.model.sequence = 1
sequence = self.model.dataset_yaml_src.split('sequence:')
if len(sequence) > 1:
self.model.sequence = int(sequence[1].split(',')[0])
serial.save(self.filename, self.model)
def convert_net():
for (dirpath, dirnames, filenames) in walk('../networks/'):
for file in filenames:
model = serial.load(dirpath+file)
try:
print "Saving {} parameters".format(file)
pvals = model.get_all_params_values()
serial.save("{}/{}_PARAMS".format(dirpath,file), pvals)
except Exception, e:
print e
def getpx_r(file): d = serial.load(file) d=d.reshape((50000,3,32,32)) for i in range(0,50000): for j in range(0,3): d[i,j]=numpy.fliplr(d[i,j]) d=d.reshape((50000,3072)) serial.save(file+"_r",d)
def save(self):
""" saves the model """
#TODO-- save state of dataset and training algorithm so training can be resumed after a crash
if self.save_path is not None:
print 'saving to ...'+self.save_path
t1 = time.time()
serial.save(self.save_path, self.model)
t2 = time.time()
print '...done. saving took ',(t2-t1),' seconds'
def __call__(self, model, dataset, algorithm):
if SAVE_MODEL is True:
save_path = 'cifar10_grbm' + str(self.current_epoch) + '_epoch.pkl'
save_start = datetime.datetime.now()
serial.save(save_path, model)
save_end = datetime.datetime.now()
delta = (save_end - save_start)
print 'saving model...done. saving took', str(delta)
self.current_epoch += 1
def __call__(self, model, dataset, algorithm):
if self._count > 0 and self._count % self.save_freq == 0:
self.avg()
saved_params = {}
for param in model.get_params():
saved_params[param] = param.get_value()
param.set_value(self.param_to_mean[param].get_value())
serial.save(self.save_path, model)
for param in model.get_params():
param.set_value(saved_params[param])
self._count += 1
def save(self):
"""Saves the model."""
#TODO-- save state of dataset and training algorithm so training can be
# resumed after a crash
if self.save_path is not None:
print 'saving to', self.save_path, '...'
save_start = datetime.datetime.now()
serial.save(self.save_path, self.model)
save_end = datetime.datetime.now()
delta = (save_end - save_start)
print '...done. saving took', str(delta)
def main(train_path,
out_path,
split,
dataset,
standardize,
**kwargs):
stl10 = dataset == 'stl10'
cifar10 = dataset == 'cifar10'
cifar100 = dataset == 'cifar100'
assert stl10 + cifar10 + cifar100 == 1
if mem:
print 'mem usage before getting labels and folds '+str(mem.usage())
train_y, fold_indices = get_labels_and_fold_indices(cifar10, cifar100, stl10)
if mem:
print 'mem usage after getting labels and folds '+str(mem.usage())
gc.collect()
assert train_y is not None
print 'loading training features'
if mem:
print 'mem usage before getting features '+str(mem.usage())
train_X = get_features(train_path, split, standardize)
if not train_X.flags.c_contiguous:
print 'not C contiguous, reshaping'
assert len(train_X.shape) == 2
train_X = np.ascontiguousarray(train_X)
assert train_X.flags.c_contiguous
print 'success, contiguous now'
gc.collect()
if mem:
print 'mem usage after getting features '+str(mem.usage())
if str(train_X.dtype) != 'float32':
warnings.warn('Your features are not float32, you may be wasting memory')
if stl10:
assert train_X.shape[0] == 5000
if cifar10 or cifar100:
assert train_X.shape[0] == 50000
assert train_y.shape == (50000,)
report = Report(train_path, split, stl10, cifar10, cifar100)
gc.collect()
if mem:
print 'mem usage before calling train: '+str(mem.usage())
model = train(fold_indices, train_X, train_y, report, **kwargs)
serial.save(out_path+'.model.pkl', model)
report.write(out_path+'.validation_report.txt')
def save(self):
"""Saves the model."""
#TODO-- save state of dataset and training algorithm so training can be
# resumed after a crash
if self.save_path is not None:
print 'saving to', self.save_path, '...'
save_start = datetime.datetime.now()
serial.save(self.save_path, self.model)
save_end = datetime.datetime.now()
delta = (save_end - save_start)
print '...done. saving took', str(delta)
def on_monitor(self, model, dataset, algorithm):
is_save_interval = (
model.batches_seen in self.save_at or
model.batches_seen % self.save_every == 0)
if self.my_save_path and is_save_interval:
fname = self.my_save_path + '_e%i.pkl' % model.batches_seen
model.save_path = fname
print 'Saving to %s ...' % fname,
serial.save(fname, model)
print 'done'
def save(stats, targets, fnames, save_path):
#dataDict = {}
#for i in range(len(stats)):
# peturbation = metaData[i]['perturbation']
# flip = metaData[i]['flipped']
# stat = stats[i,:]
# if not perturbation in dataDict:
# dataDict[perturbation].append((flip,
#d = zip(stats, targets)
#data=dict(zip(clipIDs, d))
serial.save(save_path, {'x': stats, 'y': targets, 'path': fnames})
def main():
data_dir = string_utils.preprocess('${PYLEARN2_DATA_PATH}')
print('Loading CIFAR-100 train dataset...')
data = CIFAR100(which_set='train')
print("Preparing output directory...")
patch_dir = data_dir + '/cifar100/cifar100_patches'
serial.mkdir(patch_dir)
README = open(patch_dir + '/README', 'w')
README.write(
textwrap.dedent("""
The .pkl files in this directory may be opened in python using
cPickle, pickle, or pylearn2.serial.load.
data.pkl contains a pylearn2 Dataset object defining an unlabeled
dataset of 2 million 6x6 approximately whitened, contrast-normalized
patches drawn uniformly at random from the CIFAR-100 train set.
preprocessor.pkl contains a pylearn2 Pipeline object that was used
to extract the patches and approximately whiten / contrast normalize
them. This object is necessary when extracting features for
supervised learning or test set classification, because the
extracted features must be computed using inputs that have been
whitened with the ZCA matrix learned and stored by this Pipeline.
They were created with the pylearn2 script make_cifar100_patches.py.
All other files in this directory, including this README, were
created by the same script and are necessary for the other files
to function correctly.
"""))
README.close()
print("Preprocessing the data...")
pipeline = preprocessing.Pipeline()
pipeline.items.append(
preprocessing.ExtractPatches(patch_shape=(6, 6),
num_patches=2 * 1000 * 1000))
pipeline.items.append(
preprocessing.GlobalContrastNormalization(sqrt_bias=10., use_std=True))
pipeline.items.append(preprocessing.ZCA())
data.apply_preprocessor(preprocessor=pipeline, can_fit=True)
data.use_design_loc(patch_dir + '/data.npy')
serial.save(patch_dir + '/data.pkl', data)
serial.save(patch_dir + '/preprocessor.pkl', pipeline)
def __call__(self, model, dataset, algorithm):
if self._count == self.start:
self._worker = _PolyakWorker(model)
algorithm.update_callbacks.append(self._worker)
#HACK
model.add_polyak_channels(self._worker.param_to_mean,
algorithm.monitoring_dataset)
elif self._count > self.start and self._count % self.save_freq == 0:
saved_params = {}
for param in model.get_params():
saved_params[param] = param.get_value()
param.set_value(self._worker.param_to_mean[param].get_value())
serial.save(self.save_path, model)
for param in model.get_params():
param.set_value(saved_params[param])
self._count += 1
def main_loop(self):
self.algorithm.setup(agent=self.agent, environment=self.environment)
i = 0
for param in self.agent.get_params():
assert not np.any(np.isnan(param.get_value())), (i, param.name)
assert not np.any(np.isinf(param.get_value())), (i, param.name)
while True:
rval = self.algorithm.train()
assert rval is None
i += 1
for param in self.agent.get_params():
assert not np.any(np.isnan(param.get_value())), (i, param.name)
assert not np.any(np.isinf(param.get_value())), (i, param.name)
if i % 1000 == 0:
serial.save(self.save_path, self.agent)
logger.info('saved!')
def test_pkl_yaml_src_field():
"""
Tests a regression where yaml_src wasn't getting correctly set on pkls.
"""
try:
fd, fn = mkstemp()
close(fd)
o = DumDum()
o.x = ('a', 'b', 'c')
serial.save(fn, o)
yaml = '!pkl: \'' + fn + '\'\n'
loaded = load(yaml)
assert loaded.x == ('a', 'b', 'c')
assert loaded.yaml_src == yaml
finally:
os.remove(fn)