def run():
data_seed = 0
date = datetime.now()
n_labeled = 4000
result_dir = "{root}/{dataset}/{model}/{date:%Y-%m-%d_%H:%M:%S}/{seed}".format(
root='results/final_eval',
dataset='cifar10_{}'.format(n_labeled),
model='mean_teacher',
date=date,
seed=data_seed
)
model = Model(result_dir=result_dir)
model['flip_horizontally'] = True
model['max_consistency_coefficient'] = 100.0 * n_labeled / 50000
model['adam_beta_2_during_rampup'] = 0.999
model['ema_decay_during_rampup'] = 0.999
model['normalize_input'] = False # Keep ZCA information
model['rampdown_length'] = 25000
model['training_length'] = 150000
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
cifar = Cifar10ZCA(data_seed, n_labeled)
training_batches = minibatching.training_batches(cifar.training)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(cifar.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed, ema_decay_during_rampup, ema_decay_after_rampup,
test_phase=False, n_labeled=250, n_extra_unlabeled=0, model_type='mean_teacher'):
minibatch_size = 100
hyperparams = model_hyperparameters(model_type, n_labeled, n_extra_unlabeled)
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
svhn = SVHN(n_labeled=n_labeled,
n_extra_unlabeled=n_extra_unlabeled,
data_seed=data_seed,
test_phase=test_phase)
model['ema_consistency'] = hyperparams['ema_consistency']
model['max_consistency_cost'] = hyperparams['max_consistency_cost']
model['apply_consistency_to_labeled'] = hyperparams['apply_consistency_to_labeled']
model['training_length'] = hyperparams['training_length']
model['ema_decay_during_rampup'] = ema_decay_during_rampup
model['ema_decay_after_rampup'] = ema_decay_after_rampup
training_batches = minibatching.training_batches(svhn.training,
minibatch_size,
hyperparams['n_labeled_per_batch'])
evaluation_batches_fn = minibatching.evaluation_epoch_generator(svhn.evaluation,
minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(test_phase, data_seed, n_labeled, training_length, rampdown_length):
minibatch_size = 100
n_labeled_per_batch = 100
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
cifar = SVHN(n_labeled=n_labeled,
data_seed=data_seed,
test_phase=test_phase)
model['ema_consistency'] = True
model['max_consistency_cost'] = 0.0
model['apply_consistency_to_labeled'] = False
model['rampdown_length'] = rampdown_length
model['training_length'] = training_length
# Turn off augmentation
model['translate'] = False
model['flip_horizontally'] = False
training_batches = minibatching.training_batches(cifar.training,
minibatch_size,
n_labeled_per_batch)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(
cifar.evaluation, minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed, num_logits, logit_distance_cost, test_phase=False, n_labeled=500, n_extra_unlabeled=0, model_type='mean_teacher'):
minibatch_size = 100
hyperparams = model_hyperparameters(model_type, n_labeled, n_extra_unlabeled)
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
svhn = SVHN(n_labeled=n_labeled,
n_extra_unlabeled=n_extra_unlabeled,
data_seed=data_seed,
test_phase=test_phase)
model['ema_consistency'] = hyperparams['ema_consistency']
model['max_consistency_cost'] = hyperparams['max_consistency_cost']
model['apply_consistency_to_labeled'] = hyperparams['apply_consistency_to_labeled']
model['training_length'] = hyperparams['training_length']
model['num_logits'] = num_logits
model['logit_distance_cost'] = logit_distance_cost
training_batches = minibatching.training_batches(svhn.training,
minibatch_size,
hyperparams['n_labeled_per_batch'])
evaluation_batches_fn = minibatching.evaluation_epoch_generator(svhn.evaluation,
minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(result_dir, test_phase, n_labeled, data_seed, model_type):
minibatch_size = 100
hyperparams = model_hyperparameters(model_type, n_labeled)
tf.reset_default_graph()
model = Model(result_dir=result_dir)
cifar = Cifar10ZCA(n_labeled=n_labeled,
data_seed=data_seed,
test_phase=test_phase)
model['flip_horizontally'] = True
model['ema_consistency'] = hyperparams['ema_consistency']
model['max_consistency_coefficient'] = hyperparams['max_consistency_coefficient']
model['apply_consistency_to_labeled'] = hyperparams['apply_consistency_to_labeled']
model['adam_beta_2_during_rampup'] = 0.999
model['ema_decay_during_rampup'] = 0.999
model['normalize_input'] = False # Keep ZCA information
model['rampdown_length'] = 25000
model['training_length'] = 150000
training_batches = minibatching.training_batches(cifar.training,
minibatch_size,
hyperparams['n_labeled_per_batch'])
evaluation_batches_fn = minibatching.evaluation_epoch_generator(cifar.evaluation,
minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(result_dir, test_phase, n_labeled, n_extra_unlabeled, data_seed,
model_type):
minibatch_size = 100
hyperparams = model_hyperparameters(model_type, n_labeled,
n_extra_unlabeled)
tf.reset_default_graph()
model = Model(result_dir=result_dir)
svhn = SVHN(n_labeled=n_labeled,
n_extra_unlabeled=n_extra_unlabeled,
data_seed=data_seed,
test_phase=test_phase)
model['rampdown_length'] = 0
model['ema_consistency'] = hyperparams['ema_consistency']
model['max_consistency_coefficient'] = hyperparams[
'max_consistency_coefficient']
model['apply_consistency_to_labeled'] = hyperparams[
'apply_consistency_to_labeled']
model['training_length'] = hyperparams['training_length']
training_batches = minibatching.training_batches(
svhn.training, minibatch_size, hyperparams['n_labeled_per_batch'])
evaluation_batches_fn = minibatching.evaluation_epoch_generator(
svhn.evaluation, minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(test_phase, data_seed, n_labeled, training_length, rampdown_length):
minibatch_size = 100
n_labeled_per_batch = 100
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
cifar = SVHN(n_labeled=n_labeled,
data_seed=data_seed,
test_phase=test_phase)
model['ema_consistency'] = True
model['max_consistency_cost'] = 0.0
model['apply_consistency_to_labeled'] = False
model['rampdown_length'] = rampdown_length
model['training_length'] = training_length
# Turn off augmentation
model['translate'] = False
model['flip_horizontally'] = False
training_batches = minibatching.training_batches(cifar.training,
minibatch_size,
n_labeled_per_batch)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(cifar.evaluation,
minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run():
data_seed = 0
date = datetime.now()
n_labeled = 500
n_extra_unlabeled = 0
result_dir = "{root}/{dataset}/{model}/{date:%Y-%m-%d_%H:%M:%S}/{seed}".format(
root='results/final_eval',
dataset='svhn_{}_{}'.format(n_labeled, n_extra_unlabeled),
model='mean_teacher',
date=date,
seed=data_seed)
model = Model(result_dir=result_dir)
model['rampdown_length'] = 0
model['training_length'] = 180000
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
svhn = SVHN(data_seed, n_labeled, n_extra_unlabeled)
training_batches = minibatching.training_batches(svhn.training,
n_labeled_per_batch=1)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(
svhn.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run(test_phase, data_seed, n_labeled, training_length, rampdown_length):
minibatch_size = 100
n_labeled_per_batch = 100
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
cifar = Cifar10ZCA(n_labeled=n_labeled,
data_seed=data_seed,
test_phase=test_phase)
model['flip_horizontally'] = True
model['ema_consistency'] = True
model['max_consistency_cost'] = 0.0
model['apply_consistency_to_labeled'] = False
model['adam_beta_2_during_rampup'] = 0.999
model['ema_decay_during_rampup'] = 0.999
model['normalize_input'] = False # Keep ZCA information
model['rampdown_length'] = rampdown_length
model['training_length'] = training_length
training_batches = minibatching.training_batches(cifar.training,
minibatch_size,
n_labeled_per_batch)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(
cifar.evaluation, minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(test_phase, n_labeled, n_extra_unlabeled, data_seed, model_type):
minibatch_size = 100
hyperparams = model_hyperparameters(model_type, n_labeled, n_extra_unlabeled)
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
svhn = SVHN(n_labeled=n_labeled,
n_extra_unlabeled=n_extra_unlabeled,
data_seed=data_seed,
test_phase=test_phase)
model['ema_consistency'] = hyperparams['ema_consistency']
model['max_consistency_cost'] = hyperparams['max_consistency_cost']
model['apply_consistency_to_labeled'] = hyperparams['apply_consistency_to_labeled']
model['training_length'] = hyperparams['training_length']
# Turn off augmentation
model['translate'] = False
model['flip_horizontally'] = False
training_batches = minibatching.training_batches(svhn.training,
minibatch_size,
hyperparams['n_labeled_per_batch'])
evaluation_batches_fn = minibatching.evaluation_epoch_generator(svhn.evaluation,
minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed, dropout, input_noise, augmentation,
test_phase=False, n_labeled=250, n_extra_unlabeled=0, model_type='mean_teacher'):
minibatch_size = 100
hyperparams = model_hyperparameters(model_type, n_labeled, n_extra_unlabeled)
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
svhn = SVHN(n_labeled=n_labeled,
n_extra_unlabeled=n_extra_unlabeled,
data_seed=data_seed,
test_phase=test_phase)
model['ema_consistency'] = hyperparams['ema_consistency']
model['max_consistency_cost'] = hyperparams['max_consistency_cost']
model['apply_consistency_to_labeled'] = hyperparams['apply_consistency_to_labeled']
model['training_length'] = hyperparams['training_length']
model['student_dropout_probability'] = dropout
model['teacher_dropout_probability'] = dropout
model['input_noise'] = input_noise
model['translate'] = augmentation
training_batches = minibatching.training_batches(svhn.training,
minibatch_size,
hyperparams['n_labeled_per_batch'])
evaluation_batches_fn = minibatching.evaluation_epoch_generator(svhn.evaluation,
minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(test_phase, data_seed, n_labeled, training_length, rampdown_length):
minibatch_size = 100
n_labeled_per_batch = 100
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
cifar = Cifar10ZCA(n_labeled=n_labeled,
data_seed=data_seed,
test_phase=test_phase)
model['flip_horizontally'] = True
model['ema_consistency'] = True
model['max_consistency_cost'] = 0.0
model['apply_consistency_to_labeled'] = False
model['adam_beta_2_during_rampup'] = 0.999
model['ema_decay_during_rampup'] = 0.999
model['normalize_input'] = False # Keep ZCA information
model['rampdown_length'] = rampdown_length
model['training_length'] = training_length
training_batches = minibatching.training_batches(cifar.training,
minibatch_size,
n_labeled_per_batch)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(cifar.evaluation,
minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed=0):
n_labeled = 1000
n_extra_unlabeled = 0
model = Model(RunContext(__file__, 0))
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
svhn = SVHN(data_seed, n_labeled, n_extra_unlabeled)
training_batches = minibatching.training_batches(svhn.training,
n_labeled_per_batch=1)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(
svhn.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed=0):
n_labeled = 500
n_extra_unlabeled = 0
model = Model(RunContext(__file__, 0))
model['rampdown_length'] = 0
model['rampup_length'] = 5000
model['training_length'] = 40000
model['max_consistency_cost'] = 50.0
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
svhn = SVHN(data_seed, n_labeled, n_extra_unlabeled)
training_batches = minibatching.training_batches(svhn.training, n_labeled_per_batch=50)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(svhn.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed=0):
n_labeled = 500
n_extra_unlabeled = 0
model = Model(RunContext(__file__, 0))
model['rampdown_length'] = 0
model['rampup_length'] = 5000
model['training_length'] = 40000
model['max_consistency_cost'] = 50.0
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
svhn = SVHN(data_seed, n_labeled, n_extra_unlabeled)
training_batches = minibatching.training_batches(svhn.training, n_labeled_per_batch=50)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(svhn.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed=0):
n_labeled = 4000
model = Model(RunContext(__file__, 0))
model['flip_horizontally'] = True
model['normalize_input'] = False # Keep ZCA information
model['rampdown_length'] = 0
model['rampup_length'] = 5000
model['training_length'] = 40000
model['max_consistency_cost'] = 50.0
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
cifar = Cifar10ZCA(data_seed, n_labeled)
training_batches = minibatching.training_batches(cifar.training, n_labeled_per_batch=50)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(cifar.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run(data_seed=0):
n_labeled = 4000
model = Model(RunContext(__file__, 0))
model['flip_horizontally'] = True
model['normalize_input'] = False # Keep ZCA information
model['rampdown_length'] = 0
model['rampup_length'] = 5000
model['training_length'] = 40000
model['max_consistency_cost'] = 50.0
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
cifar = Cifar10ZCA(data_seed, n_labeled)
training_batches = minibatching.training_batches(cifar.training, n_labeled_per_batch=50)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(cifar.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run():
data_seed = 0
n_labeled = 4000
model = Model(RunContext(__file__, 0))
model['flip_horizontally'] = True
model['max_consistency_cost'] = 100.0 * n_labeled / 50000
model['adam_beta_2_during_rampup'] = 0.999
model['ema_decay_during_rampup'] = 0.999
model['normalize_input'] = False # Keep ZCA information
model['rampdown_length'] = 25000
model['training_length'] = 150000
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
cifar = Cifar10ZCA(data_seed, n_labeled)
training_batches = minibatching.training_batches(cifar.training)
evaluation_batches_fn = minibatching.evaluation_epoch_generator(
cifar.evaluation)
model.train(training_batches, evaluation_batches_fn)
def run(test_phase, data_seed, model_type):
minibatch_size = 100
# fixed and pre-calculated (from file)
n_labeled = 127737
n_all = 630346 + 127737
hyperparams = model_hyperparameters(model_type, n_labeled, n_all)
tf.reset_default_graph()
model = Model(RunContext(__file__, data_seed))
train_filename = '/root/storage/hdd/eyes_color/descriptions_files/train_labeled_unlabeled_1st_stage.txt'
test_filename = '/root/storage/hdd/eyes_color/descriptions_files/test_base_path.txt'
eye_dataset = Eye24(imgs_dir='/root/storage/hdd/',
train_filename=train_filename,
test_filename=test_filename)
model['flip_horizontally'] = True
model['ema_consistency'] = hyperparams['ema_consistency']
model['max_consistency_cost'] = hyperparams['max_consistency_cost']
model['apply_consistency_to_labeled'] = hyperparams[
'apply_consistency_to_labeled']
model['adam_beta_2_during_rampup'] = 0.999
model['ema_decay_during_rampup'] = 0.999
model['normalize_input'] = False # Keep ZCA information # TODO not sure
model['rampdown_length'] = 35000
model['training_length'] = 250000
training_batches = minibatching.training_batches_transform(
eye_dataset.training, train_pipeline, minibatch_size)
evaluation_batches_fn = minibatching.evaluation_epoch_generator_transform(
eye_dataset.evaluation, eval_pipeline, minibatch_size)
tensorboard_dir = model.save_tensorboard_graph()
LOG.info("Saved tensorboard graph to %r", tensorboard_dir)
model.train(training_batches, evaluation_batches_fn)
