def __init__(self,
batch_size=100,
ini_learning_rate=0.001,
max_epochs=100,
patience=10,
y_tensor_type=T.ivector,
L2=1e-4,
objective=mean_categorical_crossentropy,
adapt_learn_rate=get_constant(),
update_function=get_update_adam(),
valid_batch_iter=get_batch_iterator(),
train_batch_iter=get_batch_iterator(),
use_weights=False,
samples_per_epoch=None,
shuffle_train=True,
report_dices=False,
use_mask=False,
refinement_strategy=None):
"""
Constructor
"""
self.batch_size = batch_size
self.ini_learning_rate = ini_learning_rate
self.max_epochs = max_epochs
self.patience = patience
self.y_tensor_type = y_tensor_type
self.L2 = L2
self.objective = objective
self.adapt_learn_rate = adapt_learn_rate
self.update_function = update_function
self.valid_batch_iter = valid_batch_iter
self.train_batch_iter = train_batch_iter
self.use_weights = use_weights
self.samples_per_epoch = samples_per_epoch
self.shuffle_train = shuffle_train
self.report_dices = report_dices
self.use_mask = use_mask
self.refinement_strategy = refinement_strategy
if refinement_strategy is None:
self.refinement_strategy = RefinementStrategy()
def get_batch_iterator():
"""
Get batch iterator
"""
def batch_iterator(batch_size, k_samples, shuffle):
return BatchIterator(batch_size=batch_size, prepare=prepare, k_samples=k_samples, shuffle=shuffle)
return batch_iterator
refinement_strategy = RefinementStrategy(n_refinement_steps=5, refinement_patience=5, learn_rate_multiplier=0.5)
train_strategy = TrainingStrategy(
batch_size=SAMPLE_SIZE,
ini_learning_rate=0.001,
max_epochs=1000,
patience=5,
L2=None,
samples_per_epoch=None,
refinement_strategy=refinement_strategy,
y_tensor_type=T.vector,
objective=mean_pixel_binary_crossentropy,
adapt_learn_rate=get_constant(),
update_function=get_update_adam(),
train_batch_iter=get_batch_iterator(),
valid_batch_iter=get_batch_iterator()
)