def final_fit(self,
x_train,
y_train,
x_test,
y_test,
trainer_args=None,
retrain=False):
"""Final training after found the best architecture.
Args:
x_train: A numpy.ndarray of training data.
y_train: A numpy.ndarray of training targets.
x_test: A numpy.ndarray of testing data.
y_test: A numpy.ndarray of testing targets.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
y_train = self.transform_y(y_train)
y_test = self.transform_y(y_test)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
searcher = self.load_searcher()
graph = searcher.load_best_model()
if retrain:
graph.weighted = False
_, _1, graph = train((graph, train_data, test_data, trainer_args, None,
self.metric, self.loss, self.verbose))
def fit(self, x, y, trainer_args=None):
"""Trains the model on the dataset given.
Args:
x: A numpy.ndarray instance containing the training data or the training data combined with the
validation data.
y: A numpy.ndarray instance containing the label of the training data. or the label of the training data
combined with the validation label.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
"""
validate_xy(x, y)
self.y_encoder.fit(y)
y = self.y_encoder.transform(y)
# Divide training data into training and testing data.
validation_set_size = int(len(y) * Constant.VALIDATION_SET_SIZE)
validation_set_size = min(validation_set_size, 500)
validation_set_size = max(validation_set_size, 1)
x_train, x_test, y_train, y_test = train_test_split(x, y,
test_size=validation_set_size,
random_state=42)
#initialize data_transformer
self.data_transformer = self.data_transformer_class(x_train)
# Wrap the data into DataLoaders
train_loader = self.data_transformer.transform_train(x_train, y_train)
test_loader = self.data_transformer.transform_test(x_test, y_test)
self.generator = self._init_generator(self.y_encoder.n_classes, x_train.shape[1:])
graph = self.generator.generate()
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
_, _1, self.graph = train(None, graph, train_loader, test_loader,
trainer_args, self.metric, self.loss,
self.verbose, self.path)
def final_fit(self, x_train, y_train, x_test, y_test, trainer_args=None, retrain=False):
"""Final training after found the best architecture.
Args:
x_train: A numpy.ndarray of training data.
y_train: A numpy.ndarray of training targets.
x_test: A numpy.ndarray of testing data.
y_test: A numpy.ndarray of testing targets.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
y_train = self.transform_y(y_train)
y_test = self.transform_y(y_test)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
searcher = self.load_searcher()
graph = searcher.load_best_model()
if retrain:
graph.weighted = False
_, _1, graph = train((graph, train_data, test_data, trainer_args, None, self.metric, self.loss, self.verbose))
def fit(self, x, y, trainer_args=None, retrain=False):
"""Trains the model on the given dataset.
Args:
x: A numpy.ndarray instance containing the training data or the training data combined with the
validation data.
y: A numpy.ndarray instance containing the label of the training data. or the label of the training data
combined with the validation label.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
x = self.preprocess(x)
# Divide training data into training and testing data.
validation_set_size = int(len(y) * Constant.VALIDATION_SET_SIZE)
validation_set_size = min(validation_set_size, 500)
validation_set_size = max(validation_set_size, 1)
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=validation_set_size, random_state=42)
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
y_train = self.transform_y(y_train)
y_test = self.transform_y(y_test)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
if retrain:
self.graph.weighted = False
_, _1, self.graph = train(None, self.graph, train_data, test_data,
trainer_args, self.metric, self.loss,
self.verbose, self.path)
def fit(self, x, y, trainer_args=None):
"""Trains the model on the dataset given.
Args:
x: A numpy.ndarray instance containing the training data or the training data combined with the
validation data.
y: A numpy.ndarray instance containing the label of the training data. or the label of the training data
combined with the validation label.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
"""
validate_xy(x, y)
self.y_encoder.fit(y)
y = self.y_encoder.transform(y)
# Divide training data into training and testing data.
validation_set_size = int(len(y) * Constant.VALIDATION_SET_SIZE)
validation_set_size = min(validation_set_size, 500)
validation_set_size = max(validation_set_size, 1)
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=validation_set_size, random_state=42)
# initialize data_transformer
self.data_transformer = self.data_transformer_class(x_train)
# Wrap the data into DataLoaders
train_loader = self.data_transformer.transform_train(x_train, y_train)
test_loader = self.data_transformer.transform_test(x_test, y_test)
self.generator = self._init_generator(self.y_encoder.n_classes,
x_train.shape[1:])
graph = self.generator.generate()
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
_, _1, self.graph = train(None, graph, train_loader, test_loader,
trainer_args, self.metric, self.loss,
self.verbose, self.path)
def fit(self,
x_train,
y_train,
x_test,
y_test,
trainer_args=None,
retrain=False):
"""further training of the model (graph).
Args:
x_train: A numpy.ndarray of training data.
y_train: A numpy.ndarray of training targets.
x_test: A numpy.ndarray of testing data.
y_test: A numpy.ndarray of testing targets.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
x_train = self.preprocess(x_train)
x_test = self.preprocess(x_test)
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
y_train = self.transform_y(y_train)
y_test = self.transform_y(y_test)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
if retrain:
self.graph.weighted = False
_, _1, self.graph = train(None, self.graph, train_data, test_data,
trainer_args, self.metric, self.loss,
self.verbose, self.path)
def fit(self, x_train, y_train, x_test, y_test, trainer_args=None, retrain=False):
"""further training of the model (graph).
Args:
x_train: A numpy.ndarray of training data.
y_train: A numpy.ndarray of training targets.
x_test: A numpy.ndarray of testing data.
y_test: A numpy.ndarray of testing targets.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
x_train = self.preprocess(x_train)
x_test = self.preprocess(x_test)
if trainer_args is None:
trainer_args = {'max_no_improvement_num': 30}
y_train = self.transform_y(y_train)
y_test = self.transform_y(y_test)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
if retrain:
self.graph.weighted = False
_, _1, self.graph = train(None, self.graph, train_data, test_data, trainer_args,
self.metric, self.loss, self.verbose, self.path)
def final_fit(self,
x_train,
y_train,
x_test,
y_test,
trainer_args=None,
retrain=False):
"""Final training after found the best architecture.
Args:
x_train: An numpy.ndarray of training data.
y_train: An numpy.ndarray of training targets.
x_test: An numpy.ndarray of testing data.
y_test: An numpy.ndarray of testing targets.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructure.
retrain: A boolean of whether reinitialize the weights of the model.
"""
if trainer_args is None:
trainer_args = {}
y_train = self.y_encoder.transform(y_train)
y_test = self.y_encoder.transform(y_test)
searcher = self.load_searcher()
graph = searcher.load_best_model()
if retrain:
graph.weighted = False
_, _1, graph = train(
(graph, x_train, y_train, x_test, y_test, trainer_args, None))
def final_fit(self, train_data, test_data, trainer_args=None, retrain=False):
"""Final training after found the best architecture.
Args:
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
train_data: A DataLoader instance representing the training data
test_data: A DataLoader instance representing the testing data
"""
searcher = self._load_searcher()
graph = searcher.load_best_model()
if retrain:
graph.weighted = False
_, _1, graph = train((graph, train_data, test_data, trainer_args, None, self.metric, self.loss, self.verbose))
def final_fit(self,
train_data,
test_data,
trainer_args=None,
retrain=False):
"""Final training after found the best architecture.
Args:
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
train_data: A DataLoader instance representing the training data.
test_data: A DataLoader instance representing the testing data.
"""
graph = self.searcher.load_best_model()
if retrain:
graph.weighted = False
_, _1, graph = train(None, graph, train_data, test_data, trainer_args,
self.metric, self.loss, self.verbose, self.path)
self.searcher.replace_model(graph, self.searcher.get_best_model_id())
pickle_to_file(self, os.path.join(self.path, 'module'))
def fit(self, x_train, y_train, time_limit=None):
"""Trains the model on the dataset given.
Args:
x_train: A numpy.ndarray instance containing the training data,
or the training data combined with the validation data.
y_train: A numpy.ndarray instance containing the label of the training data,
or the label of the training data combined with the validation label.
time_limit: A dictionary containing the parameters of the ModelTrainer constructor.
"""
validate_xy(x_train, y_train)
self.resize_shape = compute_image_resize_params(x_train)
x_train = self.preprocess(x_train)
self.y_encoder.fit(y_train)
y_train = self.transform_y(y_train)
# Divide training data into training and testing data.
validation_set_size = int(len(y_train) * Constant.VALIDATION_SET_SIZE)
validation_set_size = min(validation_set_size, 500)
validation_set_size = max(validation_set_size, 1)
x_train_new, x_test, y_train_new, y_test = train_test_split(
x_train, y_train, test_size=validation_set_size, random_state=42)
# initialize data_transformer
self.data_transformer = ImageDataTransformer(x_train_new)
# Wrap the data into DataLoaders
train_loader = self.data_transformer.transform_train(
x_train_new, y_train_new)
test_loader = self.data_transformer.transform_test(x_test, y_test)
self.generator = self._init_generator(self.y_encoder.n_classes,
x_train_new.shape[1:])
graph = self.generator.generate()
if time_limit is None:
time_limit = {'max_no_improvement_num': 30}
_, _1, self.graph = train(None, graph, train_loader, test_loader,
time_limit, self.metric, self.loss,
self.verbose, self.path)
def final_fit(self, train_data, test_data, trainer_args=None, retrain=False):
"""Final training after found the best architecture.
Args:
train_data: A DataLoader instance representing the training data.
test_data: A DataLoader instance representing the testing data.
trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
retrain: A boolean of whether reinitialize the weights of the model.
"""
graph = self.searcher.load_best_model()
if retrain:
graph.weighted = False
_, _1, graph = train(None, graph,
train_data,
test_data,
trainer_args,
self.metric,
self.loss,
self.verbose,
self.path)
self.searcher.replace_model(graph, self.searcher.get_best_model_id())
pickle_to_file(self, os.path.join(self.path, 'module'))
