def get_classification_dataloaders():
x_train = np.random.rand(200, 28, 28, 3)
y_train = np.random.rand(200, 3)
x_test = np.random.rand(190, 28, 28, 3)
y_test = np.random.rand(190, 3)
data_transformer = DataTransformer(x_train, augment=True)
train_data = data_transformer.transform_train(x_train, y_train)
test_data = data_transformer.transform_test(x_test, y_test)
return train_data, test_data
def get_classification_data_loaders():
x_train = np.random.rand(200, 28, 28, 3)
y_train = np.random.rand(200, 3)
x_test = np.random.rand(190, 28, 28, 3)
y_test = np.random.rand(190, 3)
data_transformer = DataTransformer(x_train, augment=True)
train_data = data_transformer.transform_train(x_train, y_train)
test_data = data_transformer.transform_test(x_test, y_test)
return train_data, test_data
def get_processed_data():
x_train = np.random.rand(20, 28, 28, 3)
y_train = np.random.rand(20, 3)
x_test = np.random.rand(10, 28, 28, 3)
y_test = np.random.rand(10, 3)
data_transformer = DataTransformer(x_train, augment=True)
train_data = data_transformer.transform_train(x_train, y_train)
test_data = data_transformer.transform_test(x_test, y_test)
return train_data, test_data
def fit(self, x_train):
""" Train only
Args:
x_train: ndarray contained the training data
Returns:
"""
# input size stay the same, enable cudnn optimization
cudnn.benchmark = True
self.data_transformer = DataTransformer(x_train, augment=self.augment)
train_dataloader = self.data_transformer.transform_train(x_train)
GANModelTrainer(self.net_g, self.net_d, train_dataloader,
binary_classification_loss, self.verbose,
self.gen_training_result).train_model()
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x_train: A numpy.ndarray instance containing the training data.
y_train: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
y_train = self.transform_y(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train, augment=self.augment)
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(x_train, y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
# Wrap the data into DataLoaders
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
# Save the classifier
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
self.cnn.fit(self.get_n_output_node(), x_train.shape, train_data, test_data, time_limit)
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x_train: A numpy.ndarray instance containing the training data.
y_train: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
y_train = self.transform_y(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train,
augment=self.augment)
# Create the searcher and save on disk
if not self.searcher:
input_shape = x_train.shape[1:]
self.searcher_args['n_output_node'] = self.get_n_output_node()
self.searcher_args['input_shape'] = input_shape
self.searcher_args['path'] = self.path
self.searcher_args['metric'] = self.metric
self.searcher_args['loss'] = self.loss
self.searcher_args['verbose'] = self.verbose
searcher = Searcher(**self.searcher_args)
self.save_searcher(searcher)
self.searcher = True
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(
x_train,
y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
# Wrap the data into DataLoaders
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
# Save the classifier
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
start_time = time.time()
time_remain = time_limit
try:
while time_remain > 0:
run_searcher_once(train_data, test_data, self.path,
int(time_remain))
if len(self.load_searcher().history) >= Constant.MAX_MODEL_NUM:
break
time_elapsed = time.time() - start_time
time_remain = time_limit - time_elapsed
# if no search executed during the time_limit, then raise an error
if time_remain <= 0:
raise TimeoutError
except TimeoutError:
if len(self.load_searcher().history) == 0:
raise TimeoutError(
"Search Time too short. No model was found during the search time."
)
elif self.verbose:
print('Time is out.')
class ImageSupervised(Supervised):
"""The image classifier class.
It is used for image classification. It searches convolutional neural network architectures
for the best configuration for the dataset.
Attributes:
path: A path to the directory to save the classifier.
y_encoder: An instance of OneHotEncoder for `y_train` (array of categorical labels).
verbose: A boolean value indicating the verbosity mode.
searcher: An instance of BayesianSearcher. It searches different
neural architecture to find the best model.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
augment: A boolean value indicating whether the data needs augmentation.
"""
def __init__(self,
verbose=False,
path=None,
resume=False,
searcher_args=None,
augment=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
augment: A boolean value indicating whether the data needs augmentation.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
@property
@abstractmethod
def metric(self):
pass
@property
@abstractmethod
def loss(self):
pass
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x_train: A numpy.ndarray instance containing the training data.
y_train: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
y_train = self.transform_y(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train,
augment=self.augment)
# Create the searcher and save on disk
if not self.searcher:
input_shape = x_train.shape[1:]
self.searcher_args['n_output_node'] = self.get_n_output_node()
self.searcher_args['input_shape'] = input_shape
self.searcher_args['path'] = self.path
self.searcher_args['metric'] = self.metric
self.searcher_args['loss'] = self.loss
self.searcher_args['verbose'] = self.verbose
searcher = Searcher(**self.searcher_args)
self.save_searcher(searcher)
self.searcher = True
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(
x_train,
y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
# Wrap the data into DataLoaders
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
# Save the classifier
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
start_time = time.time()
time_remain = time_limit
try:
while time_remain > 0:
run_searcher_once(train_data, test_data, self.path,
int(time_remain))
if len(self.load_searcher().history) >= Constant.MAX_MODEL_NUM:
break
time_elapsed = time.time() - start_time
time_remain = time_limit - time_elapsed
# if no search executed during the time_limit, then raise an error
if time_remain <= 0:
raise TimeoutError
except TimeoutError:
if len(self.load_searcher().history) == 0:
raise TimeoutError(
"Search Time too short. No model was found during the search time."
)
elif self.verbose:
print('Time is out.')
@abstractmethod
def get_n_output_node(self):
pass
def transform_y(self, y_train):
return y_train
def predict(self, x_test):
"""Return predict results for the testing data.
Args:
x_test: An instance of numpy.ndarray containing the testing data.
Returns:
A numpy.ndarray containing the results.
"""
if Constant.LIMIT_MEMORY:
pass
test_loader = self.data_transformer.transform_test(x_test)
model = self.load_searcher().load_best_model().produce_model()
model.eval()
outputs = []
with torch.no_grad():
for index, inputs in enumerate(test_loader):
outputs.append(model(inputs).numpy())
output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
return self.inverse_transform_y(output)
def inverse_transform_y(self, output):
return output
def evaluate(self, x_test, y_test):
"""Return the accuracy score between predict value and `y_test`."""
y_predict = self.predict(x_test)
return accuracy_score(y_test, y_predict)
def save_searcher(self, searcher):
pickle.dump(searcher, open(os.path.join(self.path, 'searcher'), 'wb'))
def load_searcher(self):
return pickle_from_file(os.path.join(self.path, 'searcher'))
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 get_best_model_id(self):
""" Return an integer indicating the id of the best model."""
return self.load_searcher().get_best_model_id()
def export_keras_model(self, model_file_name):
""" Exports the best Keras model to the given filename. """
self.load_searcher().load_best_model().produce_keras_model().save(
model_file_name)
def export_autokeras_model(self, model_file_name):
""" Creates and Exports the AutoKeras model to the given filename. """
portable_model = PortableImageSupervised(graph=self.load_searcher().load_best_model(), \
y_encoder=self.y_encoder, data_transformer=self.data_transformer)
pickle_to_file(portable_model, model_file_name)
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x_train: A numpy.ndarray instance containing the training data.
y_train: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
y_train = self.transform_y(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train, augment=self.augment)
# Create the searcher and save on disk
if not self.searcher:
input_shape = x_train.shape[1:]
self.searcher_args['n_output_node'] = self.get_n_output_node()
self.searcher_args['input_shape'] = input_shape
self.searcher_args['path'] = self.path
self.searcher_args['metric'] = self.metric
self.searcher_args['loss'] = self.loss
self.searcher_args['verbose'] = self.verbose
searcher = Searcher(**self.searcher_args)
self.save_searcher(searcher)
self.searcher = True
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(x_train, y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
# Wrap the data into DataLoaders
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
# Save the classifier
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
start_time = time.time()
time_remain = time_limit
try:
while time_remain > 0:
run_searcher_once(train_data, test_data, self.path, int(time_remain))
if len(self.load_searcher().history) >= Constant.MAX_MODEL_NUM:
break
time_elapsed = time.time() - start_time
time_remain = time_limit - time_elapsed
# if no search executed during the time_limit, then raise an error
if time_remain <= 0:
raise TimeoutError
except TimeoutError:
if len(self.load_searcher().history) == 0:
raise TimeoutError("Search Time too short. No model was found during the search time.")
elif self.verbose:
print('Time is out.')
class ImageSupervised(Supervised):
"""The image classifier class.
It is used for image classification. It searches convolutional neural network architectures
for the best configuration for the dataset.
Attributes:
path: A path to the directory to save the classifier.
y_encoder: An instance of OneHotEncoder for `y_train` (array of categorical labels).
verbose: A boolean value indicating the verbosity mode.
searcher: An instance of BayesianSearcher. It searches different
neural architecture to find the best model.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
def __init__(self, verbose=False, path=None, resume=False, searcher_args=None, augment=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: A boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: A boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
augment: A boolean value indicating whether the data needs augmentation. If not define, then it
will use the value of Constant.DATA_AUGMENTATION which is True by default.
"""
super().__init__(verbose)
if searcher_args is None:
searcher_args = {}
if path is None:
path = temp_folder_generator()
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
@property
@abstractmethod
def metric(self):
pass
@property
@abstractmethod
def loss(self):
pass
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x_train: A numpy.ndarray instance containing the training data.
y_train: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
y_train = self.transform_y(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train, augment=self.augment)
# Create the searcher and save on disk
if not self.searcher:
input_shape = x_train.shape[1:]
self.searcher_args['n_output_node'] = self.get_n_output_node()
self.searcher_args['input_shape'] = input_shape
self.searcher_args['path'] = self.path
self.searcher_args['metric'] = self.metric
self.searcher_args['loss'] = self.loss
self.searcher_args['verbose'] = self.verbose
searcher = Searcher(**self.searcher_args)
self.save_searcher(searcher)
self.searcher = True
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(x_train, y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
# Wrap the data into DataLoaders
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
# Save the classifier
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
start_time = time.time()
time_remain = time_limit
try:
while time_remain > 0:
run_searcher_once(train_data, test_data, self.path, int(time_remain))
if len(self.load_searcher().history) >= Constant.MAX_MODEL_NUM:
break
time_elapsed = time.time() - start_time
time_remain = time_limit - time_elapsed
# if no search executed during the time_limit, then raise an error
if time_remain <= 0:
raise TimeoutError
except TimeoutError:
if len(self.load_searcher().history) == 0:
raise TimeoutError("Search Time too short. No model was found during the search time.")
elif self.verbose:
print('Time is out.')
@abstractmethod
def get_n_output_node(self):
pass
def transform_y(self, y_train):
return y_train
def predict(self, x_test):
"""Return predict results for the testing data.
Args:
x_test: An instance of numpy.ndarray containing the testing data.
Returns:
A numpy.ndarray containing the results.
"""
if Constant.LIMIT_MEMORY:
pass
test_loader = self.data_transformer.transform_test(x_test)
model = self.load_searcher().load_best_model().produce_model()
model.eval()
outputs = []
with torch.no_grad():
for index, inputs in enumerate(test_loader):
outputs.append(model(inputs).numpy())
output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
return self.inverse_transform_y(output)
def inverse_transform_y(self, output):
return output
def evaluate(self, x_test, y_test):
"""Return the accuracy score between predict value and `y_test`."""
y_predict = self.predict(x_test)
return self.metric().compute(y_test, y_predict)
def save_searcher(self, searcher):
pickle.dump(searcher, open(os.path.join(self.path, 'searcher'), 'wb'))
def load_searcher(self):
return pickle_from_file(os.path.join(self.path, 'searcher'))
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 get_best_model_id(self):
""" Return an integer indicating the id of the best model."""
return self.load_searcher().get_best_model_id()
def export_keras_model(self, model_file_name):
""" Exports the best Keras model to the given filename. """
self.load_searcher().load_best_model().produce_keras_model().save(model_file_name)
def export_autokeras_model(self, model_file_name):
""" Creates and Exports the AutoKeras model to the given filename. """
portable_model = PortableImageSupervised(graph=self.load_searcher().load_best_model(),
y_encoder=self.y_encoder,
data_transformer=self.data_transformer,
metric=self.metric,
inverse_transform_y_method=self.inverse_transform_y)
pickle_to_file(portable_model, model_file_name)
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through `x_train`, `y_train`.
Args:
x_train: A numpy.ndarray instance containing the training data.
y_train: A numpy.ndarray instance containing the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
# Transform y_train.
if self.y_encoder is None:
self.y_encoder = OneHotEncoder()
self.y_encoder.fit(y_train)
y_train = self.y_encoder.transform(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train,
augment=self.augment)
# Create the searcher and save on disk
if not self.searcher:
input_shape = x_train.shape[1:]
n_classes = self.y_encoder.n_classes
self.searcher_args['n_classes'] = n_classes
self.searcher_args['input_shape'] = input_shape
self.searcher_args['path'] = self.path
self.searcher_args['metric'] = self.metric
self.searcher_args['verbose'] = self.verbose
searcher = BayesianSearcher(**self.searcher_args)
self.save_searcher(searcher)
self.searcher = True
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(
x_train,
y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
start_time = time.time()
while time.time() - start_time <= time_limit:
run_searcher_once(train_data, test_data, self.path)
if len(self.load_searcher().history) >= Constant.MAX_MODEL_NUM:
break
class ImageClassifier:
"""The image classifier class.
It is used for image classification. It searches convolutional neural network architectures
for the best configuration for the dataset.
Attributes:
path: A path to the directory to save the classifier.
y_encoder: An instance of OneHotEncoder for y_train (array of categorical labels).
verbose: A boolean value indicating the verbosity mode.
searcher: An instance of BayesianSearcher. It search different
neural architecture to find the best model.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
augment: A boolean value indicating whether the data needs augmentation.
"""
def __init__(self,
verbose=False,
path=Constant.DEFAULT_SAVE_PATH,
resume=False,
searcher_args=None,
augment=None):
"""Initialize the instance.
The classifier will be loaded from the files in 'path' if parameter 'resume' is True.
Otherwise it would create a new one.
Args:
verbose: An boolean of whether the search process will be printed to stdout.
path: A string. The path to a directory, where the intermediate results are saved.
resume: An boolean. If True, the classifier will continue to previous work saved in path.
Otherwise, the classifier will start a new search.
augment: A boolean value indicating whether the data needs augmentation.
"""
if searcher_args is None:
searcher_args = {}
if augment is None:
augment = Constant.DATA_AUGMENTATION
if has_file(os.path.join(path, 'classifier')) and resume:
classifier = pickle_from_file(os.path.join(path, 'classifier'))
self.__dict__ = classifier.__dict__
self.path = path
else:
self.y_encoder = None
self.data_transformer = None
self.verbose = verbose
self.searcher = False
self.path = path
self.searcher_args = searcher_args
self.augment = augment
ensure_dir(path)
def fit(self, x_train=None, y_train=None, time_limit=None):
"""Find the best neural architecture and train it.
Based on the given dataset, the function will find the best neural architecture for it.
The dataset is in numpy.ndarray format.
So they training data should be passed through x_train, y_train.
Args:
x_train: An numpy.ndarray instance contains the training data.
y_train: An numpy.ndarray instance contains the label of the training data.
time_limit: The time limit for the search in seconds.
"""
if y_train is None:
y_train = []
if x_train is None:
x_train = []
x_train = np.array(x_train)
y_train = np.array(y_train).flatten()
_validate(x_train, y_train)
# Transform y_train.
if self.y_encoder is None:
self.y_encoder = OneHotEncoder()
self.y_encoder.fit(y_train)
y_train = self.y_encoder.transform(y_train)
# Transform x_train
if self.data_transformer is None:
self.data_transformer = DataTransformer(x_train,
augment=self.augment)
# Create the searcher and save on disk
if not self.searcher:
input_shape = x_train.shape[1:]
n_classes = self.y_encoder.n_classes
self.searcher_args['n_classes'] = n_classes
self.searcher_args['input_shape'] = input_shape
self.searcher_args['path'] = self.path
self.searcher_args['verbose'] = self.verbose
searcher = BayesianSearcher(**self.searcher_args)
self.save_searcher(searcher)
self.searcher = True
# Divide training data into training and testing data.
x_train, x_test, y_train, y_test = train_test_split(
x_train,
y_train,
test_size=min(Constant.VALIDATION_SET_SIZE,
int(len(y_train) * 0.2)),
random_state=42)
train_data = self.data_transformer.transform_train(x_train, y_train)
test_data = self.data_transformer.transform_test(x_test, y_test)
pickle.dump(self, open(os.path.join(self.path, 'classifier'), 'wb'))
pickle_to_file(self, os.path.join(self.path, 'classifier'))
if time_limit is None:
time_limit = 24 * 60 * 60
start_time = time.time()
while time.time() - start_time <= time_limit:
run_searcher_once(train_data, test_data, self.path)
if len(self.load_searcher().history) >= Constant.MAX_MODEL_NUM:
break
def predict(self, x_test):
"""Return predict result for the testing data.
Args:
x_test: An instance of numpy.ndarray contains the testing data.
Returns:
An numpy.ndarray containing the results.
"""
if Constant.LIMIT_MEMORY:
pass
test_data = self.data_transformer.transform_test(x_test)
test_loader = DataLoader(test_data,
batch_size=Constant.MAX_BATCH_SIZE,
shuffle=False)
model = self.load_searcher().load_best_model().produce_model()
model.eval()
outputs = []
with torch.no_grad():
for index, inputs in enumerate(test_loader):
outputs.append(model(inputs).numpy())
output = reduce(lambda x, y: np.concatenate((x, y)), outputs)
return self.y_encoder.inverse_transform(output)
def evaluate(self, x_test, y_test):
"""Return the accuracy score between predict value and test_y."""
y_predict = self.predict(x_test)
return accuracy_score(y_test, y_predict)
def save_searcher(self, searcher):
pickle.dump(searcher, open(os.path.join(self.path, 'searcher'), 'wb'))
def load_searcher(self):
return pickle_from_file(os.path.join(self.path, 'searcher'))
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)
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.verbose))
def get_best_model_id(self):
"""
Returns:
An integer. The best model id.
"""
return self.load_searcher().get_best_model_id()
class DCGAN(Unsupervised):
""" Deep Convolution Generative Adversary Network
"""
def __init__(self,
nz=100,
ngf=32,
ndf=32,
nc=3,
verbose=False,
gen_training_result=None,
augment=Constant.DATA_AUGMENTATION):
"""
Args:
nz: size of the latent z vector
ngf: of gen filters in first conv layer
ndf: of discrim filters in first conv layer
nc: number of input chanel
verbose: A boolean of whether the search process will be printed to stdout.
gen_training_result: A tuple of (path, size) to denote where to output the intermediate result with size
augment: A boolean value indicating whether the data needs augmentation.
"""
super().__init__(verbose)
self.nz = nz
self.ngf = ngf
self.ndf = ndf
self.nc = nc
self.verbose = verbose
self.gen_training_result = gen_training_result
self.augment = augment
self.data_transformer = None
self.net_d = Discriminator(self.nc, self.ndf)
self.net_g = Generator(self.nc, self.nz, self.ngf)
def fit(self, x_train):
""" Train only
Args:
x_train: ndarray contained the training data
Returns:
"""
# input size stay the same, enable cudnn optimization
cudnn.benchmark = True
self.data_transformer = DataTransformer(x_train, augment=self.augment)
train_dataloader = self.data_transformer.transform_train(x_train)
GANModelTrainer(self.net_g, self.net_d, train_dataloader,
binary_classification_loss, self.verbose,
self.gen_training_result).train_model()
def generate(self, input_sample=None):
if input_sample is None:
input_sample = torch.randn(self.gen_training_result[1],
self.nz,
1,
1,
device=get_device())
if not isinstance(input_sample, torch.Tensor) and \
isinstance(input_sample, np.ndarray):
input_sample = torch.from_numpy(input_sample)
if not isinstance(input_sample, torch.Tensor) and \
not isinstance(input_sample, np.ndarray):
raise TypeError(
"Input should be a torch.tensor or a numpy.ndarray")
self.net_g.eval()
with torch.no_grad():
generated_fake = self.net_g(input_sample)
vutils.save_image(generated_fake.detach(),
'%s/evaluation.png' % self.gen_training_result[0],
normalize=True)
from autokeras.net_transformer import default_transform
from autokeras.preprocessor import OneHotEncoder, DataTransformer
from autokeras.utils import ModelTrainer
if __name__ == '__main__':
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('Start Encoding')
encoder = OneHotEncoder()
encoder.fit(y_train)
y_train = encoder.transform(y_train)
y_test = encoder.transform(y_test)
data_transformer = DataTransformer(x_train, augment=True)
train_data = data_transformer.transform_train(x_train, y_train)
test_data = data_transformer.transform_test(x_test, y_test)
print('Start Generating')
graphs = [DefaultClassifierGenerator(10, x_train.shape[1:]).generate()]
keras_model = graphs[0].produce_model()
print('Start Training')
loss, acc = ModelTrainer(keras_model,
train_data,
test_data,
True).train_model(max_no_improvement_num=100, batch_size=128)
print(loss, acc)
def get_classification_train_data_loaders():
x_train = np.random.rand(200, 32, 32, 3)
data_transformer = DataTransformer(x_train, augment=True)
train_data = data_transformer.transform_train(x_train)
return train_data
'max_iter_num': 5
}})
#clf = ImageClassifier(verbose=True, path='d:/tmp/autokeras/', searcher_args={'trainer_args':{'max_iter_num':5}})
# 3. Fitting
# time_limit : 초단위, 시간이 지나면 작동을 자동으로 멈춥니다.
clf.fit(x_train, y_train, time_limit=24 * 60 * 60)
# 3-1. Load saved model (3번 항목 실행후 3 주석처리 필요)
# if you reloaded your saved clf, y_encoder & data_transformer should be defined like following.
from autokeras.preprocessor import OneHotEncoder, DataTransformer
from autokeras.constant import Constant
clf.y_encoder = OneHotEncoder()
clf.y_encoder.fit(y_train)
clf.data_transformer = DataTransformer(x_train,
augment=Constant.DATA_AUGMENTATION)
#print(clf.get_best_model_id())
searcher = clf.load_searcher()
#print(searcher.history)
# 3-2. fitting finally and saving model
clf.final_fit(x_train,
y_train,
x_test,
y_test,
retrain=False,
trainer_args={'max_iter_num': 10})
y = clf.evaluate(x_test, y_test)
print(y)
