def fit(self,
n_output_node,
input_shape,
train_data,
test_data,
time_limit=24 * 60 * 60):
""" Search the best CnnModule.
Args:
n_output_node: A integer value represent the number of output node in the final layer.
input_shape: A tuple to express the shape of every train entry. For example,
MNIST dataset would be (28,28,1)
train_data: A PyTorch DataLoader instance represents the training data
test_data: A PyTorch DataLoader instance represents the testing data
time_limit: A integer value represents the time limit on searching for models.
"""
# Create the searcher and save on disk
if not self.searcher:
input_shape = input_shape[1:]
self.searcher_args['n_output_node'] = 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
start_time = time.time()
time_remain = time_limit
try:
while time_remain > 0:
searcher = pickle_from_file(os.path.join(
self.path, 'searcher'))
searcher.search(train_data, test_data, 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 NetworkModule:
""" Class to create a network module.
Attributes:
loss: A function taking two parameters, the predictions and the ground truth.
metric: An instance of the Metric subclasses.
searcher_args: A dictionary containing the parameters for the searcher's __init__ function.
searcher: An instance of the Searcher class.
path: A string. The path to the directory to save the searcher.
verbose: A boolean. Setting it to true prints to stdout.
generators: A list of instances of the NetworkGenerator class or its subclasses.
"""
def __init__(self, loss, metric, searcher_args, path, verbose=False):
self.searcher_args = searcher_args
self.searcher = None
self.path = path
self.verbose = verbose
self.loss = loss
self.metric = metric
self.generators = []
def fit(self,
n_output_node,
input_shape,
train_data,
test_data,
time_limit=24 * 60 * 60):
""" Search the best network.
Args:
n_output_node: A integer value represent the number of output node in the final layer.
input_shape: A tuple to express the shape of every train entry. For example,
MNIST dataset would be (28,28,1).
train_data: A PyTorch DataLoader instance representing the training data.
test_data: A PyTorch DataLoader instance representing the testing data.
time_limit: A integer value represents the time limit on searching for models.
"""
# Create the searcher and save on disk
if not self.searcher:
input_shape = input_shape[1:]
self.searcher_args['n_output_node'] = 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['generators'] = self.generators
self.searcher_args['verbose'] = self.verbose
self.searcher = Searcher(**self.searcher_args)
pickle_to_file(self, os.path.join(self.path, 'module'))
start_time = time.time()
time_remain = time_limit
try:
while time_remain > 0:
self.searcher.search(train_data, test_data, int(time_remain))
pickle_to_file(self, os.path.join(self.path, 'module'))
if len(self.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.searcher.history) == 0:
raise TimeoutError(
"Search Time too short. No model was found during the search time."
)
elif self.verbose:
print('Time is out.')
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'))
@property
def best_model(self):
return self.searcher.load_best_model()