def main():
# Loading the config file.
cfg = ut.get_config()
# Creating the searcher.
searcher = RandomSearcher(ss.search_space_fn)
# Creating the search folder for logging information.
sl.create_search_folderpath(cfg['folderpath'],
cfg['search_name'],
abort_if_exists=cfg["abort_if_exists"],
delete_if_exists=cfg['delete_if_exists'],
create_parent_folders=True)
# Search loop.
for evaluation_id in range(cfg['num_samples']):
logger = sl.EvaluationLogger(cfg["folderpath"], cfg["search_name"],
evaluation_id)
if not logger.config_exists():
(inputs, outputs, hyperp_value_lst,
searcher_eval_token) = searcher.sample()
# Logging results (including graph).
logger.log_config(hyperp_value_lst, searcher_eval_token)
vi.draw_graph(
outputs,
draw_module_hyperparameter_info=False,
print_to_screen=False,
out_folderpath=logger.get_evaluation_data_folderpath())
def main():
# Loading the config file.
cfg = ut.get_config()
num_classes = 10
num_samples = cfg['num_samples']
# Loading the data.
(Xtrain, ytrain, Xval, yval, Xtest, ytest) = load_mnist('data/mnist')
train_dataset = InMemoryDataset(Xtrain, ytrain, True)
val_dataset = InMemoryDataset(Xval, yval, False)
test_dataset = InMemoryDataset(Xtest, ytest, False)
# Creating up the evaluator.
evaluator = SimpleClassifierEvaluator(
train_dataset,
val_dataset,
num_classes,
'./temp',
max_eval_time_in_minutes=cfg['max_eval_time_in_minutes'],
log_output_to_terminal=True,
test_dataset=test_dataset)
# Creating the search space.
search_space_fn = lambda: css_dnn.dnn_net(num_classes)
search_space_factory = mo.SearchSpaceFactory(search_space_fn)
sl.create_search_folderpath(
cfg["folderpath"],
cfg["search_name"],
delete_if_exists=cfg['delete_if_exists'],
abort_if_exists=False,
create_parent_folders=True)
# Creating the searcher.
searcher = RandomSearcher(search_space_factory.get_search_space)
# Search loop.
for evaluation_id in range(num_samples):
eval_logger = sl.EvaluationLogger(cfg["folderpath"], cfg["search_name"],
evaluation_id)
if not eval_logger.config_exists():
inputs, outputs, hyperp_value_lst, eval_token = searcher.sample()
results = evaluator.eval(inputs, outputs)
# Logging results (including graph).
eval_logger.log_config(hyperp_value_lst, eval_token)
eval_logger.log_results(results)
vi.draw_graph(
outputs,
draw_module_hyperparameter_info=True,
print_to_screen=False,
out_folderpath=eval_logger.get_evaluation_data_folderpath())
# Updating the searcher given the results of logging.
searcher.update(results['validation_accuracy'], eval_token)
from __future__ import print_function
from builtins import range
# Run configs to make it easier to run the code.
import deep_architect.search_logging as sl
import deep_architect.utils as ut
# Make sure that only one GPU is visible.
if __name__ == '__main__':
cfg = ut.get_config()
if cfg['use_gpu']:
import deep_architect.contrib.misc.gpu_utils as gpu_utils
gpu_id = gpu_utils.get_available_gpu(0.1, 5.0)
print("Using GPU %d" % gpu_id)
assert gpu_id is not None
gpu_utils.set_visible_gpus([gpu_id])
from deep_architect.contrib.misc.datasets.loaders import load_mnist
from deep_architect.contrib.misc.evaluators.tensorflow.classification import SimpleClassifierEvaluator
from deep_architect.contrib.misc.datasets.dataset import InMemoryDataset
import deep_architect.visualization as vi
import searchers as local_se
import search_spaces as local_ss
def run_searcher(searcher, evaluator, num_samples, get_evaluation_logger):
for idx in range(num_samples):
evaluation_logger = get_evaluation_logger(idx)
(inputs, outputs, hyperp_value_lst,
searcher_eval_token) = searcher.sample()
results = evaluator.eval(inputs, outputs)