else:
with open(args.hyperparams_file, 'r') as filehandle:
parameters = json.load(fp=filehandle)
# restore
iteration_start = 1
if args.report_file:
with open(args.report_file, 'r') as filehandle:
for line in filehandle:
value = line.split(',')[0]
if value != 'iteration':
iteration_start = int(value) + 1
with Model(name=args.model,
learning_rate=parameters.pop('learning_rate', 1e-3),
weight_decay=parameters.pop('weight_decay', None),
clip_gradients=parameters.pop('clip_gradients', None),
model_directory=args.model_dir) as model:
parameters.pop('dropout_rate', None)
module = import_module('models.{}.{}'.format(args.type, args.model))
module.model(model=model,
inputs=dict(),
dataset_parameters=dataset_parameters,
**parameters
) # no input tensors, hence None for placeholder creation
model.finalize(restore=(args.model_dir is not None))
if args.verbosity >= 1:
sys.stdout.write(' parameters: {:,}\n'.format(
model.num_parameters))
report_file_dir = os.path.dirname(args.report_file)
if report_file_dir and not os.path.isdir(report_file_dir):
os.makedirs(report_file_dir)
with open(args.report_file, 'w') as filehandle:
filehandle.write('iteration,saved')
for name in query:
filehandle.write(',train ' + name)
if not args.tf_records:
for name in query:
filehandle.write(',validation ' + name)
filehandle.write('\n')
iteration_end = iteration_start + args.iterations - 1
with Model(name=args.model,
learning_rate=parameters.pop('learning_rate'),
weight_decay=parameters.pop('weight_decay', 0.0),
model_directory=args.model_dir,
summary_directory=args.summary_dir) as model:
dropout = parameters.pop('dropout_rate', 0.0)
module = import_module('models.{}.{}'.format(args.type, args.model))
if args.tf_records:
module.model(model=model,
inputs=tf_util.batch_records(
dataset=dataset,
batch_size=args.batch_size,
noise_range=args.pixel_noise),
**parameters)
else:
module.model(
model=model, inputs=dict(), **parameters