def run(args):
if args.config is not None:
with open(args.config, 'r') as stream:
hyper_params = load(stream, Loader=yaml.FullLoader)
else:
hyper_params = {}
if not os.path.exists(args.output):
os.makedirs(args.output)
# to be done as soon as possible otherwise mlflow will not log with the proper exp. name
if 'exp_name' in hyper_params:
mlflow.set_experiment(hyper_params['exp_name'])
# __TODO__ change the hparam that are used from the training algorithm
# (and NOT the model - these will be specified in the model itself)
check_and_log_hp(
['batch_size', 'optimizer', 'patience', 'architecture', 'max_epoch',
'exp_name'],
hyper_params)
train_loader, dev_loader = load_data(args, hyper_params)
model = load_model(hyper_params)
optimizer = load_optimizer(hyper_params, model)
loss_fun = load_loss(hyper_params)
train(model, optimizer, loss_fun, train_loader, dev_loader, hyper_params['patience'],
args.output, max_epoch=hyper_params['max_epoch'],
use_progress_bar=not args.disable_progressbar, start_from_scratch=args.start_from_scratch)
def __init__(self, hyper_params):
super(FakeModel, self).__init__()
check_and_log_hp(['size', 'dropout'], hyper_params)
self.hyper_params = hyper_params
self.linear = nn.Linear(5, 1)
def run(args, hyper_params):
"""Setup and run the dataloaders, training loops, etc.
Args:
args (list): arguments passed from the cli
hyper_params (dict): hyper parameters from the config file
"""
log_exp_details(os.path.realpath(__file__), args)
if not os.path.exists(args.output):
os.makedirs(args.output)
# __TODO__ change the hparam that are used from the training algorithm
# (and NOT the model - these will be specified in the model itself)
logger.info('List of hyper-parameters:')
check_and_log_hp(
['batch_size', 'optimizer', 'patience', 'architecture', 'max_epoch',
'exp_name'],
hyper_params)
train_loader, dev_loader = load_data(args, hyper_params)
model = load_model(hyper_params)
optimizer = load_optimizer(hyper_params, model)
loss_fun = load_loss(hyper_params)
train(model, optimizer, loss_fun, train_loader, dev_loader, hyper_params['patience'],
args.output, max_epoch=hyper_params['max_epoch'],
use_progress_bar=not args.disable_progressbar, start_from_scratch=args.start_from_scratch)
def __init__(self, hyper_params):
super(FakeModel, self).__init__()
check_and_log_hp(['size', 'dropout'], hyper_params)
self.hyper_params = hyper_params
{%- if cookiecutter.dl_framework in ['tensorflow_cpu', 'tensorflow_gpu'] %}
self.flat = tf.keras.layers.Flatten(input_shape=(5,))
self.dense1 = tf.keras.layers.Dense(10, activation='relu')
self.dense2 = tf.keras.layers.Dense(1)
def __init__(self, hyper_params):
super(MyModel, self).__init__()
check_and_log_hp(['size'], hyper_params)
self.hyper_params = hyper_params
{%- if cookiecutter.dl_framework in ['tensorflow_cpu', 'tensorflow_gpu'] %}
self.hyper_params = hyper_params
self.dense1 = tf.keras.layers.Dense(hyper_params['size'], activation=None)
self.dense2 = tf.keras.layers.Dense(1)
def __init__(self, hyper_params):
"""__init__.
Args:
hyper_params (dict): hyper parameters from the config file.
"""
super(MyModel, self).__init__()
check_and_log_hp(['size'], hyper_params)
self.hyper_params = hyper_params
{%- if cookiecutter.dl_framework in ['tensorflow_cpu', 'tensorflow_gpu'] %}
self.hyper_params = hyper_params
self.dense1 = tf.keras.layers.Dense(hyper_params['size'])
self.dense2 = tf.keras.layers.Dense(1)
def run(args, hyper_params):
if not os.path.exists(args.output):
os.makedirs(args.output)
# __TODO__ change the hparam that are used from the training algorithm
# (and NOT the model - these will be specified in the model itself)
check_and_log_hp(
['batch_size', 'optimizer', 'patience', 'architecture', 'max_epoch',
'exp_name'],
hyper_params)
train_loader, dev_loader = load_data(args, hyper_params)
model = load_model(hyper_params)
optimizer = load_optimizer(hyper_params, model)
loss_fun = load_loss(hyper_params)
train(model, optimizer, loss_fun, train_loader, dev_loader, hyper_params['patience'],
args.output, max_epoch=hyper_params['max_epoch'],
use_progress_bar=not args.disable_progressbar, start_from_scratch=args.start_from_scratch)