def add_model_specific_args(parent_parser):
parser = child_argparser(
LSTMStacked.add_model_specific_args(parent_parser))
temp_args, _ = parser.parse_known_args()
if temp_args.input_size is not None and temp_args.input_size <= 2048:
if temp_args.n_layers is not None:
default_batch_size = 256
if temp_args.n_layers == 2:
default_batch_size = 256 + 64 + 32
elif temp_args.n_layers == 1:
default_batch_size = 512 + 32
parser.add_argument('-b',
'--batch_size',
type=int,
default=default_batch_size)
return parser
def add_model_specific_args(parent_parser):
parser = child_argparser(
pn.PredNet.add_model_specific_args(parent_parser))
# See if n_layers has been specified to infer default batch size
temp_args, _ = parser.parse_known_args()
if temp_args.n_layers is not None:
if temp_args.n_layers == 2:
default_batch_size = 256 + 64 + 32
elif temp_args.n_layers == 1:
default_batch_size = 512 + 128 + 64
else:
default_batch_size = 256
parser.add_argument('-b',
'--batch_size',
type=int,
default=default_batch_size)
return parser
def add_model_specific_args(parent_parser):
parser = child_argparser(
pn.PredNet.add_model_specific_args(parent_parser))
parser.add_argument('--layer_loss_mode', type=str, default='')
parser.add_argument('--n_predictions', type=int, default=3)
parser.add_argument('--gamma', type=float, default=0.97)
# See if we have the right number of inputs and n_layers has been
# specified to infer default batch size
temp_args, _ = parser.parse_known_args()
if temp_args.input_size is not None and temp_args.input_size <= 2048:
if temp_args.n_layers is not None:
default_batch_size = 256
if temp_args.n_layers == 1:
default_batch_size = 256 + 128
parser.add_argument('-b',
'--batch_size',
type=int,
default=default_batch_size)
return parser
def add_model_specific_args(parent_parser):
parser = child_argparser(BaseTorchModel.add_model_specific_args(
parent_parser))
parser.add_argument('--n_layers', type=int, default=2)
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--output_mode', type=str, default='error')
parser.add_argument('--layer_loss_mode', type=str, default='first')
# See if we have the right number of inputs and n_layers has been
# specified to infer default batch size
temp_args, _ = parser.parse_known_args()
default_batch_size = 256
if temp_args.input_size is not None and temp_args.input_size <= 2048:
if temp_args.n_layers is not None:
if temp_args.n_layers == 2:
default_batch_size = 256 + 128
elif temp_args.n_layers == 1:
default_batch_size = 512 + 128 + 64
parser.add_argument('-b', '--batch_size', type=int,
default=default_batch_size)
return parser
def add_model_specific_args(parent_parser):
parser = child_argparser(parent_parser)
parser.add_argument('--n_layers', type=int, default=1)
parser.add_argument('--lr', type=float, default=0.001)
return parser
def add_model_specific_args(parent_parser):
parser = child_argparser(
LSTMStackedDense.add_model_specific_args(parent_parser))
parser.add_argument('--n_layers', type=int, default=1)
parser.add_argument('-b', '--batch_size', type=int, default=512 + 128)
return parser
def add_dataset_specific_args(parent_parser):
parser = child_argparser(
SchapiroFractalsDataset.add_dataset_specific_args(parent_parser))
parser.add_argument('--input_size', type=int, default=2048)
return parser
