from jactorch.utils.meta import as_numpy
from jactorch.utils.meta import as_tensor
from difflogic.tqdm_utils import tqdm_for
TASKS = ['final', 'stack', 'nlrl-Stack', 'nlrl-Unstack', 'nlrl-On', 'sort', 'path']
parser = JacArgumentParser()
parser.add_argument(
'--model',
default='dlm',
choices=['nlm', 'memnet', 'dlm'],
help='model choices, nlm: Neural Logic Machine, memnet: Memory Networks, dlm: Differentiable Logic Machine')
# NLM parameters, works when model is 'nlm'.
nlm_group = parser.add_argument_group('Neural Logic Machines')
DifferentiableLogicMachine.make_nlm_parser(
nlm_group, {
'depth': 7,
'breadth': 3,
'exclude_self': True,
'logic_hidden_dim': []
},
prefix='nlm')
nlm_group.add_argument(
'--nlm-attributes',
type=int,
default=8,
metavar='N',
help='number of output attributes in each group of each layer of the LogicMachine'
)
TASKS = [
'outdegree', 'connectivity', 'adjacent', 'adjacent-mnist', 'has-father',
'has-sister', 'grandparents', 'uncle', 'maternal-great-uncle'
]
parser = JacArgumentParser()
parser.add_argument(
'--model',
default='nlm',
choices=['nlm', 'memnet'],
help='model choices, nlm: Neural Logic Machine, memnet: Memory Networks')
# NLM parameters, works when model is 'nlm'
nlm_group = parser.add_argument_group('Neural Logic Machines')
LogicMachine.make_nlm_parser(nlm_group, {
'depth': 4,
'breadth': 3,
'exclude_self': True,
'logic_hidden_dim': []
},
prefix='nlm')
nlm_group.add_argument(
'--nlm-attributes',
type=int,
default=8,
metavar='N',
help=
'number of output attributes in each group of each layer of the LogicMachine'
)
metavar='M',
help='skip optim step if grad beyond this number')
parser.add_argument('--solution-count',
type=int,
default=5,
metavar='M',
help='number at which to cap target-set')
parser.add_argument('--model',
default='nlm',
choices=['nlm', 'rrn'],
help='model choices, nlm: Neural Logic Machine')
# NLM parameters, works when model is 'nlm'
nlm_group = parser.add_argument_group('Neural Logic Machines')
LogicMachine.make_nlm_parser(nlm_group, {
'depth': 4,
'breadth': 2,
'exclude_self': True,
'logic_hidden_dim': []
},
prefix='nlm')
nlm_group.add_argument(
'--nlm-attributes',
type=int,
default=8,
metavar='N',
help=
'number of output attributes in each group of each layer of the LogicMachine'