def __init__(self, valid=None, invalid=None, valid_equivalent=None):
'''
Check if variables can be expressed without using variables in invalid.
init_valid_equivalent provides a dictionary mapping some invalid
variables to valid ones that can be used instead.
'''
if valid is None:
valid = []
if invalid is None:
invalid = []
if valid_equivalent is None:
valid_equivalent = OrderedDict()
# Nodes that are valid to have in the graph computing outputs
self.valid = set(valid)
# Nodes that are NOT valid to have in the graph computing outputs
self.invalid = set(invalid)
# Mapping from invalid variables to equivalent valid ones.
self.valid_equivalent = valid_equivalent.copy()
self.valid.update(valid_equivalent.values())
self.invalid.update(valid_equivalent.keys())
def __init__(self, valid=None, invalid=None, valid_equivalent=None):
'''
Check if variables can be expressed without using variables in invalid.
init_valid_equivalent provides a dictionary mapping some invalid
variables to valid ones that can be used instead.
'''
if valid is None:
valid = []
if invalid is None:
invalid = []
if valid_equivalent is None:
valid_equivalent = OrderedDict()
# Nodes that are valid to have in the graph computing outputs
self.valid = set(valid)
# Nodes that are NOT valid to have in the graph computing outputs
self.invalid = set(invalid)
# Mapping from invalid variables to equivalent valid ones.
self.valid_equivalent = valid_equivalent.copy()
self.valid.update(valid_equivalent.values())
self.invalid.update(valid_equivalent.keys())
def __init__(self, valid=None, invalid=None, valid_equivalent=None):
if valid is None:
valid = []
if invalid is None:
invalid = []
if valid_equivalent is None:
valid_equivalent = OrderedDict()
# Nodes that are valid to have in the graph computing outputs
self.valid = set(valid)
# Nodes that are NOT valid to have in the graph computing outputs
self.invalid = set(invalid)
# Mapping from invalid variables to equivalent valid ones.
self.valid_equivalent = valid_equivalent.copy()
self.valid.update(list(valid_equivalent.values()))
self.invalid.update(list(valid_equivalent.keys()))
def __init__(self, valid=None, invalid=None, valid_equivalent=None):
if valid is None:
valid = []
if invalid is None:
invalid = []
if valid_equivalent is None:
valid_equivalent = OrderedDict()
# Nodes that are valid to have in the graph computing outputs
self.valid = set(valid)
# Nodes that are NOT valid to have in the graph computing outputs
self.invalid = set(invalid)
# Mapping from invalid variables to equivalent valid ones.
self.valid_equivalent = valid_equivalent.copy()
self.valid.update(list(valid_equivalent.values()))
self.invalid.update(list(valid_equivalent.keys()))
def get_lr_scalers(self):
"""
.. todo::
WRITEME
"""
rval = OrderedDict()
params = self.get_params()
for layer in self.hidden_layers + [ self.visible_layer ]:
contrib = layer.get_lr_scalers()
# No two layers can contend to scale a parameter
assert not any([key in rval for key in contrib])
# Don't try to scale anything that's not a parameter
assert all([key in params for key in contrib])
rval.update(contrib)
assert all([isinstance(val, float) for val in rval.values()])
return rval