def __init__(self, in_sequence=False, axis=1, name=None, **kwargs):
super().__init__(name=name)
self.in_sequence = in_sequence
if self.in_sequence:
self.filter_index = -1
self.eps = epsilon()
self.axis = axis
def unit_norm(model, axis=0):
"""
Constrains the weights incident to each hidden unit to have unit norm.
Args:
axis (int):axis along which to calculate weight norms.
model : the model contains weights need to setting the constraints.
"""
if isinstance(model, Layer):
for name, param in model.named_parameters():
if 'bias' not in name and param is not None and param.requires_grad == True:
norm = param.data.norm(2, dim=axis, keepdim=True)
param.data.copy_(param.data / (epsilon() + norm))
elif is_tensor(model):
if model is not None and model.requires_grad == True:
norm = model.data.norm(2, dim=axis, keepdim=True)
model.data.copy_(model.data / (epsilon() + norm))
def apply_constraint(t: Tensor):
w_data = None
if isinstance(t, tf.Variable):
w_data = t.value().detach()
else:
w_data = t.copy().detach()
param_applied = w_data / (epsilon() + sqrt(
reduce_sum(square(w_data), axis=axis, keepdims=True)))
param_applied = param_applied.detach()
return param_applied
def apply_constraint(t: Tensor):
w_data = None
if isinstance(t, tf.Variable):
w_data = t.value().detach()
else:
w_data = t.copy().detach()
norms = sqrt(reduce_sum(square(w_data), axis=axis, keepdims=True))
desired = clip(norms, 0, max_value)
param_applied = w_data * (desired / (epsilon() + norms))
param_applied = param_applied.detach()
return param_applied
def unit_norm(model, axis=0):
"""
Constrains the weights incident to each hidden unit to have unit norm.
Args:
axis (int):axis along which to calculate weight norms.
model : the model contains weights need to setting the constraints.
"""
ws = model.get_weights()
for i in range(len(ws)):
w = ws[i]
w = w / (epsilon() +
sqrt(reduce_sum(square(w), axis=axis, keepdims=True)))
def max_norm(model, max_value=3, axis=0):
"""
MaxNorm weight constraint.
Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.
Args:
model : the model contains weights need to setting the constraints.
max_value (float):the maximum norm value for the incoming weights
axis (int):axis along which to calculate weight norms.
"""
for name, param in model.named_parameters():
if 'bias' not in name and param is not None and param.requires_grad == True:
norm = param.data.norm(2, dim=axis, keepdim=True)
desired = torch.clamp(norm, 0, max_value)
param.data.copy_(param.data * (desired / (epsilon() + norm)))
def max_norm(model, max_value=3, axis=0):
"""
MaxNorm weight constraint.
Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.
Args:
model : the model contains weights need to setting the constraints.
max_value (float):the maximum norm value for the incoming weights
axis (int):axis along which to calculate weight norms.
"""
ws = model.weights()
for i in range(len(ws)):
w = ws[i]
norms = sqrt(reduce_sum(square(w), axis=axis, keepdims=True))
desired = clip(norms, 0, max_value)
w = w * (desired / (epsilon() + norms))
def min_max_norm(model, min_value=0.0, max_value=1.0, rate=3.0, axis=0):
"""
MinMaxNorm weight constraint.
Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.
Args:
model : the model contains weights need to setting the constraints.
min_value (float):the minimum norm for the incoming weights.
max_value ()float:the maximum norm for the incoming weights.
rate (float):rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(min_value, max_value). Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval.
axis (int): axis along which to calculate weight norms
"""
ws = model.get_weights()
for i in range(len(ws)):
w = ws[i]
norms = sqrt(reduce_sum(square(w), axis=axis, keepdims=True))
desired = (rate * clip(norms, min_value, max_value) +
(1 - rate) * norms)
w = w * (desired / (epsilon() + norms))
def min_max_norm(model, min_value=0, max_value=1, rate=2.0, axis=0):
"""
MinMaxNorm weight constraint.
Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.
Args:
model : the model contains weights need to setting the constraints.
min_value (float):the minimum norm for the incoming weights.
max_value ()float:the maximum norm for the incoming weights.
rate (float):rate for enforcing the constraint: weights will be rescaled to yield (1 - rate) * norm + rate * norm.clip(min_value, max_value). Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval.
axis (int): axis along which to calculate weight norms
"""
for name, param in model.named_parameters():
if 'bias' not in name and param is not None and param.requires_grad == True:
norm = param.data.norm(2, dim=axis, keepdim=True)
desired = rate * clip(norm, min_value,
max_value) + (1 - rate) * norm
param.data.copy_(param.data * (desired / (epsilon() + norm)))
def __init__(self, in_sequence=False, axis=1, name=None, **kwargs):
super().__init__(in_sequence=in_sequence, name=name)
self.eps = epsilon()
self.axis = axis