def loss(self, N, round_cl=1):
"""Loss function for training
Parameters
----------
N : int
Number of training samples
"""
loss = self.network.get_loss()
# adding nodes to dict s.t. they can be monitored during training
self.observables['loss.lprobs'] = self.network.lprobs
self.observables['loss.iws'] = self.network.iws
self.observables['loss.raw_loss'] = loss
if self.svi:
if self.round <= round_cl:
# weights close to zero-centered prior in the first round
if self.reg_lambda > 0:
kl, imvs = svi_kl_zero(self.network.mps, self.network.sps,
self.reg_lambda)
else:
kl, imvs = 0, {}
else:
# weights close to those of previous round
kl, imvs = svi_kl_init(self.network.mps, self.network.sps)
loss = loss + 1 / N * kl
# adding nodes to dict s.t. they can be monitored
self.observables['loss.kl'] = kl
self.observables.update(imvs)
return loss
def loss(self, N):
"""Loss function for training
Parameters
----------
N : int
Number of training samples
"""
loss = -tt.mean(self.network.iws * self.network.lprobs)
# adding nodes to dict s.t. they can be monitored during training
self.observables['loss.iws'] = self.network.iws
if self.svi:
if self.round == 1 or self.retain_data:
# weights close to zero-centered prior in the first round
kl, imvs = svi_kl_zero_diag_gauss(self.network.mps_wp,
self.network.sps_wp,
self.network.mps_bp,
self.network.sps_bp)
else:
# weights close to those of previous round
kl, imvs = svi_kl_init(self.network.mps, self.network.sps)
loss = loss + 1 / N * kl
# adding nodes to dict s.t. they can be monitored
self.observables['loss.kl'] = kl
self.observables.update(imvs)
return loss
def loss(self, N, round_cl=1):
"""Loss function for training
Parameters
----------
N : int
Number of training samples
"""
loss = -tt.mean(self.network.lprobs)
if self.svi:
if self.round <= round_cl:
# weights close to zero-centered prior in the first round
if self.reg_lambda > 0:
kl, imvs = svi_kl_zero(self.network.mps, self.network.sps,
self.reg_lambda)
else:
kl, imvs = 0, {}
else:
# weights close to those of previous round
kl, imvs = svi_kl_init(self.network.mps, self.network.sps)
loss = loss + 1 / N * kl
return loss
def define_loss(self,
n,
round_cl=1,
proposal='gaussian',
combined_loss=False):
"""Loss function for training
Parameters
----------
n : int
Number of training samples
round_cl : int
Round after which to start continual learning
proposal : str
Specifier for type of proposal used: continuous ('gaussian', 'mog')
or 'atomic' proposals are implemented.
combined_loss : bool
Whether to include prior likelihood terms in addition to atomic
"""
if proposal == 'prior': # using prior as proposal
loss, trn_inputs = snpe_loss_prior_as_proposal(self.network,
svi=self.svi)
elif proposal == 'gaussian':
assert isinstance(self.generator.proposal, dd.Gaussian)
loss, trn_inputs = apt_loss_gaussian_proposal(self.network,
self.generator.prior,
svi=self.svi)
elif proposal.lower() == 'mog':
loss, trn_inputs = apt_loss_MoG_proposal(self.network,
self.generator.prior,
svi=self.svi)
elif proposal == 'atomic':
loss, trn_inputs = \
apt_loss_atomic_proposal(self.network, svi=self.svi,
combined_loss=combined_loss)
else:
raise NotImplemented()
# adding nodes to dict s.t. they can be monitored during training
self.observables['loss.lprobs'] = self.network.lprobs
self.observables['loss.raw_loss'] = loss
if self.svi:
if self.round <= round_cl:
# weights close to zero-centered prior in the first round
if self.reg_lambda > 0:
kl, imvs = svi_kl_zero(self.network.mps, self.network.sps,
self.reg_lambda)
else:
kl, imvs = 0, {}
else:
# weights close to those of previous round
kl, imvs = svi_kl_init(self.network.mps, self.network.sps)
loss = loss + 1 / n * kl
# adding nodes to dict s.t. they can be monitored
self.observables['loss.kl'] = kl
self.observables.update(imvs)
return loss, trn_inputs