def _init_exprs(self):
# Here we need to replace the input with a corrupted version. If we do
# so naively by calling clone on the loss, the targets (which are
# identical to the inputs in thesense of identity in programming) the
# targets will be replaced as well. Instead, we just want to thave the
# inputs replaced. Thus we first clone the output of the model and
# replace the input with the corrupted input. This will not change the
# targets. Afterwards, we put that corruption into the loss as well.
super(DenoisingAutoEncoder, self)._init_exprs()
if self.noise_type == 'gauss':
corrupted_inpt = corrupt.gaussian_perturb(
self.exprs['inpt'], self.c_noise)
elif self.noise_type == 'mask':
corrupted_inpt = corrupt.mask(
self.exprs['inpt'], self.c_noise)
output_from_corrupt = theano.clone(
self.exprs['output'],
{self.exprs['inpt']: corrupted_inpt}
)
score = self.exprs['loss']
loss = theano.clone(
self.exprs['loss'],
{self.exprs['output']: output_from_corrupt})
self.exprs.update(get_named_variables(locals(), overwrite=True))
def _init_exprs(self):
super(ContractiveAutoEncoder, self)._init_exprs()
jacobian_loss = T.sum(T.grad(self.exprs['feature'].mean(axis=0).sum(),
self.exprs['inpt']).mean(axis=0) ** 2)
loss = self.exprs['loss'] + self.c_jacobian * jacobian_loss
self.exprs.update(get_named_variables(locals(), overwrite=True))
def _init_exprs(self):
super(ContractiveAutoEncoder, self)._init_exprs()
jacobian_loss = T.sum(
T.grad(self.exprs['feature'].mean(axis=0).sum(),
self.exprs['inpt']).mean(axis=0)**2)
loss = self.exprs['loss'] + self.c_jacobian * jacobian_loss
self.exprs.update(get_named_variables(locals(), overwrite=True))
def _init_exprs(self):
super(SparseAutoEncoder, self)._init_exprs()
f_sparsity_loss = lookup(self.sparsity_loss, loss_)
sparsity_loss = f_sparsity_loss(
self.sparsity_target, self.exprs['feature'].mean(axis=0)).sum()
loss = self.exprs['loss'] + self.c_sparsity * sparsity_loss
self.exprs.update(get_named_variables(locals(), overwrite=True))
def _init_exprs(self):
super(SparseAutoEncoder, self)._init_exprs()
f_sparsity_loss = lookup(self.sparsity_loss, loss_)
sparsity_loss = f_sparsity_loss(
self.sparsity_target, self.exprs['feature'].mean(axis=0)).sum()
loss = self.exprs['loss'] + self.c_sparsity * sparsity_loss
self.exprs.update(get_named_variables(locals(), overwrite=True))
def _init_exprs(self):
# Here we need to replace the input with a corrupted version. If we do
# so naively by calling clone on the loss, the targets (which are
# identical to the inputs in thesense of identity in programming) the
# targets will be replaced as well. Instead, we just want to thave the
# inputs replaced. Thus we first clone the output of the model and
# replace the input with the corrupted input. This will not change the
# targets. Afterwards, we put that corruption into the loss as well.
super(DenoisingAutoEncoder, self)._init_exprs()
if self.noise_type == 'gauss':
corrupted_inpt = corrupt.gaussian_perturb(self.exprs['inpt'],
self.c_noise)
elif self.noise_type == 'mask':
corrupted_inpt = corrupt.mask(self.exprs['inpt'], self.c_noise)
output_from_corrupt = theano.clone(
self.exprs['output'], {self.exprs['inpt']: corrupted_inpt})
score = self.exprs['loss']
loss = theano.clone(self.exprs['loss'],
{self.exprs['output']: output_from_corrupt})
self.exprs.update(get_named_variables(locals(), overwrite=True))
