def _make_loss_functions(self, mode=None):
"""Return pair (f_loss, f_d_loss) of functions.
- f_loss returns the current loss,
- f_d_loss returns the gradient of that loss wrt parameters,
"""
rng = T.shared_randomstreams.RandomStreams()
# Drop out inpts.
inpt = self.exprs['inpt']
inpt_dropped_out = corrupt.mask(inpt, self.p_dropout_inpt, rng)
givens = {inpt: inpt_dropped_out}
loss = theano.clone(self.exprs['loss'], givens)
n_layers = len(self.n_hiddens)
for i in range(n_layers - 1):
# Drop out hidden.
hidden = self.exprs['hidden_%i' % i]
hidden_dropped_out = corrupt.mask(hidden, self.p_dropout_hidden, rng)
givens = {hidden: hidden_dropped_out}
loss = theano.clone(loss, givens)
d_loss = T.grad(loss, self.parameters.flat)
f_loss = self.function(['inpt', 'target'], loss, explicit_pars=True,
mode=mode)
f_d_loss = self.function(['inpt', 'target'], d_loss, explicit_pars=True,
mode=mode)
return f_loss, f_d_loss
def _make_loss_functions(self, mode=None):
"""Return pair (f_loss, f_d_loss) of functions.
- f_loss returns the current loss,
- f_d_loss returns the gradient of that loss wrt parameters,
"""
rng = T.shared_randomstreams.RandomStreams()
# Drop out inpts.
inpt = self.exprs['inpt']
inpt_dropped_out = corrupt.mask(inpt, self.p_dropout_inpt, rng)
givens = {inpt: inpt_dropped_out}
loss = theano.clone(self.exprs['loss'], givens)
n_layers = len(self.n_hiddens)
for i in range(n_layers - 1):
# Drop out hidden.
hidden = self.exprs['hidden_%i' % i]
hidden_dropped_out = corrupt.mask(hidden, self.p_dropout_hidden, rng)
givens = {hidden: hidden_dropped_out}
loss = theano.clone(loss, givens)
d_loss = T.grad(loss, self.parameters.flat)
f_loss = self.function(['inpt', 'target'], loss, explicit_pars=True,
mode=mode)
f_d_loss = self.function(['inpt', 'target'], d_loss, explicit_pars=True,
mode=mode)
return f_loss, f_d_loss
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):
# 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))
