from .losses import Loss
from ..utils import get_dict_values
[docs]class CrossEntropy(Loss):
r"""
Cross entropy, a.k.a., the negative expected value of log-likelihood (Monte Carlo approximation).
.. math::
-\mathbb{E}_{q(x)}[\log p(x)] \approx -\frac{1}{L}\sum_{l=1}^L \log p(x_l),
where :math:`x_l \sim q(x)`.
"""
def __init__(self, p1, p2, input_var=None):
if input_var is None:
input_var = list(set(p1.input_var + p2.var))
super().__init__(p1, p2, input_var=input_var)
@property
def loss_text(self):
return "-E_{}[log {}]".format(self._p1.prob_text, self._p2.prob_text)
[docs] def estimate(self, x={}):
_x = super().estimate(x)
_p1_input = get_dict_values(_x, self._p1.input_var, return_dict=True)
samples = self._p1.sample(_p1_input, reparam=True, return_all=False)
_p2_input = get_dict_values(_x, self._p2.var, return_dict=True)
samples.update(_p2_input)
loss = -self._p2.log_likelihood(samples)
return loss
[docs]class Entropy(Loss):
r"""
Entropy (Monte Carlo approximation).
.. math::
-\mathbb{E}_{p(x)}[\log p(x)] \approx -\frac{1}{L}\sum_{l=1}^L \log p(x_l),
where :math:`x_l \sim p(x)`.
Note:
This class is a special case of the `CrossEntropy` class. You can get the same result with `CrossEntropy`.
"""
def __init__(self, p1, input_var=None):
if input_var is None:
input_var = p1.input_var
super().__init__(p1, None, input_var=input_var)
@property
def loss_text(self):
return "-E_{}[log {}]".format(self._p1.prob_text, self._p1.prob_text)
[docs] def estimate(self, x={}):
_x = super().estimate(x)
samples = self._p1.sample(_x, reparam=True)
loss = self._p1.log_likelihood(samples)
return loss
[docs]class StochasticReconstructionLoss(Loss):
r"""
Reconstruction Loss (Monte Carlo approximation).
.. math::
-\mathbb{E}_{q(z|x)}[\log p(x|z)] \approx -\frac{1}{L}\sum_{l=1}^L \log p(x|z_l),
where :math:`z_l \sim q(z|x)`.
Note:
This class is a special case of the `CrossEntropy` class. You can get the same result with `CrossEntropy`.
"""
def __init__(self, encoder, decoder, input_var=None):
if input_var is None:
input_var = encoder.input_var
super().__init__(encoder, decoder, input_var=input_var)
@property
def loss_text(self):
return "-E_{}[log {}]".format(self._p1.prob_text, self._p2.prob_text)
[docs] def estimate(self, x={}):
_x = super().estimate(x)
samples = self._p1.sample(_x, reparam=True)
loss = -self._p2.log_likelihood(samples)
return loss