def __init__(self, p=None, **kwargs):
logit = None
if kwargs:
logit, = argument.parse_kwargs(kwargs, ('logit', logit))
if not (p is None) ^ (logit is None):
raise ValueError(
"Either `p` or `logit` (not both) must have a value.")
with chainer.using_config('enable_backprop', True):
if p is None:
logit = chainer.as_variable(logit)
self.__log_p = log_softmax.log_softmax(logit, axis=-1)
self.__p = exponential.exp(self.__log_p)
else:
self.__p = chainer.as_variable(p)
self.__log_p = exponential.log(self.__p)
def __init__(self, p=None, **kwargs):
logit = None
if kwargs:
logit, = argument.parse_kwargs(
kwargs, ('logit', logit))
if not (p is None) ^ (logit is None):
raise ValueError(
"Either `p` or `logit` (not both) must have a value.")
with chainer.using_config('enable_backprop', True):
if p is None:
logit = chainer.as_variable(logit)
self.__log_p = log_softmax.log_softmax(logit, axis=-1)
self.__p = exponential.exp(self.__log_p)
else:
self.__p = chainer.as_variable(p)
self.__log_p = exponential.log(self.__p)
def log_p(self):
if self.__p is not None:
return exponential.log(self.__p)
else:
return log_softmax.log_softmax(self.__logit, axis=-1)
def log_p(self):
if self.__p is not None:
return exponential.log(self.__p)
else:
return log_softmax.log_softmax(self.__logit, axis=-1)