def logical_xor(x, y, name="LogicalXor"):
"""x ^ y = (x | y) & ~(x & y)."""
# TODO(alemi) Make this a cwise op if people end up relying on it.
return gen_math_ops.logical_and(
gen_math_ops.logical_or(x, y),
gen_math_ops.logical_not(gen_math_ops.logical_and(x, y)),
name=name)
def __body(w_, e_, mask, b):
e = math_ops.cast(distributions.Beta((self.__mf - 1.0) / 2.0,
(self.__mf - 1.0) / 2.0).
sample(shape, seed=seed), dtype=self.dtype)
u = random_ops.random_uniform(shape, dtype=self.dtype, seed=seed)
w = (1.0 - (1.0 + b) * e) / (1.0 - (1.0 - b) * e)
x = (1.0 - b) / (1.0 + b)
c = self.scale * x + (self.__mf - 1) * math_ops.log1p(-x**2)
tmp = tf.clip_by_value(x * w, 0, 1 - 1e-16)
reject = gen_math_ops.less(((self.__mf - 1.0) * math_ops.log(1.0 - tmp) +
self.scale * w - c),
math_ops.log(u))
accept = gen_math_ops.logical_not(reject)
w_ = array_ops.where(gen_math_ops.logical_and(mask, accept), w, w_)
e_ = array_ops.where(gen_math_ops.logical_and(mask, accept), e, e_)
mask = array_ops.where(gen_math_ops.logical_and(mask, accept),
reject, mask)
return w_, e_, mask, b
def __body(w_, e_, bool_mask, b, a, d):
e = math_ops.cast(Beta((self.__mf - 1) / 2,
(self.__mf - 1) / 2).sample(shape,
seed=seed),
dtype=self.dtype)
u = random_ops.random_uniform(shape, dtype=self.dtype, seed=seed)
w = (1 - (1 + b) * e) / (1 - (1 - b) * e)
t = (2 * a * b) / (1 - (1 - b) * e)
accept = gen_math_ops.greater(
((self.__mf - 1) * math_ops.log(t) - t + d), math_ops.log(u))
reject = gen_math_ops.logical_not(accept)
w_ = array_ops.where(gen_math_ops.logical_and(bool_mask, accept),
w, w_)
e_ = array_ops.where(gen_math_ops.logical_and(bool_mask, accept),
e, e_)
bool_mask = array_ops.where(
gen_math_ops.logical_and(bool_mask, accept), reject, bool_mask)
return w_, e_, bool_mask, b, a, d
def get_switch_op():
return state_ops.assign(
self._is_row_sweep_var,
gen_math_ops.logical_not(self._is_row_sweep_var)).op
def not_(a):
"""Functional form of "not"."""
if tensor_util.is_tensor(a):
return gen_math_ops.logical_not(a)
return not a
def _tf_not(a):
"""Implementation of the "not_" operator for TensorFlow."""
return gen_math_ops.logical_not(a)
def not_(a):
"""Functional form of "not"."""
if tensor_util.is_tensor(a):
return gen_math_ops.logical_not(a)
return not a
def get_switch_op():
return state_ops.assign(
self._is_row_sweep_var,
gen_math_ops.logical_not(self._is_row_sweep_var)).op
def _tf_not(a): """Implementation of the "not_" operator for TensorFlow.""" return gen_math_ops.logical_not(a)