def logaddexp2(x1, x2):
amax = maximum(x1, x2)
delta = x1 - x2
return np_array_ops.where(
isnan(delta),
x1 + x2, # NaNs or infinities of the same sign.
amax + log1p(exp2(-abs(delta))) / np.log(2))
def nan_reduction(a, axis=None, dtype=None, keepdims=False):
a = np_array_ops.array(a)
v = np_array_ops.array(init_val, dtype=a.dtype)
return reduction(np_array_ops.where(isnan(a), v, a),
axis=axis,
dtype=dtype,
keepdims=keepdims)
def testWhere(self):
self.assertAllEqual([[1.0, 1.0], [1.0, 1.0]],
np_array_ops.where([True], [1.0, 1.0],
[[0, 0], [0, 0]]))
