stochastic_gradient_estimators.py 文件源码

def _logexpm1(x):
  """Stably calculate log(exp(x)-1)."""
  with ops.name_scope("logsumexp1"):
    eps = np.finfo(x.dtype.as_numpy_dtype).eps
    # Choose a small offset that makes gradient calculations stable for
    # float16, float32, and float64.
    safe_log = lambda y: math_ops.log(y + eps / 1e8)  # For gradient stability
    return array_ops.where(
        math_ops.abs(x) < eps,
        safe_log(x) + x/2 + x*x/24,  # small x approximation to log(expm1(x))
        safe_log(math_ops.exp(x) - 1))