message: Add negative numbers to the usage and its corresponding formula.
diff: @@ -3249,17 +3249,30 @@ def _accumulate_n_grad(op, grad):
  @tf_export("math.sigmoid", "nn.sigmoid", "sigmoid")
  def sigmoid(x, name=None):
  - """Computes sigmoid of `x` element-wise.
  + r"""Computes sigmoid of `x` element-wise.
  - Specifically, `y = 1 / (1 + exp(-x))`.
  + Formula for calculating sigmoid(x): `y = 1 / (1 + exp(-x))`.
  +
  + For x \in (-inf, inf) => sigmoid(x) \in (0, 1)
  Example Usage:
  - >>> x = tf.constant([0.0, 0.2, 0.3, 0.5, 0.7, 1.0])
  + If a positive number is large, then its sigmoid will approach to 1 since the
  + formula will be `y = <large_num> / (1 + <large_num>)`
  +
  + >>> x = tf.constant([0.0, 1.0, 50.0, 100.0])
  + >>> tf.math.sigmoid(x)
  + <tf.Tensor: shape=(4,), dtype=float32,
  + numpy=array([0.5 , 0.7310586, 1. , 1. ], dtype=float32)>
  +
  + If a negative number is large, its sigmoid will approach to 0 since the
  + formula will be `y = 1 / (1 + <large_num>)`
  +
  + >>> x = tf.constant([-100.0, -50.0, -1.0, 0.0])
  >>> tf.math.sigmoid(x)
  - <tf.Tensor: shape=(6,), dtype=float32, numpy=
  - array([0.5 , 0.54983395, 0.5744425 , 0.62245935, 0.66818774,
  - 0.7310586 ], dtype=float32)>
  + <tf.Tensor: shape=(4,), dtype=float32, numpy=
  + array([0.0000000e+00, 1.9287499e-22, 2.6894143e-01, 0.5],
  + dtype=float32)>
  Args:
  x: A Tensor with type `float16`, `float32`, `float64`, `complex64`, or
  