parameters: (x, name=None)
docstring: Computes log sigmoid of `x` element-wise.
  
    Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,
    we use `y = -tf.nn.softplus(-x)`.
  
    Args:
      x: A Tensor with type `float32` or `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A Tensor with the same type as `x`.
  
    Usage Example:
  
    If a positive number is large, then its log_sigmoid will approach to 0 since
    the formula will be `y = log( <large_num> / (1 + <large_num>) )` which
    approximates to `log (1)` which is 0.
  
    >>> x = tf.constant([0.0, 1.0, 50.0, 100.0])
    >>> tf.math.log_sigmoid(x)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=
    array([-6.9314718e-01, -3.1326169e-01, -1.9287499e-22, -0.0000000e+00],
          dtype=float32)>
  
    If a negative number is large, its log_sigmoid will approach to the number
    itself since the formula will be `y = log( 1 / (1 + <large_num>) )` which is
    `log (1) - log ( (1 + <large_num>) )` which approximates to `- <large_num>`
    that is the number itself.
  
    >>> x = tf.constant([-100.0, -50.0, -1.0, 0.0])
    >>> tf.math.log_sigmoid(x)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=
    array([-100.       ,  -50.       ,   -1.3132616,   -0.6931472],
          dtype=float32)>
docstring_summary: Computes log sigmoid of `x` element-wise.
docstring_tokens: Computes log sigmoid of x element - wise .
function: def log_sigmoid(x, name=None):
    """Computes log sigmoid of `x` element-wise.
  
    Specifically, `y = log(1 / (1 + exp(-x)))`.  For numerical stability,
    we use `y = -tf.nn.softplus(-x)`.
  
    Args:
      x: A Tensor with type `float32` or `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A Tensor with the same type as `x`.
  
    Usage Example:
  
    If a positive number is large, then its log_sigmoid will approach to 0 since
    the formula will be `y = log( <large_num> / (1 + <large_num>) )` which
    approximates to `log (1)` which is 0.
  
    >>> x = tf.constant([0.0, 1.0, 50.0, 100.0])
    >>> tf.math.log_sigmoid(x)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=
    array([-6.9314718e-01, -3.1326169e-01, -1.9287499e-22, -0.0000000e+00],
          dtype=float32)>
  
    If a negative number is large, its log_sigmoid will approach to the number
    itself since the formula will be `y = log( 1 / (1 + <large_num>) )` which is
    `log (1) - log ( (1 + <large_num>) )` which approximates to `- <large_num>`
    that is the number itself.
  
    >>> x = tf.constant([-100.0, -50.0, -1.0, 0.0])
    >>> tf.math.log_sigmoid(x)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=
    array([-100.       ,  -50.       ,   -1.3132616,   -0.6931472],
          dtype=float32)>
    """
    with ops.name_scope(name, "LogSigmoid", [x]) as name:
      x = ops.convert_to_tensor(x, name="x")
      return gen_math_ops.neg(gen_nn_ops.softplus(-x), name=name)
function_tokens: def log_sigmoid ( x , name = None ) : with ops . name_scope ( name , "LogSigmoid" , [ x ] ) as name : x = ops . convert_to_tensor ( x , name = "x" ) return gen_math_ops . neg ( gen_nn_ops . softplus ( - x ) , name = name )