(axis=-1)
return Fn('LogSoftmax', lambda x: log_softmax(x, axis=axis))
Returns a layer that applies log softmax along one tensor axis.

  Note that the implementation actually computes x - LogSumExp(x),
  which is mathematically equal to LogSoftmax(x).

  `LogSoftmax` acts on a group of values and normalizes them to look like a set
  of log probability values. (Probability values must be non-negative, and as
  a set must sum to 1. A group of log probability values can be seen as the
  natural logarithm function applied to a set of probability values.)

  Args:
    axis: Axis along which values are grouped for computing log softmax.
Returns a layer that applies log softmax along one tensor axis.
Returns a layer that applies log softmax along one tensor axis .
def LogSoftmax(axis=-1):
  """Returns a layer that applies log softmax along one tensor axis.

  Note that the implementation actually computes x - LogSumExp(x),
  which is mathematically equal to LogSoftmax(x).

  `LogSoftmax` acts on a group of values and normalizes them to look like a set
  of log probability values. (Probability values must be non-negative, and as
  a set must sum to 1. A group of log probability values can be seen as the
  natural logarithm function applied to a set of probability values.)

  Args:
    axis: Axis along which values are grouped for computing log softmax.
  """
  return Fn('LogSoftmax', lambda x: log_softmax(x, axis=axis))
def LogSoftmax ( axis = - 1 ) : return Fn ( 'LogSoftmax' , lambda x : log_softmax ( x , axis = axis ) )