message: Added description for softmax and sigmoid
  It's my first contribution so I'm not sure whether everything is right the way i did it. Also I am not a native english speaker so maybe there are some spelling errors.
diff: @@ -42,14 +42,18 @@ _TF_ACTIVATIONS_V2 = {
  @keras_export('keras.activations.softmax')
  def softmax(x, axis=-1):
  - """Softmax activation function.
  + """The softmax activation function transforms the outputs so that all values are in
  + range (0, 1) and sum to 1. It is is often used as activation of the last layer of a
  + classification network because the result could be interpreted as probability
  + distribution. To calculate the softmax all inputs `x` become exp(x) divided by the sum
  + of all exponential values.
  Arguments:
  x : Input tensor.
  axis: Integer, axis along which the softmax normalization is applied.
  Returns:
  - Tensor, output of softmax transformation.
  + Tensor, output of softmax transformation (all values are non-negative and sum to 1).
  Raises:
  ValueError: In case `dim(x) == 1`.
  @@ -170,6 +174,17 @@ def tanh(x):
  @keras_export('keras.activations.sigmoid')
  def sigmoid(x):
  + """Sigmoid.
  + Applies the sigmoid activation function. The sigmoid function is defined as
  + 1 divided by (1 + exp(-x)). It's curve is like an "S" and is like a smoothed
  + version of the Heaviside function. For small values (<-5) the sigmoid returns
  + a value close to zero and for larger values (>5) the result of the funtion gets
  + close to 1.
  + Arguments:
  + x: A tensor or variable.
  + Returns:
  + A tensor.
  + """
  return nn.sigmoid(x)
  