message: Updated description of softmax and sigmoid
  Added suggestions
diff: @@ -43,9 +43,9 @@ _TF_ACTIVATIONS_V2 = {
  @keras_export('keras.activations.softmax')
  def softmax(x, axis=-1):
  """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
  + range (0, 1) and sum to 1. It is often used as the activation for the last layer of a
  + classification network because the result could be interpreted as a probability
  + distribution. To calculate the softmax, all inputs `x` become exp(x) divided by the sum
  of all exponential values.
  Arguments:
  @@ -177,9 +177,9 @@ 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.
  + version of the Heaviside (Unit Step Function) function. For small values (<-5)
  + the sigmoid returns a value close to zero and for larger values (>5) the result
  + of the function gets close to 1.
  Arguments:
  x: A tensor or variable.
  Returns:
  