message: Update activations.py
  All spelling corrected in the documentation of the code.
diff: @@ -44,10 +44,10 @@ _TF_ACTIVATIONS_V2 = {
  def softmax(x, axis=-1):
  """Softmax converts a real vector to a vector of categorical probabilities.
  - The the elements of the output vector are in range (0, 1) and sum to 1.
  + The elements of the output vector are in range (0, 1) and sum to 1.
  Each vector is handled independently. The `axis` argument sets which axis
  - of the input the finction is applied along.
  + of the input the function is applied along.
  Softmax is often used as the activation for the last
  layer of a classification network because the result could be interpreted as
  @@ -120,7 +120,7 @@ def selu(x):
  and the number of inputs is "large enough"
  (see references for more information).
  - ![](https://cdn-images-1.medium.com/max/1600/1*m0e8lZU_Zrkh4ESfQkY2Pw.png)
  + https://cdn-images-1.medium.com/max/1600/1*m0e8lZU_Zrkh4ESfQkY2Pw.png
  (Courtesy: Blog on Towards DataScience at
  https://towardsdatascience.com/selu-make-fnns-great-again-snn-8d61526802a9)
  @@ -177,7 +177,7 @@ def softsign(x):
  x: Input tensor.
  Returns:
  - The softplus activation: `x / (abs(x) + 1)`.
  + The softsign activation: `x / (abs(x) + 1)`.
  """
  return nn.softsign(x)
  @@ -380,7 +380,7 @@ def deserialize(name, custom_objects=None):
  x : String
  Returns:
  - Tensorlow Activation function denoted by input string.
  + Tensorflow Activation function denoted by input string.
  For example:
  >>> tf.keras.activations.deserialize('linear')
  @@ -393,7 +393,7 @@ def deserialize(name, custom_objects=None):
  ValueError: Unknown activation function:abcd
  Args:
  - name: The name of the actiuvation function.
  + name: The name of the activation function.
  custom_objects: A {name:value} dictionary for activations not build into
  keras.
  