old_contents: import autograd.numpy as np
  
  """
  References:
  https://en.wikipedia.org/wiki/Activation_function
  """
  
  
  def sigmoid(z):
      return 1.0 / (1.0 + np.exp(-z))
  
  
  def softmax(z):
      # Avoid numerical overflow by removing max
      e = np.exp(z - np.amax(z, axis=1, keepdims=True))
      return e / np.sum(e, axis=1, keepdims=True)
  
  
  def linear(z):
      return z
  
  
  def softplus(z):
      """Smooth relu."""
      # Avoid numerical overflow, see:
      # https://docs.scipy.org/doc/numpy/reference/generated/numpy.logaddexp.html
      return np.logaddexp(0.0, z)
  
  
  def softsign(z):
      return z / (1 + np.abs(z))
  
  
  def tanh(z):
      return np.tanh(z)
  
  
  def relu(z):
      return np.maximum(0, z)
  
  
  def get_activation(name):
      """Return activation function by name"""
      try:
          return globals()[name]
      except:
          raise ValueError('Invalid activation function.')
  
new_contents: import autograd.numpy as np
  
  """
  References:
  https://en.wikipedia.org/wiki/Activation_function
  """
  
  
  def sigmoid(z):
      return 1.0 / (1.0 + np.exp(-z))
  
  
  def softmax(z):
      # Avoid numerical overflow by removing max
      e = np.exp(z - np.amax(z, axis=1, keepdims=True))
      return e / np.sum(e, axis=1, keepdims=True)
  
  
  def linear(z):
      return z
  
  
  def softplus(z):
      """Smooth relu."""
      # Avoid numerical overflow, see:
      # https://docs.scipy.org/doc/numpy/reference/generated/numpy.logaddexp.html
      return np.logaddexp(0.0, z)
  
  
  def softsign(z):
      return z / (1 + np.abs(z))
  
  
  def tanh(z):
      return np.tanh(z)
  
  
  def relu(z):
      return np.maximum(0, z)
  
  
  def leakyrelu(z, a=0.01):
      return np.maximum(z * a, z)
  
  
  def get_activation(name):
      """Return activation function by name"""
      try:
          return globals()[name]
      except:
          raise ValueError('Invalid activation function.')
  
subject: Add Leaky ReLU activation. Differentiation with autograd package confirmed to work correctly.