# Backpropagate error and store in neurons
def backward_propagate_error(network, expected):
  for i in reversed(range(len(network))):
  layer = network[i]
  errors = list()
 # calculate the partial derivative of the loss with repsect to the input before activation
  for j in range(len(layer)):
   neuron = layer[j] 
   neuron['delta'] = errors[j] * transfer_derivative(neuron['output'])