def backward_propagate_error(network, expected):
 for i in reversed(range(len(network))):
  layer = network[i]
  # calculate the loss forr each layer
  if i != len(network)-1:
   ...
  else: 
      # ∂L/∂a of the output layer  
   for j in range(len(layer)):
    neuron = layer[j]
    errors.append(neuron['output'] - expected[j])