red neuronal artificial | Python Fiddle

from math import exp
class RNA:
  def __init__(self, nInput, nHidden, nOutput, inputVector, pesos, b):
    self.inputVector = inputVector
    self.nInput = len(inputVector)
    self.nHidden = nHidden
    self.nOutput = nOutput
    self.pesos = pesos
    self.b = b

#Inicializa un vector que contendra el valor para cada unidad de cada capa
    self.outputVector = [0 for _ in range(3)]
    self.outputVector[0] = inputVector
    self.outputVector[1] = [-1 for _ in range(nHidden)]
    self.outputVector[2] = [-1 for _ in range(nOutput)]
    
  def calculateHidden(self, hiddenWeight, hiddenB):
    y = z = [0 for _ in range(self.nHidden)]
    for i in range(0, self.nHidden):
      for j in range(0, self.nInput):
        # Se utiliza el indice i,j porque a cada unidad le corresponde un vector con pesos     
        z[i] = z[i] + (self.inputVector[j] * hiddenWeight[i][j] + hiddenB[i])                 
      y[i] = self.calculateSigmoidal(z[i])
    self.outputVector[1] = y
    return y

def calculateOutput(self, outputWeight, outputB):
    y = z = [0 for _ in range(self.nOutput)]
    #[[1, 1], [1,1] ]
    for i in range(0,self.nOutput):
      for j in range(0, self.nHidden):
        z[i] = z[i] + (self.outputVector[1][j] * outputWeight[i][j] + outputB[i])
      y[i] = self.calculateSigmoidal(z[i])
    self.outputVector[2] = y
    return y
            
  def calculateSigmoidal(self, suma):
    return (1.0/(1+(exp(-1*suma))))
    
  def linearActivation():
    pass

if __name__ == "__main__":
  #primer caso
  weights = [[[1,1]],[[1]]]
  b = [[-1, -1],[1],[1]]
  inputValues = [1,1]
  r = RNA(2, 1, 1, inputValues, weights, b)
  print "Output de la red neuronal con unidades 2, 1, 1 para su respectiva capa"
  print r.calculateHidden(weights[0], b[1])
  print r.calculateOutput(weights[1], b[2])

#segundo caso
  weights = [ [[1.5,1.5], [1.5,1.5]], [[1,1]]]
  b = [[-1, -1],[1, 1],[1]]
  r = RNA(2, 2, 1, inputValues, weights, b)
  print "Output de la red neuronal con unidades 2, 2, 1 para su respectiva capa"
  r.calculateHidden(weights[0], b[1])
  r.calculateOutput(weights[1], b[2])
  print r.outputVector[1]
  print r.outputVector[2]

#tercer caso
  print "Output de la red neuronal con unidades 2, 2, 2 para su respectiva capa"
  weights = [ [[1.5,1.5], [1.5,1.5]], [[1, 1], [1,1] ]]  
  b = [[-1, -1],[1, 1],[1, 1]]
  r = RNA(2, 2, 2, inputValues, weights, b)
  r.calculateHidden(weights[0], b[1])
  r.calculateOutput(weights[1], b[2])
  print r.outputVector[1]
  print r.outputVector[2]

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