def funcion_objetivo(self, solucion, n=None, cmd=None):
if n is None:
n = self.entrenamientos
red = RedNeuronal(self.entradas, self.salidas, self.alfa,
self.momentum, solucion)
error = 0.0
for i in range(n - 1):
red.backpropagation([0.0, 0.0], [0.0])
red.backpropagation([0.0, 1.0], [1.0])
red.backpropagation([1.0, 0.0], [1.0])
red.backpropagation([1.0, 1.0], [0.0])
error += red.backpropagation([0.0, 0.0], [0.0])[0]
error += red.backpropagation([0.0, 1.0], [1.0])[0]
error += red.backpropagation([1.0, 0.0], [1.0])[0]
error += red.backpropagation([1.0, 1.0], [0.0])[0]
if cmd is not None:
print "[0, 0]", red.backpropagation([0.0, 0.0], [0.0])
print "[0, 1]", red.backpropagation([0.0, 1.0], [1.0])
print "[1, 0]", red.backpropagation([1.0, 0.0], [1.0])
print "[1, 1]", red.backpropagation([1.0, 1.0], [0.0])
return error, red
return error
def funcion_objetivo(self, solucion, n=None, cmd=None):
if n is None:
n = self.entrenamientos
red = RedNeuronal(self.entradas, self.salidas, self.alfa, self.momentum, solucion)
error = 0.0
for i in range(n - 1):
red.backpropagation([0.0, 0.0], [0.0])
red.backpropagation([0.0, 1.0], [1.0])
red.backpropagation([1.0, 0.0], [1.0])
red.backpropagation([1.0, 1.0], [0.0])
error += red.backpropagation([0.0, 0.0], [0.0])[0]
error += red.backpropagation([0.0, 1.0], [1.0])[0]
error += red.backpropagation([1.0, 0.0], [1.0])[0]
error += red.backpropagation([1.0, 1.0], [0.0])[0]
if cmd is not None:
print "[0, 0]", red.backpropagation([0.0, 0.0], [0.0])
print "[0, 1]", red.backpropagation([0.0, 1.0], [1.0])
print "[1, 0]", red.backpropagation([1.0, 0.0], [1.0])
print "[1, 1]", red.backpropagation([1.0, 1.0], [0.0])
return error, red
return error
def funcion_objetivo(self, solucion, n = None, cmd = None):
if n is None:
n = self.entrenamientos
muestras = cargar_archivos(["muestraHandPointDown.txt", "muestraHandPointUp.txt"])
contador = 0
red = RedNeuronal(self.entradas, self.salidas, self.alfa, self.momentum, solucion)
"""
error = 0.0
for i in range(n):
temp = red.backpropagation(muestras[contador][0], muestras[contador][1])[0]
contador += 1
if contador >= len(muestras):
contador = 0
if i >= n-10:
error += temp
if cmd is not None:
return float(error)/10, red
return error
"""
error = 0.0
for i in range(n-1):
red.backpropagation([0.0, 0.0], [0.0])
red.backpropagation([0.0, 1.0], [1.0])
red.backpropagation([1.0, 0.0], [1.0])
red.backpropagation([1.0, 1.0], [0.0])
error += red.backpropagation([0.0, 0.0], [0.0])[0]
error += red.backpropagation([0.0, 1.0], [1.0])[0]
error += red.backpropagation([1.0, 0.0], [1.0])[0]
error += red.backpropagation([1.0, 1.0], [0.0])[0]
if cmd is not None:
print "[0, 0]", red.backpropagation([0.0, 0.0], [0.0])
print "[0, 1]", red.backpropagation([0.0, 1.0], [1.0])
print "[1, 0]", red.backpropagation([1.0, 0.0], [1.0])
print "[1, 1]", red.backpropagation([1.0, 1.0], [0.0])
return error, red
return error
