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У меня есть этот код для обучения генератора в сети GAN, но я не уверен, как построить точность обучения с помощью tenserflow? функция потерь в зависимости от эпох?

 def train_g(self, g_input, d_input):  loss_g = 0  for e in tqdm(range(1, self.conf.epochs   1)):  # train for self.conf.epochs times for current input  with self.G.graph.as_default():  # get generator predictions for the generator input  g_pred = self.sess_g.run(  self.G.final_layer,  {  self.G.input: g_input  }  )  g_pred = np.swapaxes(g_pred, axis1=0, axis2=-1)  # swap axes for the discirminator moel   with self.D.graph.as_default():  # get the discirminator outout for the generator input - fake variable  d_pred_fake = self.sess_d.run(  self.D.final_layer,  {  self.D.input: g_pred  }  )  with self.G.graph.as_default():  # get all the loss values of the generator model  value = self.sess_g.run(  [self.G.grad_op, self.G.total_loss, self.G.loss_sparse, self.G.loss_centralized, self.G.loss_boundaries, self.G.loss_bicubic, self.G.loss_sum2one, self.G.criterion_loss],  {  self.G.input: g_input,  self.G.d_pred_fake_placeholder:  d_pred_fake  }  )   # add the total loss loss_g variable which is averaged at the end.  loss_g  = value[1]  print('Generator Loss =gt; ', loss_g / self.conf.epochs)   

Любая помощь будет признательна.