Оценка ошибки PyBrain со временем не меняется при обучении сети

Question

Я использую PyBrain 0.3 (с python 2.7.9), и когда я тренирую свою сеть с BackpropTrainer, нет никаких изменений, это общая ошибка, даже после некоторых 10 - 20 итераций.

Любые предложения?

Edit:

path = "../pckld_ROIs/master_r19_7_5_emissivity_sub.pkl" 
roi = RegionsOfInterest(path) 
roi.set_aggregate(True) 
net = ClassificationNet(roi, ['soil'], neigborhood_size=3) 
net.neural_net.randomize() 
net.set_trainer(learning_rate=0.1, verbose=True, momentum=0.1, weight_decay=0.01) 
net.train_network(max_epochs=300, verbose=True) 

RegionsOfInterest Где это структура для проведения и структурирования точек данных.

def set_trainer(self, learning_rate=0.01, lrdecay=1.0, 
       momentum=0., verbose=False, batch_learning=False, 
       weight_decay=0.): 
     """ 
      Sets the trainer. If the data has been split, it uses the training data as data for the back-propagation 
      algorithm. If not, it uses the entire data set. 
     :param learning_rate:  The rate in which the parameters are changed into the direction of the gradient. 
     :param lrdecay:    How much the learning rate decreases per epoch. Multiplicative! 
     :param momentum:   The weight of the previous time-step's gradient is affecting the next iteration. 
     :param verbose:    Toggles verbose mode. Default is off. 
     :param batch_learning:  Will be parameters be updated at the end of the epoch, or continuously? The default 
            is continuously. 
     :param weight_decay:  How much the weighs are decreasing. 0 corresponds to no decrease. 
     :type learning_rate:  float 
     :type lrdecay:    float 
     :type momentum:    float 
     :type verbose:    bool 
     :type batch_learning:  bool 
     :type weight_decay:   float 
     :return: None 
     """ 
     if self.training_data is not None: 
      self.trainer = BackpropTrainer(self.neural_net, self.training_data, 
              learning_rate, lrdecay, momentum, verbose, 
              batch_learning, weight_decay) 
     else: 
      self.trainer = BackpropTrainer(self.neural_net, self.training_data, 
              learning_rate, lrdecay, momentum, verbose, 
              batch_learning, weight_decay) 



def train_network(self, 
        max_epochs=-1, 
        verbose=False, 
        continue_epochs=10, 
        validation_proportion=0.25, 
        force_split=False): 
     """ 
      Trains the network until the error rate converges. 
     :param max_epochs:    The maximum number of epochs the network is trained. The default is to not set a 
             maximum. 
     :param verbose:     Toggles verbose mode or not. The default is not. 
     :param continue_epochs:   How much longer the training should go on if we find a minimum in the error. 
     :param validation_proportion: The proportion that will be used for validation. The default is 25%, given that 
             the 'self.validation_data' has not been set. 
     :param force_split:    If the 'self.validation_data' as been set, we can force another split on the 
             training data. 
     :type max_epochs:    int 
     :type verbose:     bool 
     :type continue_epochs:   int 
     :type validation_proportion: float 
     :type force_split:    bool 
     :return:      None 
     """ 
     if max_epochs <= 0: 
      # We don't give it a stop criteria for time. 
      if self.validation_data is not None: 
       # We have already set aside some of the data for validation 
       if force_split: 
        # Screw that! I want the data to be split again! 
        self.trainer.trainUntilConvergence(self.training_data, None, verbose, 
                 continue_epochs, validation_proportion) 
       else: 
        self.trainer.trainUntilConvergence(self.training_data, None, verbose, continue_epochs, 1) 
      else: 
       # We have no validation data set 
       self.trainer.trainUntilConvergence(self.data_set, None, verbose, continue_epochs, validation_proportion) 
     else: 
      # We have a stop criteria. 
      if self.validation_data is not None: 
       # We have already split the data into a validation set, and a training set. 
       if force_split: 
        # Screw that! I want the data to be split again 
        self.trainer.trainUntilConvergence(self.training_data, max_epochs, verbose, 
                 continue_epochs, validation_proportion) 
       else: 
        self.trainer.trainUntilConvergence(self.training_data, max_epochs, verbose, continue_epochs, 1) 
      else: 
       # We do not have a validation data set. 
       self.trainer.trainUntilConvergence(self.data_set, max_epochs, verbose, 
                continue_epochs, validation_proportion) 

Answer 1

Трудно сказать, не видя данных, но одна из возможностей заключается в том, что вы пытаетесь сделать что-то невозможное. Например, если целью для вывода является 10, а ваш выходной уровень - нейроны, которые переходят только от 0 до 1, тогда ошибка застрянет на 9. Без ввода ваших входных данных для работы я не уверен, но вы можете проверить, что ваши входы и цели масштабируются соответствующим образом.