def _setup_loss(self): if self.loss[TYPE] == 'mean_squared_error': self.train_loss_function = MSELoss() self.eval_loss_function = MSEMetric(name='eval_loss') elif self.loss[TYPE] == 'mean_absolute_error': self.train_loss_function = MAELoss() self.eval_loss_function = MAEMetric(name='eval_loss') else: raise ValueError('Unsupported loss type {}'.format( self.loss[TYPE]))
def _setup_loss(self): if self.loss[TYPE] == "mean_squared_error": self.train_loss_function = MSELoss() elif self.loss[TYPE] == "mean_absolute_error": self.train_loss_function = MAELoss() elif self.loss[TYPE] == "root_mean_squared_error": self.train_loss_function = RMSELoss() elif self.loss[TYPE] == "root_mean_squared_percentage_error": self.train_loss_function = RMSPELoss() else: raise ValueError( "Unsupported loss type {}".format(self.loss[TYPE]) ) self.eval_loss_function = self.train_loss_function
def _setup_loss(self): if self.loss[TYPE] == 'mean_squared_error': self.train_loss_function = MSELoss() self.eval_loss_function = MSEMetric(name='eval_loss') elif self.loss[TYPE] == 'mean_absolute_error': self.train_loss_function = MAELoss() self.eval_loss_function = MAEMetric(name='eval_loss') elif self.loss[TYPE] == SOFTMAX_CROSS_ENTROPY: self.train_loss_function = SoftmaxCrossEntropyLoss( num_classes=self.vector_size, feature_loss=self.loss, name='train_loss') self.eval_loss_function = SoftmaxCrossEntropyMetric( num_classes=self.vector_size, feature_loss=self.loss, name='eval_loss') else: raise ValueError('Unsupported loss type {}'.format( self.loss[TYPE]))