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]))
