def get_metrics(self):
all_of_metrics = {}
for of_name, of_obj in self.output_features.items():
all_of_metrics[of_name] = of_obj.get_metrics()
all_of_metrics[COMBINED] = {
LOSS: get_scalar_from_ludwig_metric(self.eval_loss_metric)
+ get_scalar_from_ludwig_metric(self.eval_additional_losses_metrics)
}
return all_of_metrics
def get_metrics(self):
"""Returns a dictionary of metrics for each output feature of the model."""
all_of_metrics = {}
for of_name, of_obj in self.output_features.items():
all_of_metrics[of_name] = of_obj.get_metrics()
all_of_metrics[COMBINED] = {
LOSS:
get_scalar_from_ludwig_metric(self.eval_loss_metric) +
get_scalar_from_ludwig_metric(self.eval_additional_losses_metrics)
}
return all_of_metrics
def get_metrics(self):
metric_vals = {}
for metric_name, metric_fn in self.metric_functions.items():
try:
metric_vals[metric_name] = get_scalar_from_ludwig_metric(
metric_fn)
except Exception as e:
logger.error(
f"Caught exception computing metric: {metric_name}. Exception: {e}"
)
return metric_vals
>>>>>>> upstream/master
def get_metrics(self):
all_of_metrics = {}
for of_name, of_obj in self.output_features.items():
all_of_metrics[of_name] = of_obj.get_metrics()
<<<<<<< HEAD
all_of_metrics[COMBINED] = {
<<<<<<< HEAD
#LOSS: self.eval_loss_metric.result().numpy()
=======
>>>>>>> upstream/master
LOSS: self.eval_loss_metric.compute().detach().numpy().item()
}
=======
all_of_metrics[COMBINED] = {LOSS: get_scalar_from_ludwig_metric(self.eval_loss_metric)}
>>>>>>> upstream/master
return all_of_metrics
def reset_metrics(self):
for of_obj in self.output_features.values():
of_obj.reset_metrics()
<<<<<<< HEAD
#self.eval_loss_metric.reset_states()
=======
>>>>>>> upstream/master
self.eval_loss_metric.reset()
<<<<<<< HEAD
def collect_weights(
self,