y, y_pred, sample_weight, regularization_losses=self.losses)
self.compiled_metrics.update_state(y, y_pred, sample_weight)
return {m.name: m.result()for m in self.metrics}
def make_test_function(self):
Creates a function that executes one step of evaluation.
After Change
y, y_pred, sample_weight, regularization_losses=self.losses)
self.compiled_metrics.update_state(y, y_pred, sample_weight)
// Collect metrics to return
return_metrics = {}
for metric in self.metrics:
result = metric.result()
if isinstance(result, dict):
return_metrics.update(result)
else:
return_metrics[metric.name] = result
return return_metrics
def make_test_function(self):
Creates a function that executes one step of evaluation.
