Log number of model parameters when starting the training
@@ -165,6 +165,7 @@ class Trainer(abc.ABC):
training_loss, variables=self._model.trainable_variables)
if first_call and self._is_master:
self._model.visualize(self._checkpoint.model_dir)
+ tf.get_logger().info("Number of model parameters: %d", self._model.count_params())
return training_loss, reported_loss
def _save_checkpoint(self, step, moving_average=None):
