Why is the mse as a loss different from the mse as a metric in keras?

I have a regression model built in keras. The loss is mse. The output during training is as follows:

4/4 [==============================] - 16s 1s/step - loss: 21.4834 - root_mean_squared_error: 4.6350 - full_mse: 23.5336 - mean_squared_error: 23.5336 - val_loss: 32.6890 - val_root_mean_squared_error: 5.7174 - val_full_mse: 32.6890 - val_mean_squared_error: 32.6890

Why is the mse as a loss different from the mse as a metric? (loss = 21.4834; mse = 23.5336; why do these values differ? They ought to be the same.)

And why is this only the case for the traing set, not the validation set? (val_loss = 32.6890; val_mse = 32.6890; these values are equal, as it ought to be.)

Any ideas?

I'm posting this as answer as it looks like it was the solution of the problem.

The training MSE loss ("loss") is calculated as a form of average over training, where the weights are changing. "metric" MSE ("mse") is calculated after the epoch without weights updating.

For validation ("val_loss" and "val_mse") both are calculated without weight updates.

Additionally it's possible that the shown MSE loss is something like a moving average, where not all minibatches of the the epoch are weighted equally. I don't think this is the case for the given problem as the validation values match. This depends on the implementation.