Specifically, in deep networks (with 8+ layers) this effect starts to become apparent, as the earliest layers are adjusted. This has a massive effect on the outcome, and the error jumps around.
The noise also happens in a network with just 2-3 layers. However, it is only apparent when the error reached 0.00, and the iRprop+ keeps running. In some cases it will suddenly cause a very abrupt change, and the cross-entropy cost function will produce error larger than 1 000 000
I've built a custom LSTM in c++ and experiencing this noise during debugging of overfitting
Perhaps the learning rates from each layer should be initialized with a different magnitude, depending on the layer's depth? [link]