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A multiplicative constant scaling factor is often applied to the model outputto adjust the dynamics of neural network parameters. This has been used as oneof the key interventions in an empirical study of lazy and active behavior.However, we show that the combination of such scaling and a commonly usedadaptive learning rate optimizer strongly affects the training behavior of theneural network. This is problematic as it can cause \emph{unintended behavior}of neural networks, resulting in the misinterpretation of experimental results.Specifically, for some scaling settings, the effect of the adaptive learningrate disappears or is strongly influenced by the scaling factor. To avoid theunintended effect, we present a modification of an optimization algorithm anddemonstrate remarkable differences between adaptive learning rate optimizationand simple gradient descent, especially with a small ($<1.0$) scaling factor.

Unintended Effects on Adaptive Learning Rate for Training Neural Network with Output Scale Change