During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it…

Oscillation in the loss during batch training of a neural network means that the model is

overshooting the optimal point of the loss function and bouncing back and forth. This can prevent

the model from converging to the minimum loss value. One of the main reasons for this

phenomenon is that the learning rate hyperparameter, which controls the size of the steps that the

model takes along the gradient, is too high. Therefore, decreasing the learning rate hyperparameter

can help the model take smaller and more precise steps and avoid oscillation. This is a common

technique to improve the stability and performance of neural network training12.

Reference:

Interpreting Loss Curves

Is learning rate the only reason for training loss oscillation after few epochs?