Oscillation in the loss during batch to ensure that it converges?

During batch training of a neural network, you notice that there is an oscillation in the loss.

How should you adjust your model

Oscillation in the loss during batch to ensure that it converges?
A . Increase the size of the training batch
B . Decrease the size of the training batch
C . Increase the learning rate hyperparameter
D . Decrease the learning rate hyperparameter

View Answer

Answer: D

Explanation:

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?