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Google Cloud Certified Professional Machine Learning Engineer · Google Cloud
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When you observe oscillations in the loss during training, it is often a sign that the learning rate is too high. A high learning rate can cause the optimizer to overshoot the minimum of the loss function
A. Decrease Batch Size: While a smaller batch size can sometimes help with convergence, it can also lead to slower training. It might not necessarily address the issue of oscillation. C. Increase Learning Rate: A higher learning rate can cause the loss to jump around more erratically, potentially worsening the oscillation problem. D. Increase Batch Size: A larger batch size can lead to smoother updates but might also make the model less sensitive to local gradients and hinder convergence, especially with an already oscillating loss.
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