and a lower value for simpler models.

Test task: CIFAR10, Wide ResNet

1. Adjust the initial learning rate and momentum based on the size

and complexity of the dataset: higher for large and complex datasets,
lower for small and simple datasets.

2. Adjust the power and lambda parameters based on the desired speed
of the learning process: higher power and lower lambda for faster
learning, lower power and higher lambda for slower learning.
3. Consider any domain-specific constraints when configuring the

optimizer, such as accuracy requirements.

Test task: Fashion-MNIST, Max Pooling CNN with ReLU

1. Set the initial learning rate to a low or medium value.
2. Set the momentum to a high or medium value.
3. Set the power to a low or medium value.
4. Set the lambda to a low or medium value.
5. Adjust the initial learning rate, momentum, power, and lambda
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