according to the characteristics of the task, such as the dataset
size, model architecture, and the complexity of the prediction task.
For example, for tasks with larger datasets, a higher initial
learning rate may be beneficial, while for tasks with smaller
datasets, a lower initial learning rate may be more suitable.
Similarly, for tasks with more complex models, a higher momentum may
be beneficial, while for simpler models, a lower momentum may be more
suitable. Additionally, for tasks with more complex prediction tasks,
a higher power may be beneficial, while for simpler tasks, a lower
power may be more suitable. Finally, for tasks with more complex
models, a higher lambda may be beneficial, while for simpler models,
a lower lambda may be more suitable.

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

1. Choose an initial LR that is appropriate for the size of the

dataset and complexity of the model.

2. Set the momentum to a value that is appropriate for the size of
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