Comments (1)
This is the hyper-parameter set we used:
optimizer=SGD|batch_size=256|lr=0.05|normalization_type=none
Note that we do not always use the final model. We perform cross-validation. We go for 100 epochs and save each model during training after each epoch. Then, we choose the model with the highest validation accuracy and test it. In other words, we do cross validation for early stopping.
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