Why do I see a drop (or jump) in my final validation accuracy
Why do I see a drop (or jump) in my final validation accuracy when training a deep learning network?
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If the network contains batch normalization layers, the final validation metrics are often different from the validation metrics evaluated during training. This is because the network undergoes a ‘finalization’ step after the last iteration to compute the batch normalization layer statistics on the entire training data, while during training the batch normalization statistics are computed from the mini-batches.
If in addition to batch normalization layers the network contains dropout layers, the interaction between these two layers can aggravate this issue, as described here: https://arxiv.org/abs/1801.05134
If one removes the batch normalization (and dropout) layers from the network, the ‘final’ accuracy should be the same as the last iteration accuracy.
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