![]() ![]() It doesn’t make sense to calculate the mean and variance for single observations. ![]() This doesn’t matter so much at training time since you are using many mini-batches whose statistical deviations from the global mean and variance average each other out.Īt test and inference time, you are typically feeding single observations to the model to make predictions. For example, if you are using a mini-batch size of 8 observations, it is possible that you randomly pick 8 observations that are far apart and thus give you a higher variance. Depending on the size of your mini-batch, your mean and variance for single mini-batches may differ significantly from the global mean and variance. This implies that we are calculating the mean and variance for each mini-batch when applying batch normalization. In practice, we commonly use mini-batches for training neural networks. Adjusting Batch Normalization at Test and Inference Time ![]() In programming frameworks like Tensorflow, γ and β are tunable hyperparameters that you can set on the BatchNormalization layer. ![]()
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