AI News, Batch Normalization in Neural Networks

Batch Normalization in Neural Networks

If the input layer is benefiting from it, why not do the same thing also for the values in the hidden layers, that are changing all the time, and get 10 times or more improvement in the training speed.

In other words, if an algorithm learned some X to Y mapping, and if the distribution of X changes, then we might need to retrain the learning algorithm by trying to align the distribution of X with the distribution of Y.

To increase the stability of a neural network, batch normalization normalizes the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation.

Consequently, batch normalization adds two trainable parameters to each layer, so the normalized output is multiplied by a “standard deviation” parameter (gamma) and add a “mean” parameter (beta).

From the original batch-norm paper Batch normalization and pre-trained networks like VGG: VGG doesn’t have a batch norm layer in it because batch normalization didn’t exist before VGG.

If we insert a batch norm in a pre-trained network, it will change the pre-trained weights, because it will subtract the mean and divide by the standard deviation for the activation layers and we don’t want that to happen because we need those pre-trained weights to stay the same.

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