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I think it's just saying "Define a learnable scale and bias parameter for each channel. Multiply each input channel by the corresponding scale and add bias.
We're starting to get close but some differences remain. Currently my network occasionally gets exploding gradients near the start of training.
Let's start by taking a look at each model to ensure things look correct.
Since there's so much going on, we'll break it into different pieces and compare those one at a time.
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