# Millions + <something> parameters

I’m trying to understand the number of weights to be trained in any given layer. The simple way would be to start off with just 1 dense layer and look at how many weights are trained. So I refer to the following code and look at the model summary:

In the batch normalization step, there are 6 parameters being trained. I haven’t read details about batch normalization yet, so maybe I’ll understand this part after reading more about batch norm.
The part that’s throwing me off a little bit is:

We know from math that the number of parameters to be trained should be 103224*224 = 1505280

But looking at the model summary (as seen in the image above), we know that the number of parameters trained are 1505290, which is 10 more than 1505280. Is this because we have 10 output classes (I’m looking at state farm dataset)? How do these output classes play a role in increasing the number of parameters to be trained, apart from the actual multiplication of 103224*224?

This is a really great exercise! Wonderful initiative For batchnorm, check the description in lesson3.ipynb, where I describe (briefly) how it works - I mentioned there that each normalized activation gets 2 new parameters: 1 that is multiplied by the activation, and 1 that is added to that. So you’re normalizing 3 channels, so have 3*2=6 params.

For dense, you’re forgetting the bias. Each output has a bias term, so that’s the extra 10 params.

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