CNN AlexNet algorithm complexity I'm first year student in machine learning and I really recently started to immersing. So, professor gave me a task, **calculate number of** : * matrix additions * matrix multiplications * matrix divisions Which are processed in the well known convolutional neural network - AlexNet. I found some matherials about it, but I really confused where to start. So, the overall structure might looks like: ![AlexNet structure overview]( But, how can I calculate operations for each type distinctly?

First, you have to make a decision: Do you want to use the "real" alexnet (with the grouping) or what most frameworks use as AlexNet (without grouping).

In case you choose without grouping, you might want to have a look at Table D2 of my masters thesis for a better overview over the layers. Especially the output size / number of filters / stride. Don't take the number of FLOPs too seriously, it is rather a ballpark-estimate.

Then you have to ask you the following:

* How would you implement convolution?
* How would you implement max pooing?
* How would you implement a fully connected layer?



I will not give you the answer directly here, but recommend to have a look at the implementation of a framework of your choice. Or you could search for "pure numpy cnn implementation" or something similar, e.g.

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