[infurl]

@korrelan say yes, it's easier

[LOCKSUIT]

I've went and studied them many times and just yesterday a 3 page all about them padding relu pooling dropout stride filter amount and size and layer amount.

His post sounding nothing like them. He kept talking about anything but my question.

[Korrelan]

I tried... yes lock...

@ Infurl 

[LOCKSUIT]

The only part that finally but breifly says the answer is:

The CNN never has to store whole images; using just eight feature maps (more is better) you can describe thousands of objects. You only need to store the feature map numbers and their relative positions to each other.

So I'm guessing 7M mems & only 1,000 filters OK.

But does 1,000 filters allow any SINGLE *1 of the 7M images to be selected? Or 1,000 filters means only 1,000 possible selections?

[infurl]

@locksuit

start writing the software and you'll soon figure it out

parameterise everything so you can try different values to find the ones that work

use Python, it has training wheels

and FFS stop annoying the grown ups

[LOCKSUIT]

I cannot write what I don't understand.

[kei10]

First, start writing basic neural network before working your way towards harder ones like CNN, and then you will get everything even easier rather than asking questions again and again and still doesn't understand.

What do they say? First time for everything, and baby steps.



Expected reaction from Locksuit: "Hell no, I am the most intelligent being ever, oho, oho, oho, oho, oho." ... Then he proceeds to ask more questions.

Here, in case you're too lazy to get python, let me put it here so it will be a life saver for you.

https://www.python.org/

[LOCKSUIT]

I cannot write what I don't understand.

I meant CNNs.

[keghn]

 Well Cnn is a big detector neural network. But what it really is a tiny nn doing all the work. Instead of having a big input
layer that take in a image of 1600 x 900. A cnn uses a tiny NN of 10 x 10 that scans the whole picture. The little
networks travel over the image it look for lines, corner lines, and other lines. When it over one it activates
and sends it forward to a empty image that record the Little scanning NN output.
 Once done, then a new little NN scans the information collected in the second level. The little scanning NN look for combination of line together that make up a finger or an
eye. These detection are past to a new empty image, the third layer. That capture the output of The tiny scanning NN.

On the third layer. The third level scanning nn on level look for combination of detection found on the second level. like faces, arms,
and chest and stomachs. Next layer is people, cars building and so on. Next lay is mountains, moon, and forest. Next
layer is galaxies, nebulas, and constellations.