understanding keras 's layer



dot - computes a dot product between 2 tensors. Just like doing multiplication.

conv2d - creates a convulsion - a square box that you hover over a 2d image matrix.

maxPool2d - taking the max values out of a stride after we iterate our image with a predefined filter size. Small little window walks through our image to produce a matrix - which made up of biggest value of a filter.

To see more please read from this link here.

https://www.quora.com/What-is-max-pooling-in-convolutional-neural-networks

dense layer - it is a linear function which maps input directly to output based on a predefined weight. sometimes weight can be a softmax function.



dropout is a techniques used to prevent over fitting in neural network. Performs averaging and prevent co-adaptions.





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If you're getting  exception when trying to assigned key to a symmetric key and getting error message below :- The specified initialization vector (IV) does not match the block size for this algorithm And your code probably look like something below :- Then you need to go into debug mode and start looking into the supported size for IV as shown in diagram below :- As you can see, IV is 16 bytes, so you need to provide 16 bytes. The same goes for Key field too, if you're adding anything to it. As long as you provide a valid key size, then we're good and you're code will be ready.
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