at 35:09, the expression for output in case of stride convolution is (W - K + 2P)/S +1...for W=7, K=3, P = (K-1)/2 = 1 & S=2 we get output as (7 - 3 + 2*1)/2 + 1 = 3 +1 = 4 ...however, the slide shows the output as 3x3 instead of 4x4 at the right hand corner... is it correct..?
Thank you for exellent video! But I have a question here, at 1:05:42, after layer normalization, every sample in x has shape 1xD, while μ has shape Nx1. How do you perform the subtraction x-μ?
I wonder if gamma and beta with 1 x D is a typo? If it should be N x 1? If it is not a typo, doing the subtraction is just using the broadcasting mechanism like in numpy.
Just finished watching the lecture, as per my understanding, X (1 X C X H X W) is the shape of the input vector consumed at once in the algo, and for the calculated means and standard deviations they have mentioned the shape of the output vectors of these parameters in terms of batch size (N X 1 X 1 X 1) as each value uniquely represents each input (1 X C X H X W). It is a late reply but I am replying if someone else would scroll through with similar question to yours!
When you dot product 3d image example(3*32*32) with filter(3*5*5) gives a 2d feature map (28*28) just bcoz of the dot product operation between image and filter
Because both are linear operators, then you can simply concat them after training (think of them as matrices A and B, in test time you multiply C=A*B and you put that instead of both)
In Batch Normalization during Test time at 59:52, what are the averaging equations used to average Mean & Std deviation, sigma ..during the lecture some mention is made of exponential mean of Mean vectors & Sigma vectors...please suggest.
