Conventions for dimensions of input and weight matrices in neural networks? Im currently learning neural networks and I see conflicting decsriptions of the dimensions of weight and input matrices on the internet. I just wanted to know if there is some convention which more people use than the other.
I currently define my input matrix X with the dimensions of:
(m x n)
Where m is the number of samples and n is the number of features.
And I define my weight matrices with the dimensions:
(a x b)
Where a is the number of neurons in the layer and b is the number of neurons in the last layer.
Is that conventional or should I change something?
I would not say there is such a convention for it per se (if anyone has anything to comment on this, I would also like to know).
I think to make it clearer how the layer's input x interacts with the weights W, it might better to define the dimensions as the following:

x: (m x n)
W: (n x k)
bias term b: (k)

m remains as the number of examples. n represents number of input features and k represents number of neutrons in the layer.
As we know, we compute the output of the layer y as Wx + b. Therefore, the resulting output matrix will be  (m x k)