I have exactly this scenario and I need to know how many connections this set has. I searched in several places and I'm not sure of the answer. That is, I do not know how to calculate the number of connections on my network, this is still unclear to me. What I have is exactly as follows:
** Having bias in all but least of the input
I would calculate this as follows: ((784 * 400) + bias) + ((400 * 200) + bias) + ((200 * 10) + bias) = XXX
I do not know if this is correct. I need help figuring out how to solve this, and if it's not just something mathematical, what's the theory to do this calculation?
Thank you.
Your calculation is correct for total number of weights. When you have n neurons connected to m neurons, the number connections between neurons is n*m. You can see this by drawing a small graph, say 3 neurons connected to 4 neurons. You will see that there's 12 connections between the two layers. So if you want connections rather than weights, just drop the '+bias' parts of your equation.
If you want total weights, then the number is simply (n*m+m) since you get 1 bias weight for each of the m neurons in the second layer.
Total connections in that neural network: (784*400)+(400*200)+(200*10)
Total weights in that neural network: (784*400+400)+(400*200+200)+(200*10+10)