Number of parameters calculation in Convolutional NN

Question

I'm new in the CNN study and I started by watching Andrew'NG lessons. There is an example that I did not understand :

How did he compute the #parameters value ?

Answer 1

As you can see in Answer 1 of this StackOverflow question, the formula for the calculation of the number of parameters of a convolutional network is: channels_in * kernel_width * kernel_height * channels_out + channels_out.

But this formula doesn't agree with your data. And in fact the drawing you are showing does not agree with the table you are giving.

If I base myself on the drawing, then the first CN has 3 entry channels, a 5*5 sliding window and 6 output channels, so the number of parameters should be 456.

You give the number 208, and this is the number obtained for 1 entry channel and 8 output channels (the table says 8, while the drawing says 6). So it seems that 208 is correctly obtained from the table data, if we consider that there is one input channel and not three.

As for the second CN, with 6 entry channels, a sliding window 5*5 and 16 output channels, you need 2,416 parameters, which looks suspiciously close to 416, the number given in the table.

As for the remaining networks it is always the number of input dimension times the number of output dimensions, plus one: 5*5*16*120+1=48,001, 120*84+1=10,081, 84*10+1=841.