Number of parameters in convolutional layer

Number of parameters in a CONV layer would be : ((m * n * d)+1)* k), added 1 because of the bias term for each filter. The same expression can be written as follows: ((shape of width of the filter * shape of height of the filter * number of filters in the previous layer+1)*number of filters).

1. How many parameters are in a convolutional layer?
2. How many parameters should my CNN have?
3. How do you determine the number of parameters in a fully connected layer?

How many parameters are in a convolutional layer?

In a CNN, each layer has two kinds of parameters : weights and biases.

How many parameters should my CNN have?

"In color images, every filter is itself a 3D filter. This means every filter has a number of parameters: (height x width x depth) = (3 x 3 x 3 = 27) . You can see how the network complexity increases when processing color images because it has to optimize more parameters..."

How do you determine the number of parameters in a fully connected layer?

Fully-connected layers: In a fully-connected layer, all input units have a separate weight to each output unit. For n inputs and m outputs, the number of weights is n*m . Additionally, you have a bias for each output node, so you are at (n+1)*m parameters.