# Is the number of feature maps equal to the number of kernels in the LeNet 5 architecture?

In LeNet 5's first layer, the number of feature maps is equal to the number of kernels. However, the second convolutional layer has a depth different from the 3rd layer. Does the filter size dictate the number of feature maps?

• Which LeNet5's picture are you looking at? – nbro May 16 at 10:01
• medium.com/@shahariarrabby/… in short my doubt is this. does the filter size dictates the number of feature maps or the vice versa? – Stephen Philip May 17 at 4:56

In this case, each kernel has the same depth as the depth of the input cuboid. In the architecture above, we have an (input) cuboid of dimension $$14 \times 14 \times 6$$, where $$6$$ is the depth, which is followed by an (output) cuboid of dimension $$10 \times 10 \times 16$$, where $$16$$ is the depth, which is the number of (output) feature maps (or channels) and it is also equal to the number of kernels applied to the (input) cuboid of dimensions $$14 \times 14 \times 6$$. Each of these $$16$$ kernels has a depth of $$6$$. So, each of these $$16$$ kernels has a shape of $$5\times 5 \times 6$$.