# If I wanted to calculate multiple feature maps in a convolutional layer, should the filters be trained individually?

Assume I have an input of size $$32 \times 32 \times 3$$ and pass it to a convolution layer. Now, if my kernel size were to be $$5 \times 5 \times 3$$ and the depth of my convolution layer were to be 1, only one feature map would be produced for the image. Here, each neuron would have $$5 \times 5 \times 3 = 75$$ weights (+1 bias).

If I wanted to calculate multiple feature maps in this layer, say 3, is each local section (in this example, $$5 \times 5 \times 3$$) of the image looked on by three different neurons and each of their weights trained individually? And what would be the output volume of this layer?

Each feature map (or kernel) is independent of each other. If you had $$3$$ of these filters, your output shape would be $$(28, 28, 3)$$ (given the appropriate amount of padding and stride) with a total of $$75*3=225$$ trainable weights.