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 Apr 2022

cs231n.github.io cs231n.github.io

Example 1. For example, suppose that the input volume has size [32x32x3], (e.g. an RGB CIFAR10 image). If the receptive field (or the filter size) is 5x5, then each neuron in the Conv Layer will have weights to a [5x5x3] region in the input volume, for a total of 5*5*3 = 75 weights (and +1 bias parameter). Notice that the extent of the connectivity along the depth axis must be 3, since this is the depth of the input volume. Example 2. Suppose an input volume had size [16x16x20]. Then using an example receptive field size of 3x3, every neuron in the Conv Layer would now have a total of 3*3*20 = 180 connections to the input volume. Notice that, again, the connectivity is local in 2D space (e.g. 3x3), but full along the input depth (20).
These two examples are the first two layers of Andrej Karpathy's wonderful working ConvNetJS CIFAR10 demo here
