Cnn on AIBussin — AI applications, systems and books

CNN Geometry: What Shape Reaches the Next Layer?

Fri, 28 Aug 2026 10:15:00 +0100

Here is a batch of 32 RGB images and an ordinary first convolution.

x = torch.randn(32, 224, 224, 3)
conv = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3)
conv(x)

RuntimeError: Given groups=1, weight of size [16, 3, 3, 3],
expected input[32, 224, 224, 3] to have 3 channels, but got 224 channels instead

The images have three channels. PyTorch says there are 224. The message names a number that appears nowhere in the model definition, so the obvious reading is that the layer was declared wrong, and the obvious repair is to declare it right:

PyTorch CNN Shape Errors: Conv2d Output Sizes, Channels, Flatten Bugs and How to Debug Them

Sat, 08 Aug 2026 13:26:00 +0100

PyTorch: Zero to Hero — Step 06

CNN code is usually easy to write.

CNN shape bugs are usually easy to create.

A typical failure looks like this:

RuntimeError: Given groups=1, weight of size [32, 3, 3, 3],
expected input[64, 224, 224, 3] to have 3 channels,
but got 224 channels instead

Or this:

RuntimeError: mat1 and mat2 shapes cannot be multiplied

Or worse: the model runs, but the dimensions are wrong in a way that silently damages the architecture.