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Convolutional neural network: Difference between revisions
→Convolutions
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* \(s\) is the stride | * \(s\) is the stride | ||
Typically a \(3 \times 3\) conv layer will have a padding of 1 and stride of 1 to maintain the same size. A stride of \(2\) would halve the resolution. | Typically a \(3 \times 3\) conv layer will have a padding of 1 and stride of 1 to maintain the same size. A stride of \(2\) would halve the resolution. | ||
===Kernel=== | |||
Typically these days people use small kernels e.g. \(3 \times 3\) or \(4 \times 4\). | |||
Note that in practice, people use multi-channel inputs so the actual kernel will be 3D. | |||
A ''Conv2D'' layer with \(C_1\) input channels and \(C_2\) output channels will have \(C_2\) \(3 \times 3 \times C_1\) kernels. | |||
However, we still call this ''Conv2D'' because the kernel moves in 2D only. | |||
Similarly, a ''Conv3D'' layer will typically have a 4D kernel. | |||
===Stride=== | ===Stride=== | ||