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Comment by hansvm | Hacker News Reader

Concision is a style choice to be used with care. Spending screen space on additional characters and descriptions detracts from the ability to fit more logic on the screen at once and grok the larger flow. Splashing symbolic alphabet soup into your IDE in the name of concision isn't usually a good idea, but naming something "conv" in the immediate local context of a convolutional layer doesn't seem so bad.

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Does 'convo' refer to a 2D convolution or a 1D convolution? Given the large number [1] of arguments that a convolution can take, which ones are being specified? I can probably guess, since only 2 are given, but if there were more, which order would they be in and which would refer to which?

The code is on github [2, 3] see for yourself if you think it's more or less obvious than the python equivalent of a 'trivial' network.

I would say 'conv2d' is probably reasonably standard in meaning; I refer to 'convo' as arbitrary, because it is. Either (ideally) avoid abbreviations, or use standard ones.

[1] - https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.ht...

[1] - https://github.com/uncomplicate/deep-diamond/blob/master/tes...

[2] - https://github.com/uncomplicate/deep-diamond/blob/master/tes...

I know nothing about neural networks, but it looks to me like convo is smart enough to create the convolution of the correct dimension based on the arguments. Where as Keras seem to force you to use a different constructor for different dimensions. That explains why it's called convo, since it creates convolutions of any dimension.

Also, most options are provided as a map to convo as well it looks like, so you'd have similar named arguments for convo once you get to defining optional things like padding and strides.

convo is smart enough to cover 1D, 2D, 3D, and any other convolution layer that the backend can support. You only need to specify the data that it can't figure out, but you can specify more if you want.

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