问题
Just wondering whether anybody has done this? I have a dataset that is one dimensional (not sure whether it's the right word choice though). Unlike the usual CNN inputs which are images (so 2D), my data only has one dimension. An example would be:
instance1 - feature1, feature2,...featureN
instance2 - feature1, feature2,...featureN
...
instanceM - feature1, feature2,...featureN
How do I use my dataset with CNNs? the ones I have looked at accepts images (like AlexNet and GoogleNet) in the form:
instance1 - 2d feature matrix
instance2 - 2d feature matrix2
...
instanceM - 2d feature matrixN
Appreciate any help on it.
Thanks!
回答1:
If your data were spatially related (you said it isn't) then you'd feed it to a convnet (or, specifically, a conv2d layer) with shape 1xNx1 or Nx1x1 (rows x cols x channels).
If this isn't spatial data at all - you just have N non-spatially-related features, then the shape should be 1x1xN.
For completeness, I should point out that if your data really is non-spatial, then there's really no point in using a convolutional layer/net. You could shape it as 1x1xN and then use 1x1 convolutions, but since a 1x1 convolution does the exact same thing as a fully-connected (aka dense aka linear) layer, you might as well just use that instead.
来源：https://stackoverflow.com/questions/40355288/one-dimensional-data-with-cnn