com.thoughtworks.deeplearning.DifferentiableINDArray
calculate the convolution
calculate the convolution
4 dimensions weight
1 dimension bias
the kernel width and height
the stride width and height
the padding width and height
convolution result
[use case]
[use case]
[use case]
[use case]
calculate the convolution
calculate the convolution
4 dimensions weight
1 dimension bias
the kernel width and height
the stride width and height
the padding width and height
convolution result
(iNDArrayLayerOps: INDArrayLayerOps[Input]).convn(weight, bias, kernel, stride, padding)
(iNDArrayLayerOps: INDArrayLayerOps[Input]).dot(right)
(iNDArrayLayerOps: INDArrayLayerOps[Input]).dynamicReshape(newShape)
(iNDArrayLayerOps: INDArrayLayerOps[Input]).im2col(kernel, stride, padding)
(iNDArrayLayerOps: INDArrayLayerOps[Input]).maxPool(dimensions)
(iNDArrayLayerOps: INDArrayLayerOps[Input]).mean
[use case]
(iNDArrayLayerOps: INDArrayLayerOps[Input]).permute(newShape)
[use case]
(iNDArrayLayerOps: INDArrayLayerOps[Input]).permute(newShape)
[use case]
(iNDArrayLayerOps: INDArrayLayerOps[Input]).reshape(newShape)
[use case]
(iNDArrayLayerOps: INDArrayLayerOps[Input]).reshape(newShape)
(iNDArrayLayerOps: INDArrayLayerOps[Input]).shape
(iNDArrayLayerOps: INDArrayLayerOps[Input]).sum(dimensions)
(iNDArrayLayerOps: INDArrayLayerOps[Input]).sum
(iNDArrayLayerOps: INDArrayLayerOps[Input]).toSeq
(iNDArrayLayerOps: INDArrayLayerOps[Input]).unary_-