thomelane commented on issue #9064: How to perform 2D convolution with user defined kernel just like sobel filter? URL: https://github.com/apache/incubator-mxnet/issues/9064#issuecomment-404347843 With Gluon API you can `set_data` on a parameter. So for a convolutional layer you can select the `weight` (which will be the kernel) and call `set_data` on this with the kernel of your choosing. You must make sure you have the correct shape, with the dimensions being (filter, channel, height, width). So if we wanted a single sobel kernel applied to a single channel input we could for the following: ``` import mxnet as mx # kernel of your choosing (e.g. a sobel filter) kernel = mx.nd.array(((1,0,-1), (2,0,-2), (1,0,-1))) # add dimensions for filters, and input channel # both of which will have shape 1, since single filter and single input channel weight = kernel.expand_dims(0).expand_dims(0) conv = mx.gluon.nn.Conv2D(channels=1, kernel_size=(3,3), padding=(1,1)) conv._in_channels = 1 conv.initialize() conv.weight.set_data(weight) ```
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