Gradient of a Theano custom operator returning a matrix

I have to write a Theano custom operator which return a matrix

class MyOp(tt.Op):
    itypes = [tt.dscalar, tt.dscalar, tt.dscalar]
    otypes = [tt.dmatrix]
    def perform(self, node, inputs, outputs):
        c11, c12, c22 = inputs
        outputs[0][0] = np.array([[c11,c12],[c12,c22]])

Said this, how to implement the grad() method for this operator?
Since the variables are c11, c12 and c22, this method should return the array





Is your grad operation performed on matrix? I am a bit confuse - why not take gradient of each input directly?

well… yes, but how to do this? In general I have problems in defining custom operators for multivariate (gaussians). I think I missed something in the learning process about Theano. For instance see another question Multivariate Gaussian with Custom Operator Mean. I know how to implement custom operator for univariates and they works, but when I come to multivariates (such as the present example) I do not how to do.

In your code above, you dont need to wrap it in a custom operator, as your operation is reshaping the 3 scaler into a matrix, the easiest way to do is something like:

with pm.Model() as m:
    c11 = ...
    c12 = ...
    c22 = ...
    x = tt.zeros((2, 2))
    x = tt.inc_subtensor(x[0, 0], c11)
    x = tt.inc_subtensor(x[0, 1], c12)
    x = tt.inc_subtensor(x[1, 0], c12)
    x = tt.inc_subtensor(x[1, 1], c22)

And theano will take care of the gradient for each scaler.

I will reply to you in Multivariate Gaussian with Custom Operator Mean shortly.