# How to select elements in tensor greater than and keep gradient?

X is tensor of dimension (d1,d2,x,x)

how to select elements in the (x,x) if the are greater than a constant c? while keeping the gradient as well ?

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I’m using this way but it is taking the sum of all the 2D matrix instead of what is greater only

number of elements that are greater then zero = x5.view(self.batchsize, -1).ge(0).sum(1).float()
sum of all elements divided by the number of grater than zero = x5.view(self.batchsize, -1).sum(1).div(g)

I want this term. the average of the number greater than zero in dimension 1

Hi,

To get all the elements greater that a value, you can use this: `x[x > my_val]`.

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when I do this part
`x5.view(self.batchsize, -1)`
I’ll have a tensor of size (batchsize,100)
if I apply your version I’ll have a tensor of one dimension.
I still need the sum of the value of the second dimension which means I want to have (batchsize,1)

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you can use torch.gt() or torch.le() . it results in a tensor with the same dimension as the input tensor then you can do what you want.

Hi, but does this operation keep the gradient flowing?

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The gradients for all the elements that were not masked will flow properly. For the masked elements, their gradients will be set to 0.

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