2-D Tensor calculated by the mean of 3-D Tensor by specific dimension
I have a 3-D tensor, with shape (3000, 20, 5). I want to create a 2-D tensor, of shape (3000, 5), using the mean values of the second dimension of the 3-D tensor.
So basically, I want to perform something like:
mean_value = torch.mean(3d_tensor[0][:][0])

But getting values for all values of dimension one a three. I could do a for loop, e.g.:
for j in range(0, 3d_tensor.size()[2]):
    for i in range(0, len(3d_tensor)):

        mean_values[j][i] = torch.mean(3d_tensor[i][:][j])

But this takes a long time to process for large amounts of data.

You could simply specify axis along which mean should be taken:
mean = torch.mean(tensor, dim=1)

This gives you data of shape (3000, 5)
