4/19/2019 · self.expand_as(other) is equivalent to self.expand(other.size()). And the .expand() operation involves some broadcasting semantics here. I think you could not “expand” a large size tensor to a smaller one. So the code snippet above will not work. If.
pytorch code: att_h = att_h.unsqueeze(1).expand_as(att) att_h shape is (10,512) att shape is (10,196,512) Keras code: K.expand_dims(att_h, 1).expand_as(att) Got error …
8/20/2017 · I think this is a bug for function expand_as() since expand_as() works correctly on pytorch 0.1.2 but incorrectly on pytorch ‘0.2.0_1’ System: Ubuntu 16.04 64-bit Python 2.7 pytorch ‘0.2.0_1 A toy example on latest pytorch 0.2.0_1 In [39…
class torch.Tensor¶. There are a few main ways to create a tensor, depending on your use case. To create a tensor with pre-existing data, use torch.tensor().. To create a tensor with specific size, use torch.* tensor creation ops (see Creation Ops).. To create a tensor with the same size (and similar types) as another tensor, use torch.*_like tensor creation ops (see Creation Ops).
Extending torch.autograd ¶. Adding operations to autograd requires implementing a new Function subclass for each operation. Recall that Function s are what autograd uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods: forward() – the code that performs the operation. It can take as many arguments as.
8/12/2018 · Issue description torch.Tensor with 3 dimensions cannot be expanded with expand_as . Code example 1 dimension >>> torch.zeros(1). expand_as (torch.zeros(8)) tensor([0 …
8/26/2018 · * tox.ini -> .flake8 (#15065) Summary: We were only using this file to configure flake8, and fbcode linters do not recognize tox.ini which causes spurious linter warnings.
pytorch memory leak in expand_as . GitHub Gist: instantly share code, notes, and snippets.
10/24/2018 · Hi, Backprop-wise, they will give the exact same result. The difference is that if the original dimension you want to expand is of size 1, you can use torch.expand() to do it without using extra memory. If the dimension you want to expand is of size more than 1, then you actually want to repeat what is at that dimension and you should use torch.repeat().
Pytorch ???loss??????????? ????????????? ? Pytorch ? tensor . expand ()? tensor . expand_as ()?? sinat_36197913???
