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So a.view(-1,1) will result in a vector with the dimension 17x1 because there are 17 values - so v.view(1,-1) will result in a 1x17 vector.. . – Yuna Commented Jun 11, 2018 at 7:31
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Betaview (dtype) → Tensor. Returns a new tensor with the same data as the self tensor but of a different dtype.. If the element size of dtype is different than that of self.dtype, then the size of the last dimension of the output will be scaled proportionally.For instance, if dtype element size is twice that of self.dtype, then each pair of elements in the last dimension of self will be combined ...
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BetaThe view(-1) operation flattens the tensor, if it wasn’t already flattened as seen here:. x = torch.randn(2, 3, 4) print(x.shape) > torch.Size([2, 3, 4]) x = x.view(-1) print(x.shape) > torch.Size([24]) It’ll modify the tensor metadata and will not create a copy of it.
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Beta文章浏览阅读5.2w次，点赞159次，收藏342次。本文介绍了PyTorch中.view()方法的使用，包括手动调整Tensor尺寸和使用-1作为参数自动调整尺寸。通过实例展示了.view()如何等效于reshape和resize，以及如何在保持元素总数不变的情况下灵活变换数组形状。
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Betatensor.view(*shape) tensor: The original tensor you want to reshape. shape: The desired shape of the output tensor. This can be a tuple of integers or a series of comma-separated integers representing the new shape. Practical Examples of .view() in PyTorch Example 1: Basic Reshaping. Suppose you have a 2D tensor and want to reshape it into a 1D ...
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BetaWhere: self: The input tensor that you want to reshape. *shape: Either a torch.Size object or a sequence of integers that specify the desired shape of the output tensor. You can also use -1 to infer the size of a dimension from the other dimensions.; However, Tensor.view() only works on contiguous tensors, which are tensors that are stored in contiguous memory.
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BetaThe PyTorch view() function returns a new tensor with a similar number of data and should have a similar number ... [3, 4]]) s.is_contiguous() # c is a view of s. c = s.transpose(0, 1) # View tensors might be non-contiguous. c.is_contiguous() # Here we get a contiguous tensor by calling contiguous() function # And copying the data when c is not ...
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BetaTypically a PyTorch op returns a new tensor as output, e.g. add(). But in case of view ops, outputs are views of input tensors to avoid unnecessary data copy. No data movement occurs when creating a view, view tensor just changes the way it interprets the same data. Taking a view of contiguous tensor could potentially produce a non-contiguous ...
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BetaThe .view() method in PyTorch reshapes a tensor without altering its underlying data, provided the total number of elements remains the same. This method is useful when preparing tensors for operations requiring specific dimensions, such as neural network inputs. Syntax tensor.view(shape) shape: A tuple or list defining the desired dimensions ...
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BetaWhat is the necessary condition to use the PyTorch view() function on a tensor? As long as the number of elements in your original tensor is the same as the number of elements that must be there to form a matrix of the desired shape you can use the view function. For example, we have a tensor of shape, 1 x 16 we can convert it to 4 x 4, 2 x 2 x ...
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