a=torch.randn(3,2)a,a.repeat(4,2) 输出结果如下:(tensor([[-0.58,-1.21],[-0.35,0.68],[ 0.33,0.70]]),tensor([[-0.58,-1.21,-0.58,-1.21],[-0.35,0.68,-0.35,0.68],[ 0.33,0.70,0.33,0.70],[-0.58,-1.21,-0.58,-1.21],[-0.35,0.68,-0.35,0.68],[ 0.33,0.70,0.33,0.70],[-0.58,-...
a,torch.repeat_interleave(a,3,dim=1) 输出结果如下:(tensor([[-0.81,0.56],[-2.41,-0.56],[ 0.38,-0.90]]),tensor([[-0.81,-0.81,-0.81,0.56,0.56,0.56],[-2.41,-2.41,-2.41,-0.56,-0.56,-0.56],[ 0.38,0.38,0.38,-0.90,-0.90,-0.90]])) 6.5 输入二维张量,指定dim=0,重复次数为一个张量...
torch.repeat() behaves differently from numpy.repeat, but is more similar to numpy.tile. For the operator similar to numpy.repeat, see torch.repeat_interleave(). Parameters sizes (torch.Size or int...) – The number of times to repeat this tensor along each dimension Example: >>> x = ...
torch.repeat()behaves differently fromnumpy.repeat, but is more similar tonumpy.tile. For the operator similar tonumpy.repeat, seetorch.repeat_interleave(). Parameters sizes (torch.Sizeorint...) – The number of times torepeat this tensor along each dimension Example: >>>x=torch.tensor([ 1...
pytorch中的torch.repeat（）函数与numpy.tile（）repeat(*sizes) → Tensor Repeats this tensor along the specified dimensions.Unlike , this function copies the tensor’s data.WARNING torch.repeat() behaves differently from , but is more similar to . For the operator similar to numpy.repeat, see ...
【PyTorch】torch.utils.data.DataLoader 2019-12-09 16:09 −torch.utils.data.DataLoader 简介 DataLoader是PyTorch中的一种数据类型。对数据进行按批读取。 使用Pytorch自定义读取数据时步骤如下:1)创建Dataset对象2)将Dataset对象作为参数传递到Dataloader中 ... ...