tile方法与repeat和repeat_interleave类似，主要用于复制张量。然而，tile在处理复制维度参数小于输入维度的情况时更为灵活。例如，在复制时，可以指定某些维度上的复制次数，而其他维度保持不变。输出 例如，原始张量为[[1, 2], [3, 4]]，若要将列复制两次，而保持行不变，则使用tile方法可以实现这一需求。
torch.tile传入一个元组的话, 表示(行复制次数, 列复制次数) importtorchx=torch.tensor([[1,2],[3,4]])print(x.tile((2,2)))>>>tensor([[1,2,1,2],[3,4,3,4],[1,2,1,2],[3,4,3,4]])print(x.repeat(2,2))>>>tensor([[1,2,1,2],[3,4,3,4],[1,2,1,2],[3,4,3,...
torch.tile函数也是元素复制的一个函数, 但是在传参上和torch.repeat不同,但是也是以input为一个整体进行复制, torch.tile如果只传入一个参数的话, 默认是沿着行进行复制 import torch x = torch.tensor([[1, 2], [3, 4]]) # 只传入一个参数
大多数场景中与repeat相同,但是能够处理复制维度参数小于输入维度的情况 x=torch.LongTensor(range(0,6)).reshape(2,3)print(x)print(x.tile(2))# 与repeat不同,tile不会报错,而是将其转换为x.tile(1,2)print(x.tile(1,2))# 0维复制成1倍,1维复制成2倍print(x.tile(2,2))# 0维复制成2倍,1维...
tile函数用于复制张量，功能类似于repeat，但在参数传递上略有不同。默认情况下，tile会沿行复制张量。若传入元组，表示在指定维度上的复制次数。例如，对于形状为(2, 2, 2)的张量，传入tile中的参数为(2, 2)时，会默认表示为(1, 2, 2)，以行、列、列的顺序进行复制。若传入参数多于张量的维度...
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 ...
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 ...
einops has a minimalistic yet powerful API. Three core operations provided (einops tutorial shows those cover stacking, reshape, transposition, squeeze/unsqueeze, repeat, tile, concatenate, view and numerous reductions) from einops import rearrange, reduce, repeat # rearrange elements according to the ...
API einopshas a minimalistic yet powerful API. Three core operations provided (einops tutorialshows those cover stacking, reshape, transposition, squeeze/unsqueeze, repeat, tile, concatenate, view and numerous reductions) fromeinopsimportrearrange,reduce,repeat# rearrange elements according to the patternou...
# 训练 # X_tile的形状:(n_train,n_train),每一行都包含着相同的训练输入 X_tile = x_train.repeat((n_train, 1)) # Y_tile的形状:(n_train,n_train),每一行都包含着相同的训练输出 Y_tile = y_train.repeat((n_train, 1)) # keys的形状:('n_train','n_train'-1) keys = X_tile[(...