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*메모:
UnFlatten()은 0개 이상의 요소로 구성된 1D 이상의 D 텐서에 0개 이상의 차원을 추가하여 아래와 같이 0개 이상의 요소로 구성된 1D 이상의 D 텐서를 얻을 수 있습니다.
*메모:
import torch from torch import nn unflatten = nn.Unflatten() unflatten # Unflatten(dim=0, unflattened_size=(6,)) unflatten.dim # 0 unflatten.unflattened_size # (6,) my_tensor = torch.tensor([7, 1, -8, 3, -6, 0]) unflatten = nn.Unflatten(dim=0, unflattened_size=(6,)) unflatten = nn.Unflatten(dim=0, unflattened_size=(-1,)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(6,)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1,)) unflatten(input=my_tensor) # tensor([7, 1, -8, 3, -6, 0]) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 6)) unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 6)) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, -1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 6)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 6)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, -1)) unflatten(input=my_tensor) # tensor([[7, 1, -8, 3, -6, 0]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(2, 3)) unflatten = nn.Unflatten(dim=0, unflattened_size=(2, -1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(2, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(2, -1)) unflatten(input=my_tensor) # tensor([[7, 1, -8], [3, -6, 0]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(3, 2)) unflatten = nn.Unflatten(dim=0, unflattened_size=(3, -1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, 2)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, -1)) unflatten(input=my_tensor) # tensor([[7, 1], [-8, 3], [-6, 0]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(6, 1)) unflatten = nn.Unflatten(dim=0, unflattened_size=(6, -1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(6, 1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(6, -1)) unflatten(input=my_tensor) # tensor([[7], [1], [-8], [3], [-6], [0]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 2, 3)) unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 2, 3)) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, -1, 3)) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 2, -1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 2, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 2, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, -1, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 2, -1)) unflatten(input=my_tensor) # tensor([[[7, 1, -8], [3, -6, 0]]]) etc my_tensor = torch.tensor([[7, 1, -8], [3, -6, 0]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(2,)) unflatten = nn.Unflatten(dim=0, unflattened_size=(-1,)) unflatten = nn.Unflatten(dim=1, unflattened_size=(3,)) unflatten = nn.Unflatten(dim=1, unflattened_size=(-1,)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(3,)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1,)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(2,)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(-1,)) unflatten(input=my_tensor) # tensor([[7, 1, -8], [3, -6, 0]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 2)) unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 2)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, 2)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(-1, 2)) unflatten(input=my_tensor) # tensor([[[7, 1, -8], [3, -6, 0]]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(2, 1)) unflatten = nn.Unflatten(dim=0, unflattened_size=(2, -1)) unflatten = nn.Unflatten(dim=1, unflattened_size=(1, 3)) unflatten = nn.Unflatten(dim=1, unflattened_size=(-1, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 3)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(2, 1)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(2, -1)) unflatten(input=my_tensor) # tensor([[[7, 1, -8]], [[3, -6, 0]]]) unflatten = nn.Unflatten(dim=1, unflattened_size=(3, 1)) unflatten = nn.Unflatten(dim=1, unflattened_size=(3, -1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, 1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(3, -1)) unflatten(input=my_tensor) # tensor([[[7], [1], [-8]], [[3], [-6], [0]]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 1, 2)) unflatten = nn.Unflatten(dim=0, unflattened_size=(-1, 1, 2)) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, -1, 2)) unflatten = nn.Unflatten(dim=0, unflattened_size=(1, 1, -1)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, 1, 2)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(-1, 1, 2)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, -1, 2)) unflatten = nn.Unflatten(dim=-2, unflattened_size=(1, 1, -1)) unflatten(input=my_tensor) # tensor([[[[7, 1, -8], [3, -6, 0]]]]) unflatten = nn.Unflatten(dim=1, unflattened_size=(1, 1, 3)) unflatten = nn.Unflatten(dim=1, unflattened_size=(-1, 1, 3)) unflatten = nn.Unflatten(dim=1, unflattened_size=(1, -1, 3)) unflatten = nn.Unflatten(dim=1, unflattened_size=(1, 1, -1)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 1, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(-1, 1, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, -1, 3)) unflatten = nn.Unflatten(dim=-1, unflattened_size=(1, 1, -1)) unflatten(input=my_tensor) # tensor([[[[7, 1, -8]]], [[[3, -6, 0]]]]) my_tensor = torch.tensor([[7., 1., -8.], [3., -6., 0.]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(2,)) unflatten(input=my_tensor) # tensor([[7., 1., -8.], [3., -6., 0.]]) my_tensor = torch.tensor([[7.+0.j, 1.+0.j, -8.+0.j], [3.+0.j, -6.+0.j, 0.+0.j]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(2,)) unflatten(input=my_tensor) # tensor([[7.+0.j, 1.+0.j, -8.+0.j], # [3.+0.j, -6.+0.j, 0.+0.j]]) my_tensor = torch.tensor([[True, False, True], [False, True, False]]) unflatten = nn.Unflatten(dim=0, unflattened_size=(2,)) unflatten(input=my_tensor) # tensor([[True, False, True], [False, True, False]])
위 내용은 PyTorch에서 평탄화 해제의 상세 내용입니다. 자세한 내용은 PHP 중국어 웹사이트의 기타 관련 기사를 참조하세요!