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在 PyTorch 中展平
ID:20440 / 打印
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*备忘录：
- 我的帖子解释了 flatten() 和 ravel()。
- 我的帖子解释了 unflatten()。
flatten() 可以通过从零个或多个元素的 0d 或多个 d 张量中选择维度来移除零个或多个维度，得到零个或多个元素的 1d 或多个 d 张量，如下所示：
*备忘录：
- 初始化的第一个参数是 start_dim(optional-default:1-type:int)。
- 初始化的第二个参数是 end_dim（可选-默认：-1-类型：int）。
- 第一个参数是输入（必需类型：int、float、complex 或 bool 的张量）。
- flatten() 可以将 0d 张量更改为 1d 张量。
- flatten() 对于一维张量没有任何作用。
- flatten() 和 flatten() 的区别是：
- flatten() 的 start_dim 默认值为 1，而 flatten() 的 start_dim 默认值为 0。
- 基本上，flatten() 用于定义模型，而 flatten() 不用于定义模型。
import torch from torch import nn flatten = nn.Flatten() flatten # Flatten(start_dim=1, end_dim=-1) flatten.start_dim # 1 flatten.end_dim # -1 my_tensor = torch.tensor(7) flatten = nn.Flatten(start_dim=0, end_dim=0) flatten = nn.Flatten(start_dim=0, end_dim=-1) flatten = nn.Flatten(start_dim=-1, end_dim=0) flatten = nn.Flatten(start_dim=-1, end_dim=-1) flatten(input=my_tensor) # tensor([7]) my_tensor = torch.tensor([7, 1, -8, 3, -6, 0]) flatten = nn.Flatten(start_dim=0, end_dim=0) flatten = nn.Flatten(start_dim=0, end_dim=-1) flatten = nn.Flatten(start_dim=-1, end_dim=0) flatten = nn.Flatten(start_dim=-1, end_dim=-1) flatten(input=my_tensor) # tensor([7, 1, -8, 3, -6, 0]) my_tensor = torch.tensor([[7, 1, -8], [3, -6, 0]]) flatten = nn.Flatten(start_dim=0, end_dim=1) flatten = nn.Flatten(start_dim=0, end_dim=-1) flatten = nn.Flatten(start_dim=-2, end_dim=1) flatten = nn.Flatten(start_dim=-2, end_dim=-1) flatten(input=my_tensor) # tensor([7, 1, -8, 3, -6, 0]) flatten = nn.Flatten() flatten = nn.Flatten(start_dim=0, end_dim=0) flatten = nn.Flatten(start_dim=-1, end_dim=-1) flatten = nn.Flatten(start_dim=0, end_dim=-2) flatten = nn.Flatten(start_dim=1, end_dim=1) flatten = nn.Flatten(start_dim=1, end_dim=-1) flatten = nn.Flatten(start_dim=-1, end_dim=1) flatten = nn.Flatten(start_dim=-1, end_dim=-1) flatten = nn.Flatten(start_dim=-2, end_dim=0) flatten = nn.Flatten(start_dim=-2, end_dim=-2) flatten(input=my_tensor) # tensor([[7, 1, -8], [3, -6, 0]]) my_tensor = torch.tensor([[[7], [1], [-8]], [[3], [-6], [0]]]) flatten = nn.Flatten(start_dim=0, end_dim=2) flatten = nn.Flatten(start_dim=0, end_dim=-1) flatten = nn.Flatten(start_dim=-3, end_dim=2) flatten = nn.Flatten(start_dim=-3, end_dim=-1) flatten(input=my_tensor) # tensor([7, 1, -8, 3, -6, 0]) flatten = nn.Flatten(start_dim=0, end_dim=0) flatten = nn.Flatten(start_dim=0, end_dim=-3) flatten = nn.Flatten(start_dim=1, end_dim=1) flatten = nn.Flatten(start_dim=1, end_dim=-2) flatten = nn.Flatten(start_dim=2, end_dim=2) flatten = nn.Flatten(start_dim=2, end_dim=-1) flatten = nn.Flatten(start_dim=-1, end_dim=2) flatten = nn.Flatten(start_dim=-1, end_dim=-1) flatten = nn.Flatten(start_dim=-2, end_dim=1) flatten = nn.Flatten(start_dim=-2, end_dim=-2) flatten = nn.Flatten(start_dim=-3, end_dim=0) flatten = nn.Flatten(start_dim=-3, end_dim=-3) flatten(input=my_tensor) # tensor([[[7], [1], [-8]], [[3], [-6], [0]]]) flatten = nn.Flatten(start_dim=0, end_dim=1) flatten = nn.Flatten(start_dim=0, end_dim=-2) flatten = nn.Flatten(start_dim=-3, end_dim=1) flatten = nn.Flatten(start_dim=-3, end_dim=-2) flatten(input=my_tensor) # tensor([[7], [1], [-8], [3], [-6], [0]]) flatten = nn.Flatten() flatten = nn.Flatten(start_dim=1, end_dim=2) flatten = nn.Flatten(start_dim=1, end_dim=-1) flatten = nn.Flatten(start_dim=-2, end_dim=2) flatten = nn.Flatten(start_dim=-2, end_dim=-1) flatten(input=my_tensor) # tensor([[7, 1, -8], [3, -6, 0]]) my_tensor = torch.tensor([[[7.], [1.], [-8.]], [[3.], [-6.], [0.]]]) flatten = nn.Flatten() flatten(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]]]) flatten = nn.Flatten() flatten(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]]]) flatten = nn.Flatten() flatten(input=my_tensor) # tensor([[True, False, True], # [False, True, False]])
