在 PyTorch 中展平
请我喝杯咖啡☕
*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]])
以上就是在 PyTorch 中展平的详细内容，更多请关注其它相关文章！