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]])

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