import torch
from torch import nn
from d2l import torch as d2l
下面实例化的多层感知机的输入维度是未知的，因此框架尚未初始化任何参数，显示为“UninitializedParameter”。
net = nn.Sequential(nn.LazyLinear(256), nn.ReLU(), nn.LazyLinear(10))
net[0].weight
c:\Software\Miniconda3\envs\d2l\lib\site-packages\torch\nn\modules\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
warnings.warn('Lazy modules are a new feature under heavy development '
<UninitializedParameter>
一旦指定了输入维度，框架就可以一层一层的延迟初始化。
X = torch.rand(2, 20)
net(X)
net[0].weight.shape
torch.Size([256, 20])
练习
（1）如果指定了第一层的输入维度，但没有指定后续层的维度，会发生什么？是否立即进行初始化？
net = nn.Sequential(
nn.Linear(20, 256), nn.ReLU(),
nn.LazyLinear(128), nn.ReLU(),
nn.LazyLinear(10)
)
net[0].weight, net[2].weight, net[4].weight
c:\Software\Miniconda3\envs\d2l\lib\site-packages\torch\nn\modules\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.
warnings.warn('Lazy modules are a new feature under heavy development '
(Parameter containing:
tensor([[ 0.1332, 0.1372, -0.0939, ..., -0.0579, -0.0911, -0.1820],
[-0.1570, -0.0993, -0.0685, ..., -0.0469, -0.0208, 0.0665],
[ 0.0861, 0.1135, 0.1631, ..., -0.1407, 0.1088, -0.2052],
...,
[-0.1454, -0.0283, -0.1074, ..., -0.2164, -0.2169, 0.1913],
[-0.1617, 0.1206, -0.2119, ..., -0.1862, -0.0951, 0.1535],
[-0.0229, -0.2133, -0.1027, ..., 0.1973, 0.1314, 0.1283]],
requires_grad=True),
<UninitializedParameter>,
<UninitializedParameter>)
net(X) # 延迟初始化
net[0].weight.shape, net[2].weight.shape, net[4].weight.shape
(torch.Size([256, 20]), torch.Size([128, 256]), torch.Size([10, 128]))
（2）如果指定了不匹配的维度会发生什么？
X = torch.rand(2, 10)
net(X) # 会报错
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[14], line 2
1 X = torch.rand(2, 10)
----> 2 net(X)
File c:\Software\Miniconda3\envs\d2l\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File c:\Software\Miniconda3\envs\d2l\lib\site-packages\torch\nn\modules\container.py:139, in Sequential.forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input
File c:\Software\Miniconda3\envs\d2l\lib\site-packages\torch\nn\modules\module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File c:\Software\Miniconda3\envs\d2l\lib\site-packages\torch\nn\modules\linear.py:114, in Linear.forward(self, input)
113 def forward(self, input: Tensor) -> Tensor:
--> 114 return F.linear(input, self.weight, self.bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (2x10 and 20x256)
（3）如果输入具有不同的维度，需要做什么？
调整维度，要么填充，要么降维。