机器学习-线性层（pytorch环境）

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一个例子：

import torch
import torchvision.datasets
from torch import nn
from torch.nn import ReLU, Sigmoid, Linear
from torch.nn import Conv2d, MaxPool2d, ReLU
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset_transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])

dataset = torchvision.datasets.CIFAR10(root='./dataset', train=False, transform=dataset_transform, download=True)


dataLoader = DataLoader(dataset=dataset, batch_size=64, shuffle=True, num_workers=0, drop_last=True)

class TuDui(nn.Module):
    def __init__(self):
        super(TuDui, self).__init__()
        self.linear = Linear(196608,10)


    def forward(self,input):
        output = self.linear(input)
        return output

tudui = TuDui()

for data in dataLoader:
    imgs, targets = data
    print(imgs.shape)
    output = torch.flatten(imgs)
    print(output.shape)
    output = tudui(output)
    print(output.shape)

class Linear(Module):
    r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`

    This module supports :ref:`TensorFloat32<tf32_on_ampere>`.

    Args:
        in_features: size of each input sample
        out_features: size of each output sample
        bias: If set to ``False``, the layer will not learn an additive bias.
            Default: ``True``

    Shape:
        - Input: :math:`(N, *, H_{in})` where :math:`*` means any number of
          additional dimensions and :math:`H_{in} = \text{in\_features}`
        - Output: :math:`(N, *, H_{out})` where all but the last dimension
          are the same shape as the input and :math:`H_{out} = \text{out\_features}`.

    Attributes:
        weight: the learnable weights of the module of shape
            :math:`(\text{out\_features}, \text{in\_features})`. The values are
            initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where
            :math:`k = \frac{1}{\text{in\_features}}`
        bias:   the learnable bias of the module of shape :math:`(\text{out\_features})`.
                If :attr:`bias` is ``True``, the values are initialized from
                :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where
                :math:`k = \frac{1}{\text{in\_features}}`

    Examples::

        >>> m = nn.Linear(20, 30)
        >>> input = torch.randn(128, 20)
        >>> output = m(input)
        >>> print(output.size())
        torch.Size([128, 30])
    """