神经网络搭建小实战和sequential的使用————PyTorch

哔哩大学的PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】
的P22讲讲述了神经网络搭建小实战和sequential的使用。

要搭建的模型为：

一开始的代码如下：但是他不能运行，为什么不能？不知道！！！

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential


class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.conv1 = Conv2d(3, 32, 5, padding=2)        # 前三个参数见图片，图中是32*32变成32*32，padding的算法为图片
        self.maxpool1 = MaxPool2d(2)
        self.conv2 = Conv2d(3, 32, 5, padding=2)
        self.maxpool2 = MaxPool2d(2)
        self.conv3 = Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = MaxPool2d(2)
        self.flatten = Flatten()
        self.linear1 = Linear(1024, 64)
        self.linear2 = Linear(64, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flatten(x)
        self.linear1(x)
        self.linear2(x)
        return x

tudui = Tudui()
print(tudui)
input = torch.ones((64, 3, 32, 32))# 创建一个输入，来检查上边网络的正确性
output = tudui(input)
print(output.shape)

报错：

F:Users86133anaconda3envspytorchpython.exe C:/Users/86133/Desktop/pythonProject/shiyan.py
Tudui(
  (conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv2): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
  (maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear1): Linear(in_features=1024, out_features=64, bias=True)
  (linear2): Linear(in_features=64, out_features=10, bias=True)
)
Traceback (most recent call last):
  File "C:/Users/86133/Desktop/pythonProject/shiyan.py", line 34, in 
    output = tudui(input)
  File "F:Users86133anaconda3envspytorchlibsite-packagestorchnnmodulesmodule.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "C:/Users/86133/Desktop/pythonProject/shiyan.py", line 22, in forward
    x = self.conv2(x)
  File "F:Users86133anaconda3envspytorchlibsite-packagestorchnnmodulesmodule.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "F:Users86133anaconda3envspytorchlibsite-packagestorchnnmodulesconv.py", line 443, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "F:Users86133anaconda3envspytorchlibsite-packagestorchnnmodulesconv.py", line 440, in _conv_forward
    self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [32, 3, 5, 5], expected input[64, 32, 16, 16] to have 3 channels, but got 32 channels instead

Process finished with exit code 1

好像看的懂，又不知道咋改，而且和视频中的一摸一样啊！！@！

直到后边有了另一种一样的简单方法，然后可以运行了

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriter


class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        # self.conv1 = Conv2d(3, 32, 5, padding=2)        # 前三个参数见图片，图中是32*32变成32*32，padding的算法为图片
        # self.maxpool1 = MaxPool2d(2)
        # self.conv2 = Conv2d(3, 32, 5, padding=2)
        # self.maxpool2 = MaxPool2d(2)
        # self.conv3 = Conv2d(32, 64, 5, padding=2)
        # self.maxpool3 = MaxPool2d(2)
        # self.flatten = Flatten()
        # self.linear1 = Linear(1024, 64)
        # self.linear2 = Linear(64, 10)

        self.model1 = Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        # x = self.conv1(x)
        # x = self.maxpool1(x)
        # x = self.conv2(x)
        # x = self.maxpool2(x)
        # x = self.conv3(x)
        # x = self.maxpool3(x)
        # x = self.flatten(x)
        # self.linear1(x)
        # self.linear2(x)
        x = self.model1(x)
        return x

tudui = Tudui()
print(tudui)
input = torch.ones((64, 3, 32, 32))# 创建一个输入，来检查上边网络的正确性
output = tudui(input)
print(output.shape)

# 用tensorboard输出
writer = SummaryWriter("logs_seq")
writer.add_graph(tudui, input)
writer.close()

运行结果：

经过我老乡的指点！第一次报错是因为
self.conv2 = Conv2d(3, 32, 5, padding=2)写错了
应该是：
self.conv2 = Conv2d(32, 32, 5, padding=2)
完蛋玩意

还有
self.linear1(x)
self.linear2(x)
改成
x = self.linear1(x)
x = self.linear2(x)