CIFAR10神经网络搭建

网络搭建

# encoding: utf-8
# @Author: yiyi
# @Date: 2024/09/09

# CIFAR 10结构
# 卷积->最大池化->卷积->最大池化->卷积->最大池化->Flatten->线性

import torch
from sympy.physics.units import nm
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear


class Y1y1(nn.Module):
    def __init__(self):
        super(Y1y1,self).__init__()
        self.conv1 = Conv2d(3, 32,5,padding=2)
        self.maxpool1 = MaxPool2d(2)
        self.conv2 = Conv2d(32, 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(64 * 4 * 4, 64) # 1024 = 64 * 4
        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)
        x = self.linear1(x)
        x = self.linear2(x)
        return x

yiyi = Y1y1()
print(yiyi)
input = torch.ones(64,3,32,32)
print(input.shape)
output = yiyi(input)
print(output.shape)

输出

Y1y1(
  (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(32, 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)
)
torch.Size([64, 3, 32, 32])
torch.Size([64, 10])

使用Sequential优化代码并写入board

# encoding: utf-8
# @Author: yiyi
# @Date: 2024/09/09

# CIFAR 10结构
# 卷积->最大池化->卷积->最大池化->卷积->最大池化->Flatten->线性

import torch
from sympy.physics.units import nm
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.tensorboard import SummaryWriter


class Y1y1(nn.Module):
    def __init__(self):
        super(Y1y1,self).__init__()
        self.model1 = nn.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.model1(x)
        return x

yiyi = Y1y1()
print(yiyi)
input = torch.ones(64,3,32,32)
print(input.shape)
output = yiyi(input)
print(output.shape)

writer = SummaryWriter("./logs_seq")
writer.add_graph(yiyi, input)
writer.close()

输出

Y1y1(
  (model1): Sequential(
    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Flatten(start_dim=1, end_dim=-1)
    (7): Linear(in_features=1024, out_features=64, bias=True)
    (8): Linear(in_features=64, out_features=10, bias=True)
  )
)
torch.Size([64, 3, 32, 32])
torch.Size([64, 10])

导入CIFAR 10示例数据

# encoding: utf-8
# @Author: yiyi
# @Date: 2024/09/09

# CIFAR 10结构
# 卷积->最大池化->卷积->最大池化->卷积->最大池化->Flatten->线性

import torch
import torchvision
from sympy.physics.units import nm
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("./data",train=False,transform=torchvision.transforms.ToTensor(),
                                       download=True)

dataloader = DataLoader(dataset, batch_size= 64,drop_last=True)
class Y1y1(nn.Module):
    def __init__(self):
        super(Y1y1,self).__init__()
        self.model1 = nn.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.model1(x)
        return x

yiyi = Y1y1()
for data in dataloader:
    imgs, targets = data
    output = yiyi(imgs)
    writer = SummaryWriter("./logs_seq")
    writer.add_graph(yiyi, imgs)
    break