小土堆-pytorch-神经网络-搭建小实战和Sequential 09_笔记

sequential

Sequential 是 Keras 中的一种神经网络框架，可以被认为是一个容器，其中封装了神经网络的结构。Sequential 模型只有一组输入和一组输出。各层之间按照先后顺序进行堆叠。前面一层的输出就是后面一次的输入。通过不同层的堆叠，构建出神经网络。
实战实现目标:从inputs到outputs

#bitch_size=64 想象成有64张图片

不使用sequential创建网络,使用正常方法

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

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.conv1=Conv2d(3,32,5,padding=2) #  padding是通过公式计算得来的
        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(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)
        x=self.flatten(x)
        x=self.linear1(x)
        x=self.linear2(x)
        return x

tudui =Tudui()
print(tudui)
input=torch.ones((64,3,32,32))
output=tudui(input)
print(output.shape)

运行结果截图:

使用sequential简化代码

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.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.model1(x)
        return x

tudui =Tudui()
print(tudui)
input=torch.ones((64,3,32,32))
output=tudui(input)
print(output.shape)

# 增加可视化
writer=SummaryWriter("../logs_seq")
writer.add_graph(tudui,input)
writer.close()

运行结果截图: