[pytorch]搭建神经网络小实战和Sequential的使用

Conv2d

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


class NeuralNode(nn.Module):
    def __init__(self):
        super(NeuralNode, self).__init__()
        # 输出通道数 = 卷积核数
        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(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.linear1(x)
        x = self.linear2(x)
        return x

node = NeuralNode()

print(node)

运行结果:

代码1：

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


class NeuralNode(nn.Module):
    def __init__(self):
        super(NeuralNode, self).__init__()
        # 输出通道数 = 卷积核数
        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(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.linear1(x)
        x = self.linear2(x)
        return x

node = NeuralNode()

print(node)

input = torch.ones((64,3,32,32))

output = node(input)

print(output.shape)

运行结果：

代码2&&使用Sequential：

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


class NeuralNode(nn.Module):
    def __init__(self):
        super(NeuralNode, self).__init__()
        # 输出通道数 = 卷积核数
        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

node = NeuralNode()

print(node)

input = torch.ones((64,3,32,32))

output = node(input)

print(output.shape)

运行结果：

代码3：

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


class NeuralNode(nn.Module):
    def __init__(self):
        super(NeuralNode, self).__init__()
        # 输出通道数 = 卷积核数
        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

node = NeuralNode()

print(node)

input = torch.ones((64,3,32,32))

output = node(input)

print(output.shape)

writer = SummaryWriter("logs_seq")

writer.add_graph(node,input)

writer.close()

运行结果：