【Pytorch】神经网络搭建小实战和Sequential的使用 - 学习笔记

视频网址
Sequential官方文档，就是把一些层连起来

我们来使用CIFAR数据集写一个简单的神经网络

随便找一个CIFAR10的模型：

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


class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        # 方法1 复杂的写法 一般不用
        # self.conv1 = Conv2d(3, 32, 5, padding=2)  # 这里的padding=2, stride=1是利用公式算出来的
        # 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)

        # 方法2：使用Sequential，一步搞定
        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):
        # 方法1 复杂的写法 一般不用
        # 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)

        # 方法2：使用Sequential，一步搞定
        x = self.model1(x)
        return x


model = Model()
print(model)

# 检查代码正确性
input = torch.ones((64, 3, 32, 32))
output = model(input)
print(output.shape)

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

输出结果为

D:\Anaconda3\envs\pytorch\python.exe D:/研究生/代码尝试/nn_seq.py
Model(
  (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, 10])

打开Tensorboard