Pytorch基本操作（8）——搭建实战、Sequential、损失函数以及优化器

1 前言

在学习李沐在B站发布的《动手学深度学习》PyTorch版本教学视频中发现在操作使用PyTorch方面有许多地方看不懂，往往只是“动手”了，没有动脑。所以打算趁着寒假的时间好好恶补、整理一下PyTorch的操作，以便跟上课程。

学习资源:

• B站up主：我是土堆的视频：PyTorch深度学习快速入门教程（绝对通俗易懂！）【小土堆】
• PyTorch中文手册：(pytorch handbook)
• Datawhale开源内容：深入浅出PyTorch（thorough-pytorch）

2 CIFAR 10 模型结构

2.1 通过Sequential搭建网络

• 根据第一层网络的输入输出和卷积核的大小，来计算所需要的参数，这里指的是padding
  

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

• 试着用Sequential()函数来简化代码，记得每层之间用括号分割

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, 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(1024, 64) # flatten的输出不知道维数的话，可以模拟一个数据跑到这一层看看输出的shape
#         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)
#         x = self.linear1(x)
#         x = self.linear2(x)

        x = self.model1(x)
        return x

tudui = Tudui()
tudui

Tudui(
  (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)
  )
)

'''模拟一个试试网络能不能走通，输入输出能不能对上'''
input = torch.ones([64, 3, 32, 32]) # 模拟一个64批量，3通道，32*32的图片
output = tudui(input)
output.shape

torch.Size([64, 10])

2.2 可视化writer.add_graph()

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

3 损失函数

3.1 L1Loss,MSELoss

import torch
from torch.nn import L1Loss
from torch import nn

inputs = torch.tensor([1, 2, 3], dtype = torch.float32)
targets = torch.tensor([1, 2, 5], dtype = torch.float32)

inputs = torch.reshape(inputs, (1, 1, 1, 3))
targets = torch.reshape(targets, (1, 1, 1, 3))

loss = L1Loss(reduction = 'sum') # 损失相加，默认是算平均
result = loss(inputs, targets)
result

tensor(2.)

loss_mse = nn.MSELoss()
result_mse = loss_mse(inputs, targets)
result_mse

tensor(1.3333)

3.2 交叉熵损失CrossEntropyLoss

• 注意input的形状

x = torch.tensor([0.1, 0.2, 0.3])
y = torch.tensor([1])

x = torch.reshape(x, (1, 3))

loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(x, y)
result_cross

tensor(1.1019)

4 优化器

官方文档

import torchvision
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoader

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

dataloader = DataLoader(dataset, batch_size = 1)

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

Files already downloaded and verified

loss = nn.CrossEntropyLoss()
tudui = Tudui()

optim = torch.optim.SGD(tudui.parameters(), lr = 0.01)

for epoch in range(20):
    running_loss = 0.0
    for data in dataloader:
        imgs, targets = data
        outputs = tudui(imgs)
        result_loss = loss(outputs, targets)

        optim.zero_grad()
        result_loss.backward()
        optim.step()
        
        running_loss = running_loss + result_loss
    print(running_loss)

tensor(18720.8652, grad_fn=)
tensor(16169.7070, grad_fn=)
tensor(15471.6523, grad_fn=)