「深度学习一遍过」必修23：基于ResNet18的MNIST手写数字识别

本专栏用于记录关于深度学习的笔记，不光方便自己复习与查阅，同时也希望能给您解决一些关于深度学习的相关问题，并提供一些微不足道的人工神经网络模型设计思路。
专栏地址：「深度学习一遍过」必修篇 

目录

项目 GitHub 地址

项目心得

项目代码

项目 GitHub 地址

Classic_model_examples/2015_ResNet18_MNIST at main · zhao302014/Classic_model_examples · GitHubContribute to zhao302014/Classic_model_examples development by creating an account on GitHub.https://github.com/zhao302014/Classic_model_examples/tree/main/2015_ResNet18_MNIST

项目心得

• 2015 年——ResNet：这是由微软研究院的 Kaiming He 等四名华人提出，通过使用 ResNet Unit 成功训练出了更深层次的神经网络。该项目自己搭建了 ResNet18 网络并在 MNIST 手写数字识别项目中得到了应用。通过此次实践，我终于知道了跳层连接是如何连接的了：ResNet “跳层链接” 的代码体现在相同大小和相同特征图之间用 “+” 相连，而不是 concat。concat 操作常用于 inception 结构中，具体而言是用于特征图大小相同二通道数不同的通道合并中，而看起来简单粗暴的 “+” 连接方式则是用于 ResNet 的 “跳层连接” 结构中，具体而言是用于特征图大小相同且通道数相同的特征图合并。这让我想到一句古诗：“绝知此事要躬行” 啊！

项目代码

下面这张图是网上找的，描述的细节是真的赞！

图片来源：resnet18 50网络结构以及pytorch实现代码 - 简书 

net.py

#!/usr/bin/python
# -*- coding:utf-8 -*-
# ------------------------------------------------- #
#      作者：赵泽荣
#      时间：2021年9月10日（农历八月初四）
#      个人站点：1.https://zhao302014.github.io/
#              2.https://blog.csdn.net/IT_charge/
#      个人GitHub地址：https://github.com/zhao302014
# ------------------------------------------------- #
import torch
import torch.nn as nn
import torch.nn.functional as F# --------------------------------------------------------------------------------- #
#  自己搭建一个 ResNet18 模型结构
#   · 提出时间：2015 年（作者：何凯明）
#   · ResNet 解决了深度 CNN 模型难训练的问题
#   · ResNet 在 2015 名声大噪,而且影响了 2016 年 DL 在学术界和工业界的发展方向
#   · ResNet 网络是参考了 VGG19 网络，在其基础上进行了修改，并通过短路机制加入了残差单元
#   · 变化主要体现在 ResNet 直接使用 stride=2 的卷积做下采样，并且用 global average pool 层替换了全连接层
#   · ResNet 的一个重要设计原则是：当 feature map 大小降低一半时，feature map 的数量增加一倍，这保持了网络层的复杂度
#   · ResNet18 的 18 指定的是带有权重的 18 层，包括卷积层和全连接层，不包括池化层和 BN 层
#   · ResNet “跳层链接” 的代码体现在相同大小和相同特征图之间用 “+” 相连，而不是 concat
# --------------------------------------------------------------------------------- #
class MyResNet18(nn.Module):def __init__(self):super(MyResNet18, self).__init__()# 第一层：卷积层self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=2, padding=3)self.bn1 = nn.BatchNorm2d(64)# Max Pooling 层self.s1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)# 第二、三层：“实线”卷积层self.conv2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)self.bn2 = nn.BatchNorm2d(64)self.conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)self.bn3 = nn.BatchNorm2d(64)# 第四、五层：“实线”卷积层self.conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)self.bn4 = nn.BatchNorm2d(64)self.conv5 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1)self.bn5 = nn.BatchNorm2d(64)# 第六、七层：“虚线”卷积层self.conv6_1 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1)self.bn6_1 = nn.BatchNorm2d(128)self.conv7_1 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1)self.bn7_1 = nn.BatchNorm2d(128)self.conv7 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=1, stride=2, padding=0)self.bn7 = nn.BatchNorm2d(128)# 第八、九层：“实线”卷积层self.conv8 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1)self.bn8 = nn.BatchNorm2d(128)self.conv9 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1)self.bn9 = nn.BatchNorm2d(128)# 第十、十一层：“虚线”卷积层self.conv10_1 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1)self.bn10_1 = nn.BatchNorm2d(256)self.conv11_1 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)self.bn11_1 = nn.BatchNorm2d(256)self.conv11 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=1, stride=2, padding=0)self.bn11 = nn.BatchNorm2d(256)# 第十二 、十三层：“实线”卷积层self.conv12 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)self.bn12 = nn.BatchNorm2d(256)self.conv13 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1)self.bn13 = nn.BatchNorm2d(256)# 第十四、十五层：“虚线”卷积层self.conv14_1 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1)self.bn14_1 = nn.BatchNorm2d(512)self.conv15_1 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)self.bn15_1 = nn.BatchNorm2d(512)self.conv15 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1, stride=2, padding=0)self.bn15 = nn.BatchNorm2d(512)# 第十六 、十七层：“实线”卷积层self.conv16 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)self.bn16 = nn.BatchNorm2d(512)self.conv17 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1)self.bn17 = nn.BatchNorm2d(512)# avg pooling 层self.s2 = nn.AvgPool2d(kernel_size=7, stride=1, padding=0)# 第十八层：全连接层self.Flatten = nn.Flatten()self.f18 = nn.Linear(512, 1000)# 为满足该实例另加 ↓self.f_output = nn.Linear(1000, 10)def forward(self, x):              # shape: torch.Size([1, 3, 224, 224])x = self.conv1(x)              # shape: torch.Size([1, 64, 112, 112])x = self.bn1(x)                # shape: torch.Size([1, 64, 112, 112])x = self.s1(x)                 # shape: torch.Size([1, 64, 56, 56])x = self.conv2(x)              # shape: torch.Size([1, 64, 56, 56])x = self.bn2(x)                # shape: torch.Size([1, 64, 56, 56])x = self.conv3(x)              # shape: torch.Size([1, 64, 56, 56])x = self.bn3(x)                # shape: torch.Size([1, 64, 56, 56])x = self.conv4(x)              # shape: torch.Size([1, 64, 56, 56])x = self.bn4(x)                # shape: torch.Size([1, 64, 56, 56])x = self.conv5(x)              # shape: torch.Size([1, 64, 56, 56])x = self.bn5(x)                # shape: torch.Size([1, 64, 56, 56])x6_1 = self.conv6_1(x)         # shape: torch.Size([1, 128, 28, 28])x7_1 = self.conv7_1(x6_1)      # shape: torch.Size([1, 128, 28, 28])x7 = self.conv7(x)             # shape: torch.Size([1, 128, 28, 28])x = x7 + x7_1                  # shape: torch.Size([1, 128, 28, 28])x = self.conv8(x)              # shape: torch.Size([1, 128, 28, 28])x = self.conv9(x)              # shape: torch.Size([1, 128, 28, 28])x10_1 = self.conv10_1(x)       # shape: torch.Size([1, 256, 14, 14])x11_1 = self.conv11_1(x10_1)   # shape: torch.Size([1, 256, 14, 14])x11 = self.conv11(x)           # shape: torch.Size([1, 256, 14, 14])x = x11 + x11_1                # shape: torch.Size([1, 256, 14, 14])x = self.conv12(x)             # shape: torch.Size([1, 256, 14, 14])x = self.conv13(x)             # shape: torch.Size([1, 256, 14, 14])x14_1 = self.conv14_1(x)       # shape: torch.Size([1, 512, 7, 7])x15_1 = self.conv15_1(x14_1)   # shape: torch.Size([1, 512, 7, 7])x15 = self.conv15(x)           # shape: torch.Size([1, 512, 7, 7])x = x15 + x15_1                # shape: torch.Size([1, 512, 7, 7])x = self.conv16(x)             # shape: torch.Size([1, 512, 7, 7])x = self.conv17(x)             # shape: torch.Size([1, 512, 7, 7])x = self.s2(x)                 # shape: torch.Size([1, 512, 1, 1])x = self.Flatten(x)            # shape: shape: torch.Size([1, 512])x = self.f18(x)                # shape: torch.Size([1, 1000])# 为满足该实例另加 ↓x = self.f_output(x)           # shape: torch.Size([1, 10])x = F.softmax(x, dim=1)        # shape: torch.Size([1, 10])return xif __name__ == '__main__':x = torch.randn(1, 3, 224, 224)model = MyResNet18()y = model(x)

train.py

#!/usr/bin/python
# -*- coding:utf-8 -*-
# ------------------------------------------------- #
#      作者：赵泽荣
#      时间：2021年9月10日（农历八月初四）
#      个人站点：1.https://zhao302014.github.io/
#              2.https://blog.csdn.net/IT_charge/
#      个人GitHub地址：https://github.com/zhao302014
# ------------------------------------------------- #
import torch
from torch import nn
from net import MyResNet18
import numpy as np
from torch.optim import lr_scheduler
from torchvision import datasets, transformsdata_transform = transforms.Compose([transforms.Scale(224),    # 缩放图像大小为 224*224transforms.ToTensor()     # 仅对数据做转换为 tensor 格式操作
])# 加载训练数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=data_transform, download=True)
# 给训练集创建一个数据集加载器
train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=8, shuffle=True)
# 加载测试数据集
test_dataset = datasets.MNIST(root='./data', train=False, transform=data_transform, download=True)
# 给测试集创建一个数据集加载器
test_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=8, shuffle=True)# 如果显卡可用，则用显卡进行训练
device = "cuda" if torch.cuda.is_available() else 'cpu'# 调用 net 里定义的模型，如果 GPU 可用则将模型转到 GPU
model = MyResNet18().to(device)# 定义损失函数（交叉熵损失）
loss_fn = nn.CrossEntropyLoss()
# 定义优化器（SGD：随机梯度下降）
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=0.9)
# 学习率每隔 10 个 epoch 变为原来的 0.1
lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)# 定义训练函数
def train(dataloader, model, loss_fn, optimizer):loss, current, n = 0.0, 0.0, 0for batch, (X, y) in enumerate(dataloader):# 单通道转为三通道X = np.array(X)X = X.transpose((1, 0, 2, 3))             # array 转置image = np.concatenate((X, X, X), axis=0)image = image.transpose((1, 0, 2, 3))     # array 转置回来image = torch.tensor(image)               # 将 numpy 数据格式转为 tensor# 前向传播image, y = image.to(device), y.to(device)output = model(image)cur_loss = loss_fn(output, y)_, pred = torch.max(output, axis=1)cur_acc = torch.sum(y == pred) / output.shape[0]# 反向传播optimizer.zero_grad()cur_loss.backward()optimizer.step()loss += cur_loss.item()current += cur_acc.item()n = n + 1print('train_loss：' + str(loss / n))print('train_acc：' + str(current / n))# 定义测试函数
def test(dataloader, model, loss_fn):# 将模型转换为验证模式model.eval()loss, current, n = 0.0, 0.0, 0# 非训练，推理期用到（测试时模型参数不用更新，所以 no_grad）with torch.no_grad():for batch, (X, y) in enumerate(dataloader):# 单通道转为三通道X = np.array(X)X = X.transpose((1, 0, 2, 3))  # array 转置image = np.concatenate((X, X, X), axis=0)image = image.transpose((1, 0, 2, 3))  # array 转置回来image = torch.tensor(image)  # 将 numpy 数据格式转为 tensorimage, y = image.to(device), y.to(device)output = model(image)cur_loss = loss_fn(output, y)_, pred = torch.max(output, axis=1)cur_acc = torch.sum(y == pred) / output.shape[0]loss += cur_loss.item()current += cur_acc.item()n = n + 1print('test_loss：' + str(loss / n))print('test_acc：' + str(current / n))# 开始训练
epoch = 100
for t in range(epoch):lr_scheduler.step()print(f"Epoch {t + 1}\n----------------------")train(train_dataloader, model, loss_fn, optimizer)test(test_dataloader, model, loss_fn)torch.save(model.state_dict(), "save_model/{}model.pth".format(t))    # 模型保存
print("Done!")

test.py

#!/usr/bin/python
# -*- coding:utf-8 -*-
# ------------------------------------------------- #
#      作者：赵泽荣
#      时间：2021年9月10日（农历八月初四）
#      个人站点：1.https://zhao302014.github.io/
#              2.https://blog.csdn.net/IT_charge/
#      个人GitHub地址：https://github.com/zhao302014
# ------------------------------------------------- #
import torch
from net import MyResNet18
import numpy as np
from torch.autograd import Variable
from torchvision import datasets, transforms
from torchvision.transforms import ToPILImagedata_transform = transforms.Compose([transforms.Scale(224),     # 缩放图像大小为 224*224transforms.ToTensor()      # 仅对数据做转换为 tensor 格式操作
])# 加载训练数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=data_transform, download=True)
# 给训练集创建一个数据集加载器
train_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=8, shuffle=True)
# 加载测试数据集
test_dataset = datasets.MNIST(root='./data', train=False, transform=data_transform, download=True)
# 给测试集创建一个数据集加载器
test_dataloader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=8, shuffle=True)# 如果显卡可用，则用显卡进行训练
device = "cuda" if torch.cuda.is_available() else 'cpu'# 调用 net 里定义的模型，如果 GPU 可用则将模型转到 GPU
model = MyResNet18().to(device)
# 加载 train.py 里训练好的模型
model.load_state_dict(torch.load("./save_model/99model.pth"))# 获取预测结果
classes = ["0","1","2","3","4","5","6","7","8","9",
]# 把 tensor 转成 Image，方便可视化
show = ToPILImage()
# 进入验证阶段
model.eval()
# 对 test_dataset 里 10000 张手写数字图片进行推理
for i in range(len(test_dataset)):x, y = test_dataset[i][0], test_dataset[i][1]# tensor格式数据可视化show(x).show()# 扩展张量维度为 4 维x = Variable(torch.unsqueeze(x, dim=0).float(), requires_grad=False).to(device)# 单通道转为三通道x = x.cpu()x = np.array(x)x = x.transpose((1, 0, 2, 3))          # array 转置x = np.concatenate((x, x, x), axis=0)x = x.transpose((1, 0, 2, 3))      # array 转置回来x = torch.tensor(x).to(device)   # 将 numpy 数据格式转为 tensor，并转回 cuda 格式with torch.no_grad():pred = model(x)# 得到预测类别中最高的那一类，再把最高的这一类对应classes中的哪一个标签predicted, actual = classes[torch.argmax(pred[0])], classes[y]# 最终输出预测值与真实值print(f'Predicted: "{predicted}", Actual: "{actual}"')
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