目录
- >- **🍨 本文为[🔗365天深度学习训练营](https://mp.weixin.qq.com/s/xLjALoOD8HPZcH563En8bQ) 中的学习记录博客**
- >- **🍦 参考文章:365天深度学习训练营-第P2周:彩色图片识别(训练营内部成员可读)**
- >- **🍖 原作者:[K同学啊|接辅导、项目定制](https://mtyjkh.blog.csdn.net/)**
- ● 难度:夯实基础⭐⭐
- ● 语言:Python3、Pytorch3
- ● 时间:11月26日-12月2日
- 🍺 要求:
- 1. 自己搭建CNN网络框架
- 2. 调用官方的VGG-16网络框架
-
- 🍻 拔高(可选):
- 1. 验证集准确率达到85%
- 2. 使用PPT画出VGG-16算法框架图
语言环境:Python3.7
编译器:jupyter notebook
深度学习环境:TensorFlow2
- # 设置GPU
- import copy
-
- import torch
- import torch.nn as nn
- import matplotlib.pyplot as plt
- from torchvision import datasets, transforms, models
- import torchvision
- import random
-
- device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
- device
-
- # 导入数据
- train_ds = torchvision.datasets.CIFAR10('data',
- train=True,
- transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor
- download=True)
-
- test_ds = torchvision.datasets.CIFAR10('data',
- train=False,
- transform=torchvision.transforms.ToTensor(), # 将数据类型转化为Tensor
- download=True)
- batch_size = 32
-
- train_dl = torch.utils.data.DataLoader(train_ds,
- batch_size=batch_size,
- shuffle=True)
-
- test_dl = torch.utils.data.DataLoader(test_ds,
- batch_size=batch_size)
- # 取一个批次查看数据格式
- # 数据的shape为:[batch_size, channel, height, weight]
- # 其中batch_size为自己设定,channel,height和weight分别是图片的通道数,高度和宽度。
- imgs, labels = next(iter(train_dl))
- imgs.shape
- import numpy as np
-
- # 指定图片大小,图像大小为20宽、5高的绘图(单位为英寸inch)
- plt.figure(figsize=(20, 5))
- for i, imgs in enumerate(imgs[:20]):
- # 维度缩减
- npimg = imgs.numpy().transpose((1, 2, 0))
- # 将整个figure分成2行10列,绘制第i+1个子图。
- plt.subplot(2, 10, i + 1)
- plt.imshow(npimg, cmap=plt.cm.binary)
- plt.axis('off')
- # 构建CNN网络
- import torch.nn.functional as F
-
- num_classes = 10 # 图片的类别数
-
-
- class Model(nn.Module):
- def __init__(self):
- super().__init__()
- # 特征提取网络
- self.conv1 = nn.Conv2d(3, 64, kernel_size=3) # 第一层卷积,卷积核大小为3*3
- self.pool1 = nn.MaxPool2d(kernel_size=2) # 设置池化层,池化核大小为2*2
- self.conv2 = nn.Conv2d(64, 64, kernel_size=3) # 第二层卷积,卷积核大小为3*3
- self.pool2 = nn.MaxPool2d(kernel_size=2)
- self.conv3 = nn.Conv2d(64, 128, kernel_size=3) # 第二层卷积,卷积核大小为3*3
- self.pool3 = nn.MaxPool2d(kernel_size=2)
-
- # 分类网络
- self.fc1 = nn.Linear(512, 256)
- self.fc2 = nn.Linear(256, num_classes)
-
- # 前向传播
- def forward(self, x):
- x = self.pool1(F.relu(self.conv1(x)))
- x = self.pool2(F.relu(self.conv2(x)))
- x = self.pool3(F.relu(self.conv3(x)))
-
- x = torch.flatten(x, start_dim=1)
-
- x = F.relu(self.fc1(x))
- x = self.fc2(x)
-
- return x
-
-
- from torchinfo import summary
-
- # 将模型转移到GPU中(我们模型运行均在GPU中进行)
- model = Model().to(device)
-
- summary(model)
- # 设置超参数
- loss_fn = nn.CrossEntropyLoss() # 创建损失函数
- learn_rate = 1e-2 # 学习率
- opt = torch.optim.SGD(model.parameters(), lr=learn_rate)
-
-
- # 编写训练函数
- # 训练循环
- def train(dataloader, model, loss_fn, optimizer):
- size = len(dataloader.dataset) # 训练集的大小,一共60000张图片
- num_batches = len(dataloader) # 批次数目,1875(60000/32)
-
- train_loss, train_acc = 0, 0 # 初始化训练损失和正确率
-
- for X, y in dataloader: # 获取图片及其标签
- X, y = X.to(device), y.to(device)
-
- # 计算预测误差
- pred = model(X) # 网络输出
- loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距,targets为真实值,计算二者差值即为损失
-
- # 反向传播
- optimizer.zero_grad() # grad属性归零
- loss.backward() # 反向传播
- optimizer.step() # 每一步自动更新
-
- # 记录acc与loss
- train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()
- train_loss += loss.item()
-
- train_acc /= size
- train_loss /= num_batches
-
- return train_acc, train_loss
-
-
- # 编写测试函数
- def test(dataloader, model, loss_fn):
- size = len(dataloader.dataset) # 测试集的大小,一共10000张图片
- num_batches = len(dataloader) # 批次数目,313(10000/32=312.5,向上取整)
- test_loss, test_acc = 0, 0
-
- # 当不进行训练时,停止梯度更新,节省计算内存消耗
- with torch.no_grad():
- for imgs, target in dataloader:
- imgs, target = imgs.to(device), target.to(device)
-
- # 计算loss
- target_pred = model(imgs)
- loss = loss_fn(target_pred, target)
-
- test_loss += loss.item()
- test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()
-
- test_acc /= size
- test_loss /= num_batches
-
- return test_acc, test_loss
-
-
- epochs = 10
- train_loss = []
- train_acc = []
- test_loss = []
- test_acc = []
-
- for epoch in range(epochs):
- model.train()
- epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)
-
- model.eval()
- epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
- # 保存最优模型
- if epoch_test_acc > best_acc:
- best_acc = epoch_test_acc
- best_model = copy.deepcopy(model)
-
- train_acc.append(epoch_train_acc)
- train_loss.append(epoch_train_loss)
- test_acc.append(epoch_test_acc)
- test_loss.append(epoch_test_loss)
-
- template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%,Test_loss:{:.3f}')
- print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss))
-
- PATH = './best_model.pth '
- torch.save(model.state_dict(), PATH)
- print('Done')
-
- # 训练结果
- import matplotlib.pyplot as plt
- # 隐藏警告
- import warnings
-
- warnings.filterwarnings("ignore") # 忽略警告信息
- plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
- plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
- plt.rcParams['figure.dpi'] = 100 # 分辨率
-
- epochs_range = range(epochs)
-
- plt.figure(figsize=(12, 3))
- plt.subplot(1, 2, 1)
-
- plt.plot(epochs_range, train_acc, label='Training Accuracy')
- plt.plot(epochs_range, test_acc, label='Test Accuracy')
- plt.legend(loc='lower right')
- plt.title('Training and Validation Accuracy')
-
- plt.subplot(1, 2, 2)
- plt.plot(epochs_range, train_loss, label='Training Loss')
- plt.plot(epochs_range, test_loss, label='Test Loss')
- plt.legend(loc='upper right')
- plt.title('Training and Validation Loss')
- plt.show()
-
- plt.figure(figsize=(16, 14))
- for i in range(10):
- img_data, label_id = random.choice(list(zip(test_ds.data, test_ds.targets)))
- img = transforms.ToPILImage()(img_data)
- predict_id = torch.argmax(model(transform(img).to(device).unsqueeze(0)))
- predict = test_ds.classes[predict_id]
- label = test_ds.classes[label_id]
- plt.subplot(3, 4, i + 1)
- plt.imshow(img)
- plt.title(f'truth:{label}\npredict:{predict}')
得到的训练集和测试集的的acc和loss数据可视化,得知预测的结果并不是很满意,所以本文后面会对模型进行改善。
主要的思路就是增加卷积层和池化层 可以在其中加BN层
BN的本质原理:在网络的每一层输入的时候,又插入了一个归一化层,也就是先做一个归一化处理(归一化至:均值0、方差为1),然后再进入网络的下一层。不过文献归一化层,可不像我们想象的那么简单,它是一个可学习、有参数(γ、β)的网络层。
- class Model(nn.Module):
- def __init__(self):
- super(Model, self).__init__()
- self.conv1 = nn.Sequential(
- nn.Conv2d(3, 12, kernel_size=5, padding=0), # 12*220*220
- nn.BatchNorm2d(12),
- nn.ReLU()
- )
- self.conv2 = nn.Sequential(
- nn.Conv2d(12, 12, kernel_size=5, padding=0), # 12*216*216
- nn.BatchNorm2d(12),
- nn.ReLU()
- )
- self.pool3 = nn.Sequential(
- nn.MaxPool2d(2), # 12*108*108
- nn.Dropout(0.15)
- )
- self.conv4 = nn.Sequential(
- nn.Conv2d(12, 24, kernel_size=5, padding=0), # 24*104*104
- nn.BatchNorm2d(24),
- nn.ReLU()
- )
- self.conv5 = nn.Sequential(
- nn.Conv2d(24, 24, kernel_size=5, padding=0), # 24*100*100
- nn.BatchNorm2d(24),
- nn.ReLU()
- )
- self.pool6 = nn.Sequential(
- nn.MaxPool2d(2), # 24*50*50
- nn.Dropout(0.15)
- )
- self.fc = nn.Sequential(
- nn.Linear(24 * 50 * 50, num_classes)
- )
-
- def forward(self, x):
- batch_size = x.size(0)
- x = self.conv1(x) # 卷积-BN-激活
- x = self.conv2(x) # 卷积-BN-激活
- x = self.pool3(x) # 池化-Drop
- x = self.conv4(x) # 卷积-BN-激活
- x = self.conv5(x) # 卷积-BN-激活
- x = self.pool6(x) # 池化-Drop
- x = x.view(batch_size, -1) # flatten 变成全连接网络需要的输入 (batch, 24*50*50) ==> (batch, -1), -1 此处自动算出的是21168
- x = self.fc(x)
-
- return x
模型结构图可以在进行绘制
- class Vgg16_net(nn.Module):
- def __init__(self):
- super(Vgg16_net, self).__init__()
-
- self.layer1 = nn.Sequential(
- nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1), # (32-3+2)/1+1=32 32*32*64
- nn.BatchNorm2d(64),
- # inplace-选择是否进行覆盖运算
- # 意思是是否将计算得到的值覆盖之前的值,比如
- nn.ReLU(inplace=True),
- # 意思就是对从上层网络Conv2d中传递下来的tensor直接进行修改,
- # 这样能够节省运算内存,不用多存储其他变量
-
- nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
- # (32-3+2)/1+1=32 32*32*64
- # Batch Normalization强行将数据拉回到均值为0,方差为1的正太分布上,
- # 一方面使得数据分布一致,另一方面避免梯度消失。
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True),
-
- nn.MaxPool2d(kernel_size=2, stride=2) # (32-2)/2+1=16 16*16*64
- )
-
- self.layer2 = nn.Sequential(
- nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
- # (16-3+2)/1+1=16 16*16*128
- nn.BatchNorm2d(128),
- nn.ReLU(inplace=True),
-
- nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
- # (16-3+2)/1+1=16 16*16*128
- nn.BatchNorm2d(128),
- nn.ReLU(inplace=True),
-
- nn.MaxPool2d(2, 2) # (16-2)/2+1=8 8*8*128
- )
-
- self.layer3 = nn.Sequential(
- nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1), # (8-3+2)/1+1=8 8*8*256
- nn.BatchNorm2d(256),
- nn.ReLU(inplace=True),
-
- nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), # (8-3+2)/1+1=8 8*8*256
- nn.BatchNorm2d(256),
- nn.ReLU(inplace=True),
-
- nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1), # (8-3+2)/1+1=8 8*8*256
- nn.BatchNorm2d(256),
- nn.ReLU(inplace=True),
-
- nn.MaxPool2d(2, 2) # (8-2)/2+1=4 4*4*256
- )
-
- self.layer4 = nn.Sequential(
- nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
- # (4-3+2)/1+1=4 4*4*512
- nn.BatchNorm2d(512),
- nn.ReLU(inplace=True),
-
- nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
- # (4-3+2)/1+1=4 4*4*512
- nn.BatchNorm2d(512),
- nn.ReLU(inplace=True),
-
- nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
- # (4-3+2)/1+1=4 4*4*512
- nn.BatchNorm2d(512),
- nn.ReLU(inplace=True),
-
- nn.MaxPool2d(2, 2) # (4-2)/2+1=2 2*2*512
- )
-
- self.layer5 = nn.Sequential(
- nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
- # (2-3+2)/1+1=2 2*2*512
- nn.BatchNorm2d(512),
- nn.ReLU(inplace=True),
-
- nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
- # (2-3+2)/1+1=2 2*2*512
- nn.BatchNorm2d(512),
- nn.ReLU(inplace=True),
-
- nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
- # (2-3+2)/1+1=2 2*2*512
- nn.BatchNorm2d(512),
- nn.ReLU(inplace=True),
-
- nn.MaxPool2d(2, 2) # (2-2)/2+1=1 1*1*512
- )
-
- self.conv = nn.Sequential(
- self.layer1,
- self.layer2,
- self.layer3,
- self.layer4,
- self.layer5
- )
-
- self.fc = nn.Sequential(
- # y=xA^T+b x是输入,A是权值,b是偏执,y是输出
- # nn.Liner(in_features,out_features,bias)
- # in_features:输入x的列数 输入数据:[batchsize,in_features]
- # out_freatures:线性变换后输出的y的列数,输出数据的大小是:[batchsize,out_features]
- # bias: bool 默认为True
- # 线性变换不改变输入矩阵x的行数,仅改变列数
- nn.Linear(512, 512),
- nn.ReLU(inplace=True),
- nn.Dropout(0.5),
-
- nn.Linear(512, 256),
- nn.ReLU(inplace=True),
- nn.Dropout(0.5),
-
- nn.Linear(256, 10)
- )
-
- def forward(self, x):
- x = self.conv(x)
- # 这里-1表示一个不确定的数,就是你如果不确定你想要reshape成几行,但是你很肯定要reshape成512列
- # 那不确定的地方就可以写成-1
-
- # 如果出现x.size(0)表示的是batchsize的值
- # x=x.view(x.size(0),-1)
- x = x.view(-1, 512)
- x = self.fc(x)
- return x
模型结构图大致如下
可以使用 torchvision.models定义神经网络
- # 使用torchvision.models定义神经网络
- net_a = models.alexnet(num_classes = 10)
- print(net_a)
-
- # 定义loss函数:
- loss_fn = nn.CrossEntropyLoss()
- print(loss_fn)
-
- # 定义优化器
- net = net_a
-
- Learning_rate = 0.01 # 学习率
-
- # optimizer = SGD: 基本梯度下降法
- # parameters:指明要优化的参数列表
- # lr:指明学习率
- # optimizer = torch.optim.Adam(model.parameters(), lr = Learning_rate)
- optimizer = torch.optim.SGD(net.parameters(), lr=Learning_rate, momentum=0.9)
- print(optimizer)
模型结构图
- class ResidualBlock(nn.Module):
- def __init__(self, in_channels, out_channels, stride = 1, shotcut = None):
- super(ResidualBlock, self).__init__()
- self.conv1 = conv3x3(in_channels, out_channels,stride)
- self.bn1 = nn.BatchNorm2d(out_channels)
- self.relu = nn.ReLU(inplace=True)
-
- self.conv2 = conv3x3(out_channels, out_channels)
- self.bn2 = nn.BatchNorm2d(out_channels)
- self.shotcut = shotcut
-
- def forward(self, x):
- residual = x
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
- out = self.conv2(out)
- out = self.bn2(out)
- if self.shotcut:
- residual = self.shotcut(x)
- out += residual
- out = self.relu(out)
- return out
- class ResNet(nn.Module):
- def __init__(self, block, layer, num_classes = 10):
- super(ResNet, self).__init__()
- self.in_channels = 16
- self.conv = conv3x3(3,16)
- self.bn = nn.BatchNorm2d(16)
- self.relu = nn.ReLU(inplace=True)
-
- self.layer1 = self.make_layer(block, 16, layer[0])
- self.layer2 = self.make_layer(block, 32, layer[1], 2)
- self.layer3 = self.make_layer(block, 64, layer[2], 2)
- self.avg_pool = nn.AvgPool2d(8)
- self.fc = nn.Linear(64, num_classes)
-
- def make_layer(self, block, out_channels, blocks, stride = 1):
- shotcut = None
- if(stride != 1) or (self.in_channels != out_channels):
- shotcut = nn.Sequential(
- nn.Conv2d(self.in_channels, out_channels,kernel_size=3,stride = stride,padding=1),
- nn.BatchNorm2d(out_channels))
-
- layers = []
- layers.append(block(self.in_channels, out_channels, stride, shotcut))
-
- for i in range(1, blocks):
- layers.append(block(out_channels, out_channels))
- self.in_channels = out_channels
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- x = self.relu(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.avg_pool(x)
- x = x.view(x.size(0), -1)
- x = self.fc(x)
- return x
模型图转自知乎