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CV09_深度学习模块之间的缝合教学(4)--调参

news 来源：原创 2024/9/20 9:57:18

深度学习就像炼丹。炉子就是模型，火候就是那些参数，材料就是数据集。

1.1 参数有哪些

调参调参，参数到底是哪些参数？

1.网络相关的参数：（1）神经网络网络层

（2）隐藏层的神经元个数等

（3）卷积核的数量

（4）损失层函数的选择

2.数据预处理的相关参数：（1）batch normalization（2）等等

3.超参数：（1）激活函数（2）初始化（凯明初始化等）

（3）梯度下降（SGD、Adam）

(4)epoch (5)batch size

(6)学习率lr （7）衰减函数、正则化等

1.2 常见的情况及原因

1.通常是在网络训练的结果：(1)过拟合----样本数量太少了

（2）欠拟合----样本多但模型简单

（3）拟合，但是在上下浮动（震荡）

（4）恰好拟合

（5）模型不收敛

1.3 解决方法

（1）过拟合----数据增强、早停法、drop out、降低学习率、调整epoch

（2）欠拟合----加深层数、尽量用非线性的激活函数如relu

（3）拟合但震荡----降低数据增强的程度、学习率

（4）

（5）模型不收敛----数据集有问题、网络模型有问题

1.4 调参的过程

1.搭建网络模型

2.先用小样本进行尝试模型效果

3.根据小样本的效果，进行调参，包括分析损失。

1.5 代码（CPU训练）

# Author:SiZhen
# Create: 2024/7/14
# Description: 调参练习-以手写数字集为例import  torch
import torch.nn as nn
import torchvision
import torch.utils
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
#设置超参数
from torch import optim
from torch.nn import initbatch_size = 64
hidden_size = 128
learning_rate = 0.001
num_epoch = 10#将图片进行预处理转换成pytorch张量
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,),)]) #mnist是单通道，所以括号内只有一个0.5#下载训练集
train_set = torchvision.datasets.MNIST(root="./data",train=True,download=True,transform=transform)#下载测试集
test_set = torchvision.datasets.MNIST(root="./data",train=False,download=True,transform=transform)#加载数据集
train_loader= torch.utils.data.DataLoader(train_set,batch_size=batch_size,shuffle=True)test_loader = torch.utils.data.DataLoader(test_set,batch_size=batch_size,shuffle=False)#测试不shuffleinput_size = 784 #mnist,28x28像素
num_classes = 10class SEAttention(nn.Module):# 初始化SE模块，channel为通道数，reduction为降维比率def __init__(self, channel=1, reduction=8):super().__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)  # 自适应平均池化层，将特征图的空间维度压缩为1x1self.fc = nn.Sequential(  # 定义两个全连接层作为激励操作，通过降维和升维调整通道重要性nn.Linear(channel, channel // reduction, bias=False),  # 降维，减少参数数量和计算量nn.ReLU(inplace=True),  # ReLU激活函数，引入非线性nn.Linear(channel // reduction, channel, bias=False),  # 升维，恢复到原始通道数nn.Sigmoid(),  # Sigmoid激活函数，输出每个通道的重要性系数)# 权重初始化方法def init_weights(self):for m in self.modules():  # 遍历模块中的所有子模块if isinstance(m, nn.Conv2d):  # 对于卷积层init.kaiming_normal_(m.weight, mode='fan_out')  # 使用Kaiming初始化方法初始化权重if m.bias is not None:init.constant_(m.bias, 0)  # 如果有偏置项，则初始化为0elif isinstance(m, nn.BatchNorm2d):  # 对于批归一化层init.constant_(m.weight, 1)  # 权重初始化为1init.constant_(m.bias, 0)  # 偏置初始化为0elif isinstance(m, nn.Linear):  # 对于全连接层init.normal_(m.weight, std=0.001)  # 权重使用正态分布初始化if m.bias is not None:init.constant_(m.bias, 0)  # 偏置初始化为0# 前向传播方法def forward(self, x):b, c, _, _ = x.size()  # 获取输入x的批量大小b和通道数cy = self.avg_pool(x).view(b, c)  # 通过自适应平均池化层后，调整形状以匹配全连接层的输入y = self.fc(y).view(b, c, 1, 1)  # 通过全连接层计算通道重要性，调整形状以匹配原始特征图的形状return x * y.expand_as(x)  # 将通道重要性系数应用到原始特征图上，进行特征重新校准
import torch
import torch.nn as nn
from torch.nn import Softmax# 定义一个无限小的矩阵，用于在注意力矩阵中屏蔽特定位置
def INF(B, H, W):return -torch.diag(torch.tensor(float("inf")).repeat(H), 0).unsqueeze(0).repeat(B * W, 1, 1)class CrissCrossAttention(nn.Module):""" Criss-Cross Attention Module"""def __init__(self, in_dim):super(CrissCrossAttention, self).__init__()# Q, K, V转换层self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)# 使用softmax对注意力分数进行归一化self.softmax = Softmax(dim=3)self.INF = INF# 学习一个缩放参数，用于调节注意力的影响self.gamma = nn.Parameter(torch.zeros(1))def forward(self, x):m_batchsize, _, height, width = x.size()# 计算查询(Q)、键(K)、值(V)矩阵proj_query = self.query_conv(x)proj_query_H = proj_query.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height).permute(0, 2, 1)proj_query_W = proj_query.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width).permute(0, 2, 1)proj_key = self.key_conv(x)proj_key_H = proj_key.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height)proj_key_W = proj_key.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width)proj_value = self.value_conv(x)proj_value_H = proj_value.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height)proj_value_W = proj_value.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width)# 计算垂直和水平方向上的注意力分数，并应用无穷小掩码屏蔽自注意energy_H = (torch.bmm(proj_query_H, proj_key_H) + self.INF(m_batchsize, height, width)).view(m_batchsize, width, height, height).permute(0, 2, 1, 3)energy_W = torch.bmm(proj_query_W, proj_key_W).view(m_batchsize, height, width, width)# 在垂直和水平方向上应用softmax归一化concate = self.softmax(torch.cat([energy_H, energy_W], 3))# 分离垂直和水平方向上的注意力，应用到值(V)矩阵上att_H = concate[:, :, :, 0:height].permute(0, 2, 1, 3).contiguous().view(m_batchsize * width, height, height)att_W = concate[:, :, :, height:height + width].contiguous().view(m_batchsize * height, width, width)# 计算最终的输出，加上输入x以应用残差连接out_H = torch.bmm(proj_value_H, att_H.permute(0, 2, 1)).view(m_batchsize, width, -1, height).permute(0, 2, 3, 1)out_W = torch.bmm(proj_value_W, att_W.permute(0, 2, 1)).view(m_batchsize, height, -1, width).permute(0, 2, 1, 3)return self.gamma * (out_H + out_W) + xclass Net(nn.Module):def __init__(self, input_size, hidden_size, num_classes):super(Net, self).__init__()self.fc1 = nn.Linear(input_size, hidden_size)self.relu = nn.ReLU()self.fc2 = nn.Linear(hidden_size, num_classes)self.conv1 =nn.Conv2d(1,64,kernel_size=1)self.se = SEAttention(channel=1)self.cca = CrissCrossAttention(64)self.conv2 = nn.Conv2d(64,1,kernel_size=1)def forward(self, x):x = self.se(x)x = self.conv1(x)x = self.cca(x)x = self.conv2(x)out = self.fc1(x.view(-1, input_size))out = self.relu(out)out = self.fc2(out)return outmodel = Net(input_size,hidden_size,num_classes)
criterion = nn.CrossEntropyLoss()optimizer = optim.Adam(model.parameters(),lr=learning_rate)train_loss_list = []
test_loss_list = []#训练
total_step = len(train_loader)
for epoch in range(num_epoch):for i,(images,labels) in enumerate(train_loader):outputs = model(images) #获取模型分类后的结果loss = criterion(outputs,labels) #计算损失optimizer.zero_grad() #反向传播前，梯度清零loss.backward() #反向传播optimizer.step() #更新参数train_loss_list.append(loss.item())if (i+1)%100 ==0:print('Epoch[{}/{}],Step[{}/{}],Train Loss:{:.4f}'.format(epoch+1,num_epoch,i+1,total_step,loss.item()))model.eval()with torch.no_grad(): #禁止梯度计算test_loss = 0.0for images,labels in test_loader:outputs = model(images)loss = criterion(outputs,labels)test_loss +=loss.item()*images.size(0) #累加每个批次总损失得到总损失test_loss /=len(test_loader.dataset) #整个测试集的平均损失# 将计算得到的单个平均测试损失值扩展到一个列表中，长度为total_step# 这样做可能是为了在绘图时每个step都有一个对应的测试损失值，尽管实际测试损失在整个epoch内是恒定的test_loss_list.extend([test_loss]*total_step) #方便可视化model.train()print("Epoch[{}/{}],Test Loss:{:.4f}".format(epoch+1,num_epoch,test_loss))plt.plot(train_loss_list,label='Train Loss')
plt.plot(test_loss_list,label='Test Loss')
plt.title('model loss')
plt.xlabel('iterations')
plt.ylabel('Loss')
plt.legend()
plt.show()

1.6 代码（GPU训练）

要想模型在GPU上训练，需要两点：

（1）模型在GPU上

（2）所有参与运算的张量在GPU上

# Author:SiZhen
# Create: 2024/7/14
# Description: 调参练习-以手写数字集为例import  torch
import torch.nn as nn
import torchvision
import torch.utils
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
#设置超参数
from torch import optim
from torch.nn import initdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")batch_size = 64
hidden_size = 128
learning_rate = 0.001
num_epoch = 10#将图片进行预处理转换成pytorch张量
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,),(0.5,),)]) #mnist是单通道，所以括号内只有一个0.5#下载训练集
train_set = torchvision.datasets.MNIST(root="./data",train=True,download=True,transform=transform)#下载测试集
test_set = torchvision.datasets.MNIST(root="./data",train=False,download=True,transform=transform)#加载数据集
train_loader= torch.utils.data.DataLoader(train_set,batch_size=batch_size,shuffle=True,pin_memory=True)test_loader = torch.utils.data.DataLoader(test_set,batch_size=batch_size,shuffle=False,pin_memory=True)#测试不shuffleinput_size = 784 #mnist,28x28像素
num_classes = 10class SEAttention(nn.Module):# 初始化SE模块，channel为通道数，reduction为降维比率def __init__(self, channel=1, reduction=8):super().__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)  # 自适应平均池化层，将特征图的空间维度压缩为1x1self.fc = nn.Sequential(  # 定义两个全连接层作为激励操作，通过降维和升维调整通道重要性nn.Linear(channel, channel // reduction, bias=False),  # 降维，减少参数数量和计算量nn.ReLU(inplace=True),  # ReLU激活函数，引入非线性nn.Linear(channel // reduction, channel, bias=False),  # 升维，恢复到原始通道数nn.Sigmoid(),  # Sigmoid激活函数，输出每个通道的重要性系数)# 权重初始化方法def init_weights(self):for m in self.modules():  # 遍历模块中的所有子模块if isinstance(m, nn.Conv2d):  # 对于卷积层init.kaiming_normal_(m.weight, mode='fan_out')  # 使用Kaiming初始化方法初始化权重if m.bias is not None:init.constant_(m.bias, 0)  # 如果有偏置项，则初始化为0elif isinstance(m, nn.BatchNorm2d):  # 对于批归一化层init.constant_(m.weight, 1)  # 权重初始化为1init.constant_(m.bias, 0)  # 偏置初始化为0elif isinstance(m, nn.Linear):  # 对于全连接层init.normal_(m.weight, std=0.001)  # 权重使用正态分布初始化if m.bias is not None:init.constant_(m.bias, 0)  # 偏置初始化为0# 前向传播方法def forward(self, x):b, c, _, _ = x.size()  # 获取输入x的批量大小b和通道数cy = self.avg_pool(x).view(b, c)  # 通过自适应平均池化层后，调整形状以匹配全连接层的输入y = self.fc(y).view(b, c, 1, 1)  # 通过全连接层计算通道重要性，调整形状以匹配原始特征图的形状return x * y.expand_as(x)  # 将通道重要性系数应用到原始特征图上，进行特征重新校准
import torch
import torch.nn as nn
from torch.nn import Softmax# 定义一个无限小的矩阵，用于在注意力矩阵中屏蔽特定位置
def INF(B, H, W):return -torch.diag(torch.tensor(float("inf")).repeat(H), 0).unsqueeze(0).repeat(B * W, 1, 1)class CrissCrossAttention(nn.Module):""" Criss-Cross Attention Module"""def __init__(self, in_dim):super(CrissCrossAttention, self).__init__()# Q, K, V转换层self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 8, kernel_size=1)self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)# 使用softmax对注意力分数进行归一化self.softmax = Softmax(dim=3)self.INF = INF# 学习一个缩放参数，用于调节注意力的影响self.gamma = nn.Parameter(torch.zeros(1))def forward(self, x):m_batchsize, _, height, width = x.size()# 计算查询(Q)、键(K)、值(V)矩阵proj_query = self.query_conv(x)proj_query_H = proj_query.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height).permute(0, 2, 1)proj_query_W = proj_query.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width).permute(0, 2, 1)proj_key = self.key_conv(x)proj_key_H = proj_key.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height)proj_key_W = proj_key.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width)proj_value = self.value_conv(x)proj_value_H = proj_value.permute(0, 3, 1, 2).contiguous().view(m_batchsize * width, -1, height)proj_value_W = proj_value.permute(0, 2, 1, 3).contiguous().view(m_batchsize * height, -1, width)# 计算垂直和水平方向上的注意力分数，并应用无穷小掩码屏蔽自注意energy_H = (torch.bmm(proj_query_H, proj_key_H)+ self.INF(m_batchsize, height, width).to(device)).view(m_batchsize, width, height, height).permute(0, 2, 1, 3).to(device)energy_W = torch.bmm(proj_query_W, proj_key_W).view(m_batchsize, height, width, width)# 在垂直和水平方向上应用softmax归一化concate = self.softmax(torch.cat([energy_H, energy_W], 3))# 分离垂直和水平方向上的注意力，应用到值(V)矩阵上att_H = concate[:, :, :, 0:height].permute(0, 2, 1, 3).contiguous().view(m_batchsize * width, height, height)att_W = concate[:, :, :, height:height + width].contiguous().view(m_batchsize * height, width, width)# 计算最终的输出，加上输入x以应用残差连接out_H = torch.bmm(proj_value_H, att_H.permute(0, 2, 1)).view(m_batchsize, width, -1, height).permute(0, 2, 3, 1)out_W = torch.bmm(proj_value_W, att_W.permute(0, 2, 1)).view(m_batchsize, height, -1, width).permute(0, 2, 1, 3)return self.gamma * (out_H + out_W) + xclass Net(nn.Module):def __init__(self, input_size, hidden_size, num_classes):super(Net, self).__init__()self.fc1 = nn.Linear(input_size, hidden_size)self.relu = nn.ReLU()self.fc2 = nn.Linear(hidden_size, num_classes)self.conv1 =nn.Conv2d(1,64,kernel_size=1)self.se = SEAttention(channel=1)self.cca = CrissCrossAttention(64)self.conv2 = nn.Conv2d(64,1,kernel_size=1)def forward(self, x):x = self.se(x)x = self.conv1(x)x = self.cca(x)x = self.conv2(x)out = self.fc1(x.view(-1, input_size))out = self.relu(out)out = self.fc2(out)return outmodel = Net(input_size,hidden_size,num_classes)model.to(device)criterion = nn.CrossEntropyLoss().to(device)optimizer = optim.Adam(model.parameters(),lr=learning_rate)
train_loss_list = []
test_loss_list = []#训练
total_step = len(train_loader)
for epoch in range(num_epoch):for i,(images,labels) in enumerate(train_loader):images,labels = images.to(device),labels.to(device)outputs = model(images).to(device) #获取模型分类后的结果loss = criterion(outputs,labels).to(device) #计算损失optimizer.zero_grad() #反向传播前，梯度清零loss.backward() #反向传播optimizer.step() #更新参数train_loss_list.append(loss.item())if (i+1)%100 ==0:print('Epoch[{}/{}],Step[{}/{}],Train Loss:{:.4f}'.format(epoch+1,num_epoch,i+1,total_step,loss.item()))model.eval()with torch.no_grad(): #禁止梯度计算test_loss = 0.0for images,labels in test_loader:images, labels = images.to(device), labels.to(device)outputs = model(images).to(device)loss = criterion(outputs,labels).to(device)test_loss +=loss.item()*images.size(0) #累加每个批次总损失得到总损失test_loss /=len(test_loader.dataset) #整个测试集的平均损失# 将计算得到的单个平均测试损失值扩展到一个列表中，长度为total_step# 这样做可能是为了在绘图时每个step都有一个对应的测试损失值，尽管实际测试损失在整个epoch内是恒定的test_loss_list.extend([test_loss]*total_step) #方便可视化model.train()print("Epoch[{}/{}],Test Loss:{:.4f}".format(epoch+1,num_epoch,test_loss))plt.plot(train_loss_list,label='Train Loss')
plt.plot(test_loss_list,label='Test Loss')
plt.title('model loss')
plt.xlabel('iterations')
plt.ylabel('Loss')
plt.legend()
plt.show()