汽车之家网站做的很烂,腾讯云域名购买流程,wordpress 密码保护,云主机重装系统后网站重新部署吗1 概述
迁移学习概念#xff1a;将已经训练好的识别某些信息的网络拿去经过训练识别另外不同类别的信息
优越性#xff1a;提高了训练模型利用率#xff0c;解决了数据缺失的问题#xff08;对于新的预测场景#xff0c;不需要大量的数据#xff0c;只需要少量数据即可…1 概述
迁移学习概念将已经训练好的识别某些信息的网络拿去经过训练识别另外不同类别的信息
优越性提高了训练模型利用率解决了数据缺失的问题对于新的预测场景不需要大量的数据只需要少量数据即可实现训练可用于数据点很少的场景
如何实现将训练好的一个网络拿来和另一个网络连起来去训练即可实现迁移
训练方式按是否改变源网络参数可分两类分别是可改变和不可改变
2 案例 南非贫困预测
2.1 背景
南非存在贫困1990-2021贫困人口从56%下降到43%但下降的贫困人口数量和国际人道主义援助资源并不对应而且大量资金援助一定程度加剧了贫富差距。可以看下具体哪些地区需要援助
2.2 方法
一个方法夜光光亮遥感数据和人类gdp相关性经实验可达0.8-0.9但夜光遥感和贫富没太大相关性夜间光照月亮表示该地区越富有但越安并不表示该地区越贫穷也可能无人居住。
另一个方法光亮遥感数据无法准确预测地区贫穷程度但卫星遥感数据大体可以做到判定依据有街道混乱程度等。如果要用深度网络训练还需要对卫星遥感数据的图片标注贫困程度。非洲能获取到的贫困数据很少但深度网络需要的数据量很大
最终方法用迁移学习将前两种方法合起来见下图 3 案例2
3.1 背景
任务区分图像里动物是蚂蚁还是蜜蜂像素均为224x224
难点只有244个图像样本太少不足训练大型卷积网络准确率只有50%左右
3.2 解决方案
解决方案resnet与模型迁移即用已训练好的物体分类的网络加全连接用来区分蚂蚁与蜜蜂
resnet残差网络对物体分类有较高精度
3.3 代码实现
3.3.1 准备数据
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as pyplot
import time
import copy
import osdata_path pytorch/jizhi/figure_plus/data
image_size 224class TranNet():def __init__(self):super(TranNet, self).__init__()self.train_dataset datasets.ImageFolder(os.path.join(data_path, train), transforms.Compose([transforms.RandomSizedCrop(image_size),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]))self.verify_dataset datasets.ImageFolder(os.path.join(data_path, verify), transforms.Compose([transforms.Scale(256),transforms.CenterCrop(image_size),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]))self.train_loader torch.utils.data.DataLoader(self.train_dataset, batch_size4, shuffleTrue, num_workers4)self.verify_loader torch.utils.data.DataLoader(self.verify_dataset, batch_size4, shuffleTrue, num_workers4)self.num_classes len(self.train_dataset.classes)def exec(self):...def main():TranNet().exec()if __name__ __main__:main()
3.3.2 模型迁移 def exec(self):self.model_prepare()def model_prepare(self):net models.resnet18(pretrainedTrue)# float net valuesnum_features net.fc.in_featuresnet.fc nn.Linear(num_features, 2)criterion nn.CrossEntropyLoss()optimizer optim.SGD(net.parameters(), lr0.0001, momentum0.9)# fixed net valuesfor param in net.parameters():param.requires_grad Falsenum_features net.fc.in_featuresnet.fc nn.Linear(num_features, 2)criterion nn.CrossEntropyLoss()optimizer optim.SGD(net.fc.parameters(), lr 0.001, momentum0.9)
3.3.3 gpu加速
特点gpu速度快但内存低所以尽量减少在gpu中存储的数据只用来计算就好 def model_prepare(self):# jusge whether GPUuse_cuda torch.cuda.is_available()dtype torch.cuda.FloatTensor if use_cuda else torch.FloatTensoritype torch.cuda.LongTensor if use_cuda else torch.LongTensornet models.resnet18(pretrainedTrue)net net.cuda() if use_cuda else net
3.3.4 训练 def model_prepare(self):net models.resnet18(pretrainedTrue)# jusge whether GPUuse_cuda torch.cuda.is_available()if use_cuda:dtype torch.cuda.FloatTensor if use_cuda else torch.FloatTensoritype torch.cuda.LongTensor if use_cuda else torch.LongTensornet net.cuda() if use_cuda else net# float net valuesnum_features net.fc.in_featuresnet.fc nn.Linear(num_features, 2)criterion nn.CrossEntropyLoss()optimizer optim.SGD(net.parameters(), lr0.0001, momentum0.9)# fixed net valuesfor param in net.parameters():param.requires_grad Falsenum_features net.fc.in_featuresnet.fc nn.Linear(num_features, 2)criterion nn.CrossEntropyLoss()optimizer optim.SGD(net.fc.parameters(), lr 0.001, momentum0.9)record []num_epochs 3net.train(True) # open dropoutfor epoch in range(num_epochs):train_rights []train_losses []for batch_index, (data, target) in enumerate(self.train_loader):data, target data.clone().detach().requires_grad_(True), target.clone().detach()output net(data)loss criterion(output, target)optimizer.zero_grad()loss.backward()optimizer.step()right rightness(output, target)train_rights.append(right)train_losses.append(loss.data.numpy())if batch_index % 400 0:verify_rights []for index, (data_v, target_v) in enumerate(self.verify_loader):data_v, target_v data_v.clone().detach(), target_v.clone().detach()output_v net(data_v)right rightness(output_v, target_v)verify_rights.append(right)verify_accu sum([row[0] for row in verify_rights]) / sum([row[1] for row in verify_rights])record.append((verify_accu))print(fverify data accu:{verify_accu})# plotpyplot.figure(figsize(8, 6))pyplot.plot(record)pyplot.xlabel(step)pyplot.ylabel(verify loss)pyplot.show()
4 手写数字加法机
4.1 网络结构
可以先用cnn识别出两个待求和数字不要输出只保留池化层加后面一层全连接层可以获取图像一维特征然后将两个图像识别获取的一维特征合并然后用全连接作为剩下的网络
4.2 代码实现
4.2.1 数据加载
class FigurePlus():def __init__(self):super(FigurePlus, self).__init__()self.image_size 28self.num_classes 10self.num_epochs 3self.batch_size 64self.train_dataset datasets.MNIST(root./data, trainTrue, transformtransforms.ToTensor(), downloadTrue)self.test_dataset datasets.MNIST(root./data, trainFalse, transformtransforms.ToTensor())sampler_a torch.utils.data.sampler.SubsetRandomSampler(np.random.permutation(range(len(self.train_dataset))))sampler_b torch.utils.data.sampler.SubsetRandomSampler(np.random.permutation(range(len(self.train_dataset))))self.train_loader_a torch.utils.data.DataLoader(datasetself.train_dataset, batch_sizeself.batch_size, shuffleFalse, samplersampler_a)self.train_loader_b torch.utils.data.DataLoader(datasetself.train_dataset, batch_sizeself.batch_size, shuffleFalse, samplersampler_b)self.verify_size 5000verify_index_a range(self.verify_size)verify_index_b np.random.permutation(range(self.verify_size))test_index_a range(self.verify_size, len(self.test_dataset))test_index_b np.random.permutation(test_index_a)verify_sampler_a torch.utils.data.sampler.SubsetRandomSampler(verify_index_a)verify_sampler_b torch.utils.data.sampler.SubsetRandomSampler(verify_index_b)test_sampler_a torch.utils.data.sampler.SubsetRandomSampler(test_index_a)test_sampler_b torch.utils.data.sampler.SubsetRandomSampler(test_index_b)self.verify_loader_a torch.utils.data.DataLoader(datasetself.test_dataset, batch_sizeself.batch_size, shuffleFalse, samplerverify_sampler_a)self.verify_loader_b torch.utils.data.DataLoader(datasetself.test_dataset, batch_sizeself.batch_size, shuffleFalse, samplerverify_sampler_b)self.test_loader_a torch.utils.data.DataLoader(datasetself.test_dataset, batch_sizeself.batch_size, shuffleFalse, samplertest_sampler_a)self.test_loader_b torch.utils.data.DataLoader(datasetself.test_dataset, batch_sizeself.batch_size, shuffleFalse, samplertest_sampler_b)def gpu_ok(self):use_cuda torch.cuda.is_available()dtype torch.cuda.FloatTensor if use_cuda else torch.FloatTensoritype torch.cuda.LongTensor if use_cuda else torch.LongTensordef exec(self):passdef main():# TranNet().exec()FigurePlus().exec()if __name__ __main__:main()
4.2.2 手写数字加法机实现网络实现 def forward(self, x, y, trainingTrue):x, y F.relu(self.net1_conv1(x)), F.relu(self.net2_conv1(y))x, y self.net_pool(x), self.net_pool(y)x, y F.relu(self.net1_conv2(x)), F.relu(self.net2_conv2(y))x, y self.net_pool(x), self.net_pool(y)x x.view(-1, (self.image_size // 4) ** 2 * self.depth[1])y y.view(-1, (self.image_size // 4) ** 2 * self.depth[1])z torch.cat((x, y), 1)z self.fc1(z)z F.relu(z)z F.dropout(z, trainingself.training)z F.relu(self.fc2(z))z F.relu(self.fc3(z))return F.relu(self.fc4(z))
4.2.3 模型迁移
思路将上一篇弄好的数字识别模型保存到文件然后在本章节加载进来将各参数权重赋值到本节创的新网络
torch.save(cnn, model_save_path)
将网络加载进来时需要源模型的定义在本章重新定义下拷贝后稍作修改
class FigureIdentify(nn.Module):def __init__(self):super(FigureIdentify, self).__init__()self.depth (4, 8)def forward(self, x):x self.conv1(x)x F.relu(x)x self.pool(x)x self.conv2(x)x F.relu(x)x self.pool(x)x x.view(-1, (image_size // 4)**2 * self.depth[1])x F.relu(self.fc1(x))x F.dropout(x, trainingself.training)x self.fc2(x)x F.log_softmax(x, dim1)return x注意
1 从文件加载模型后只加载网络权重没加载网络的方法需重新定义
2 加载后模型会赋值给一个对象这个对象需要和保存网络时的网络架构保持一致尽量保持一致不然报错
模型文件加载进来后用预训练模式从模型文件加载网络权重作为初始权重参数会随新网络的训练跟随大网络参数调节
注意加法器两个数字识别网络不可直接将文件加载的网络赋值因为会共享地址实际是一组参数一个网络训练后另一个网络的参数也会变可以复制出来再操作 def copy_origin_weight(self, net):self.net1_conv1.weight.data copy.deepcopy(net.conv1.weight.data)self.net1_conv1.bias.data copy.deepcopy(net.conv1.bias.data)self.net1_conv2.weight.data copy.deepcopy(net.conv2.weight.data)self.net1_conv2.bias.data copy.deepcopy(net.conv2.bias.data)self.net2_conv1.weight.data copy.deepcopy(net.conv1.weight.data)self.net2_conv1.bias.data copy.deepcopy(net.conv1.bias.data)self.net2_conv2.weight.data copy.deepcopy(net.conv2.weight.data)self.net2_conv2.bias.data copy.deepcopy(net.conv2.bias.data)def main():# TranNet().exec()net FigurePlusNet()origin_net torch.load(model_save_path)net.copy_origin_weight(origin_net)criterion nn.MSELoss()optmizer optim.SGD(net.parameters(), lr0.0001, momentum0.9)
如要固定值迁移即新模型训练过程不改变加载进来模型的权重设requires_grad False即可 def copy_origin_weight_nograd(self, net):self.copy_origin_weight(net)self.net1_conv1.weight.requires_grad Falseself.net1_conv1.bias.requires_grad Falseself.net1_conv2.weight.requires_grad Falseself.net1_conv2.bias.requires_grad Falseself.net2_conv1.weight.requires_grad Falseself.net2_conv1.bias.requires_grad Falseself.net2_conv2.weight.requires_grad Falseself.net2_conv2.bias.requires_grad False
4.3 训练与测试 # train records []for epoch in range(net.num_epochs):losses []for index, data in enumerate(zip(net.train_loader_a, net.train_loader_b)):(x1, y1), (x2, y2) dataif net.gpu_ok():x1, y1, x2, y2 x1.cuda(), y1.cuda(), x2.cuda(), y2.cuda()optimizer.zero_grad()net.train()outputs net(x1.clone().detach(), x2.clone().detach())outputs outputs.squeeze()labels y1 y2loss criterion(outputs, labels.type(torch.float))loss.backward()optimizer.step()loss loss.cpu() if net.gpu_ok() else losslosses.append(loss.data.numpy())if index % 300 0:verify_losses []rights []net.eval()for verify_data in zip(net.verify_loader_a, net.verify_loader_b):(x1, y1), (x2, y2) verify_dataif net.gpu_ok():x1, y1, x2, y2 x1.cuda(), y1.cuda(), x2.cuda(), y2.cuda()outputs net(x1.clone().detach(), x2.clone().detach())outputs outputs.squeeze()labels y1 y2loss criterion(outputs, labels.type(torch.float))loss loss.cpu() if net.gpu_ok() else lossverify_losses.append(loss.data.numpy())right rightness(outputs.data, labels)rights.append(right)right_ratio 1.0 * np.sum([i[0] for i in rights]) / np.sum([i[1] for i in rights])print(fno.{epoch}, {index}/{len(net.train_loader_a)}, train loss:{np.mean(losses)}, verify loss:{np.mean(verify_losses)}, accu: {right_ratio})# records.append([np.mean(losses), np.mean(verify_losses), right_ratio])records.append([right_ratio])# plot train datapyplot.figure(figsize(8, 6))pyplot.plot(records)pyplot.xlabel(step) pyplot.ylabel(loss accuracy)# testrights []net.eval()for test_data in zip(net.test_loader_a, net.test_loader_b):(x1, y1), (x2, y2) test_dataif net.gpu_ok():x1, y1, x2, y2 x1.cuda(), y1.cuda(), x2.cuda(), y2.cuda()outputs net(x1.clone().detach(), x2.clone().detach())outputs outputs.squeeze()labels y1 y2loss criterion(outputs, labels.type(torch.float))right rightness(outputs, labels)rights.append(right)right_ratio 1.0 * np.sum([i[0] for i in rights]) / np.sum([i[1] for i in rights])print(ftest accuracy: {right_ratio})pyplot.show()
4.4 结果
一轮打3个点总共6轮多了太慢没用gpu总共打大概24个点。发现随着训练轮数增加准确率逐步提高 4.5 大规模测试
4.5.1 大模型
大模型指迁移学习全连接层用4层网络。以数据量为自变量分别看5%50%100%数据量情况下迁移学习与不迁移学习准确率随轮数变化趋势
结论1 数据量100%时迁移学习与无迁移学习准确率趋势很接近数据量较小时迁移学习准确率上升速度会远快于无迁移学习。数据量到50%左右时差异就变得不是很明显了但还是有 2数据量大时固定值训练模式比预训练模式精度更好
4.5.2 小模型
小模型指迁移学习全连接层用2层而不是4层仍然以数据量和是否迁移学习为自变量分析
结论1 数据量小时迁移学习比无迁移学习准确率上升速度快数据量大时迁移学习与无迁移学习这种差异变小
5 总结
适用场景不仅限于这些数据量小
两种迁移方式固定值和预训练固定值方式参数变动范围小训练可能更快
