pytorch实战-6手写数字加法机-迁移学习

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_size=4, shuffle=True, num_workers=4)self.verify_loader = torch.utils.data.DataLoader(self.verify_dataset, batch_size=4, shuffle=True, num_workers=4)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(pretrained=True)# float net valuesnum_features = net.fc.in_featuresnet.fc = nn.Linear(num_features, 2)criterion = nn.CrossEntropyLoss()optimizer = optim.SGD(net.parameters(), lr=0.0001, momentum=0.9)# fixed net values'''for 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, momentum=0.9)'''

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(pretrained=True)net = net.cuda() if use_cuda else net

3.3.4 训练

    def model_prepare(self):net = models.resnet18(pretrained=True)# 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(), lr=0.0001, momentum=0.9)# fixed net values'''for 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, momentum=0.9)'''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(f'verify 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', train=True, transform=transforms.ToTensor(), download=True)self.test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.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(dataset=self.train_dataset, batch_size=self.batch_size, shuffle=False, sampler=sampler_a)self.train_loader_b = torch.utils.data.DataLoader(dataset=self.train_dataset, batch_size=self.batch_size, shuffle=False, sampler=sampler_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(dataset=self.test_dataset, batch_size=self.batch_size, shuffle=False, sampler=verify_sampler_a)self.verify_loader_b = torch.utils.data.DataLoader(dataset=self.test_dataset, batch_size=self.batch_size, shuffle=False, sampler=verify_sampler_b)self.test_loader_a = torch.utils.data.DataLoader(dataset=self.test_dataset, batch_size=self.batch_size, shuffle=False, sampler=test_sampler_a)self.test_loader_b = torch.utils.data.DataLoader(dataset=self.test_dataset, batch_size=self.batch_size, shuffle=False, sampler=test_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, training=True):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, training=self.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, training=self.training)x = self.fc2(x)x = F.log_softmax(x, dim=1)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(), lr=0.0001, momentum=0.9)

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(f'no.{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(f'test accuracy: {right_ratio}')pyplot.show()