python深度学习之多标签分类器及pytorch实现源码

多标签分类器

多标签分类任务与多分类任务有所不同，多分类任务是将一个实例分到某个类别中，多标签分类任务是将某个实例分到多个类别中。多标签分类任务有有两大特点：

• 类标数量不确定，有些样本可能只有一个类标，有些样本的类标可能高达几十甚至上百个
• 类标之间相互依赖，例如包含蓝天类标的样本很大概率上包含白云

如下图所示，即为一个多标签分类学习的一个例子，一张图片里有多个类别，房子，树，云等，深度学习模型需要将其一一分类识别出来。

多标签分类器损失函数

代码实现

针对图像的多标签分类器pytorch的简化代码实现如下所示。因为图像的多标签分类器的数据集比较难获取，所以可以通过对mnist数据集中的每个图片打上特定的多标签，例如类别1的多标签可以为[1,1,0,1,0,1,0,0,1]，然后再利用重新打标后的数据集训练出一个mnist的多标签分类器。

from torchvision import datasets, transforms
from torch.utils.data import dataloader, dataset
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as f
import os
class cnn(nn.module):
    def __init__(self):
        super().__init__()
        self.sq1 = nn.sequential(         
            nn.conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2),   # (16, 28, 28)                           #  output: (16, 28, 28)
            nn.relu(),                    
            nn.maxpool2d(kernel_size=2),    # (16, 14, 14)
        )
        self.sq2 = nn.sequential(
            nn.conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2),  # (32, 14, 14)
            nn.relu(),                      
            nn.maxpool2d(2),                # (32, 7, 7)
        )
        self.out = nn.linear(32 * 7 * 7, 100)  
    def forward(self, x):
        x = self.sq1(x)
        x = self.sq2(x)
        x = x.view(x.size(0), -1)    
        x = self.out(x)
        ## sigmoid activation   
        output = f.sigmoid(x)  # 1/(1+e**(-x))
        return output
def loss_fn(pred, target):
    return -(target * torch.log(pred) + (1 - target) * torch.log(1 - pred)).sum()
def multilabel_generate(label):
    y1 = f.one_hot(label, num_classes = 100)
    y2 = f.one_hot(label+10, num_classes = 100)
    y3 = f.one_hot(label+50, num_classes = 100) 	
    multilabel = y1+y2+y3
    return multilabel
        
# def multilabel_generate(label):
# 	multilabel_dict = {}
# 	multi_list = []
# 	for i in range(label.shape[0]):
# 		multi_list.append(multilabel_dict[label[i].item()])
# 	multilabel_tensor = torch.tensor(multi_list)
#     return multilabel
def train():
    epoches = 10
    mnist_net = cnn()
    mnist_net.train()
    opitimizer = optim.sgd(mnist_net.parameters(), lr=0.002)
    mnist_train = datasets.mnist("mnist-data", train=true, download=true, transform=transforms.totensor())
    train_loader = torch.utils.data.dataloader(mnist_train, batch_size= 128, shuffle=true)
    for epoch in range(epoches):
    	loss = 0 
    	for batch_x, batch_y in train_loader:
    		opitimizer.zero_grad()
    		outputs = mnist_net(batch_x)
    		loss = loss_fn(outputs, multilabel_generate(batch_y)) / batch_x.shape[0]
    		loss.backward()
    		opitimizer.step()
    		print(loss)
if __name__ == '__main__':
	train()

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