python深度学习之多标签分类器及pytorch实现源码
目录
多标签分类器
多标签分类器损失函数
代码实现
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。
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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