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前言

介绍如何利用PyTorch中Softmax 分类器实现多分类问题。

一、说明

1.多分类问题的输出是一个分布，满足和为1.
2.Softmax 分类器

3.损失函数：交叉熵损失
torch.nn.CrossEntropyLoss()

二、示例

1.步骤

1.建立模型
2.定义训练函数
3.定义测试函数
4.主函数：定义训练集和测试集，定义损失函数和优化器，进行训练，存储结果，绘图

2.示例代码

代码如下（示例）：

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import pickle
# prepare dataset# batch_size = 64
# transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])  # 归一化,均值和方差
#
# train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)
# train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
# test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)
# test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)# design model using classclass Net(torch.nn.Module):def __init__(self):super(Net, self).__init__()self.l1 = torch.nn.Linear(784, 512)self.l2 = torch.nn.Linear(512, 256)self.l3 = torch.nn.Linear(256, 128)self.l4 = torch.nn.Linear(128, 64)self.l5 = torch.nn.Linear(64, 10)def forward(self, x):x = x.view(-1, 784)  # -1其实就是自动获取mini_batchx = F.relu(self.l1(x))x = F.relu(self.l2(x))x = F.relu(self.l3(x))x = F.relu(self.l4(x))return self.l5(x)  # 最后一层不做激活，不进行非线性变换# model = Net()
#
# # construct loss and optimizer
# criterion = torch.nn.CrossEntropyLoss()
# optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)# training cycle forward, backward, updatedef train(epoch):running_loss = 0.0loss_s = 0.0for batch_idx, data in enumerate(train_loader, 0):# 获得一个批次的数据和标签inputs, target = dataoptimizer.zero_grad()# 获得模型预测结果(64, 10)outputs = model(inputs)# 交叉熵代价函数outputs(64,10),target（64）loss = criterion(outputs, target)loss.backward()optimizer.step()running_loss += loss.item()loss_s += loss.item()if batch_idx % 300 == 299:print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))running_loss = 0.0return loss_s / len(train_loader)def test():correct = 0total = 0with torch.no_grad():for data in test_loader:images, labels = dataoutputs = model(images)_, predicted = torch.max(outputs.data, dim=1)  # dim = 1 列是第0个维度，行是第1个维度total += labels.size(0)correct += (predicted == labels).sum().item()  # 张量之间的比较运算print('accuracy on test set: %d %% ' % (100 * correct / total))return 100 * correct / totalif __name__ == '__main__':batch_size = 64transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])  # 归一化,均值和方差train_dataset = datasets.MNIST(root='../dataset/mnist/', train=True, download=True, transform=transform)train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)test_dataset = datasets.MNIST(root='../dataset/mnist/', train=False, download=True, transform=transform)test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)model = Net()# construct loss and optimizercriterion = torch.nn.CrossEntropyLoss(reduction='mean')optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)epoch_list = []loss_list = []accuracy_list = []for epoch in range(10):epoch_list.append(epoch)loss_lis = train(epoch)loss_list.append(loss_lis)tes = test()accuracy_list.append(tes)with open('8/epoch_list.pkl', 'wb') as f:pickle.dump(epoch_list, f)with open('8/loss_list.pkl', 'wb') as f:pickle.dump(loss_list, f)with open('8/accuracy_list.pkl', 'wb') as f:pickle.dump(accuracy_list, f)

画图程序如下：

import pickle
import matplotlib.pyplot as pltwith open('8/epoch_list.pkl', 'rb') as f:loaded_epoch_list = pickle.load(f)
with open('8/loss_list.pkl', 'rb') as f:loaded_loss_list = pickle.load(f)
with open('8/accuracy_list.pkl', 'rb') as f:loaded_acc_list = pickle.load(f)plt.subplot(2, 1, 1)  # 创建子图，2行1列，第1个子图
plt.plot(loaded_epoch_list, loaded_loss_list)
plt.xlabel('epoch')
plt.ylabel('loss 1')plt.subplot(2, 1, 2)  # 创建子图，2行1列，第2个子图
plt.plot(loaded_epoch_list, loaded_acc_list,'r')
plt.xlabel('epoch')
plt.ylabel('acc 1')
plt.show()

得到如下结果：

总结

PyTorch学习8：多分类问题