这段代码中的损失函数是哪些：import os import torch import torchvision import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import models, datasets, transforms import torch.utils.data as tud import numpy as np from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler from PIL import Image from PIL import Image import matplotlib.pyplot as plt import warnings warnings.filterwarnings("ignore") device = torch.device("cuda:0" if torch.cuda.is_available() else 'cpu') n_classes = 3 # 几种分类的 preteain = False # 是否下载使用训练参数 有网true 没网false epoches = 50 # 训练的轮次 traindataset = datasets.ImageFolder(root='./dataset/train/', transform=transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])) testdataset = datasets.ImageFolder(root='./dataset/test/', transform=transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])) classes = testdataset.classes print(classes) model = models.resnet18(pretrained=preteain) if preteain == True: for param in model.parameters(): param.requires_grad = False model.fc = nn.Linear(in_features=512, out_features=n_classes, bias=True) model = model.to(device) def train_model(model, train_loader, loss_fn, optimizer, epoch): model.train() total_loss = 0. total_corrects = 0. total = 0. for idx, (inputs, labels) in enumerate(train_loader): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) loss = loss_fn(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() preds = outputs.argmax(dim=1) total_corrects += torch.sum(preds.eq(labels)) total_loss += loss.item() * inputs.size(0) total += labels.size(0) total_loss = total_loss / total acc = 100 * total_corrects / total print("轮次:%4d|训练集损失:%.5f|训练集准确率:%6.2f%%" % (epoch + 1, total_loss, acc)) return total_loss, acc def test_model(model, test_loader, loss_fn, optimizer, epoch): model.train() total_loss = 0. total_corrects = 0. total = 0. with torch.no_grad(): for idx, (inputs, labels) in enumerate(test_loader): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) loss = loss_fn(outputs, labels) preds = outputs.argmax(dim=1) total += labels.size(0) total_loss += loss.item() * inputs.size(0) total_corrects += torch.sum(preds.eq(labels)) loss = total_loss / total accuracy = 100 * total_corrects / total print("轮次:%4d|测试集损失:%.5f|测试集准确率:%6.2f%%" % (epoch + 1, loss, accuracy)) return loss, accuracy loss_fn = nn.CrossEntropyLoss().to(device) optimizer = optim.Adam(model.parameters(), lr=0.0001) train_loader = DataLoader(traindataset, batch_size=50, shuffle=True) test_loader = DataLoader(testdataset, batch_size=50, shuffle=True) for epoch in range(0, epoches): loss1, acc1 = train_model(model, train_loader, loss_fn, optimizer, epoch) loss2, acc2 = test_model(model, test_loader, loss_fn, optimizer, epoch) classes = testdataset.classes transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) path = r'D:\PyCharm\untitled\dataset\test\2S1\sample_0.png' # 测试图片路径 model.eval() img = Image.open(path) img_p = transform(img).unsqueeze(0).to(device) output = model(img_p) pred = output.argmax(dim=1).item() plt.imshow(img) plt.show() p = 100 * nn.Softmax(dim=1)(output).detach().cpu().numpy()[0]