我用的是Anaconda3 ，用spyder编写pytorch的代码，在Anaconda3中新建了一个pytorch的虚拟环境（虚拟环境的名字就叫pytorch)。

以下内容仅供参考哦~~

1.首先打开Anaconda Prompt，然后输入activate pytorch，进入pytorch.

2.输入pip install tensorboardX，安装完成后，输入python，用from tensorboardX import SummaryWriter检验是否安装成功。如下图所示：

3.安装完成之后，先给大家看一下我的文件夹，如下图：

假设用LeNet5框架识别图像的准确率，LeNet.py代码如下：

import torchimport torch.nn as nnfrom torchsummary import summaryfrom torch.autograd import Variableimport torch.nn.functional as F class LeNet5(nn.Module): #定义网络 pytorch定义网络有很多方式，推荐以下方式，结构清晰 def __init__(self): super(LeNet5,self).__init__() self.conv1 = nn.Conv2d(3, 6, 5) self.conv2 = nn.Conv2d(6, 16, 5) self.fc1 = nn.Linear(16*5*5, 120) self.fc2 = nn.Linear(120, 84) self.fc3 = nn.Linear(84, 2) def forward(self,x): # print(x.size()) x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # print(x.size()) x = F.max_pool2d(F.relu(self.conv2(x)), 2) # print(x.size()) x = x.view(x.size()[0], -1)#全连接层均使用的nn.Linear()线性结构，输入输出维度均为一维，故需要把数据拉为一维 #print(x.size()) x = F.relu(self.fc1(x)) # print(x.size()) x = F.relu(self.fc2(x)) #print(x.size()) x = self.fc3(x) # print(x.size()) return x net = LeNet5()data_input = Variable(torch.randn(16,3,32,32))print(data_input.size())net(data_input)print(summary(net,(3,32,32)))

示网络结构如下图：

训练代码（LeNet_train_test.py）如下：

# -*- coding: utf-8 -*-"""Created on Wed Jan 2 15:53:33 2019@author: Administrator""" import torchimport torch.nn as nnimport osimport numpy as npimport matplotlib.pyplot as pltfrom torchvision import datasets,transformsimport torchvisionimport LeNetfrom torch import optimimport timefrom torch.optim import lr_schedulerfrom tensorboardX import SummaryWriter writer = SummaryWriter('LeNet5')data_transforms = { 'train':transforms.Compose([ #transforms.Resize(56), transforms.RandomResizedCrop(32),# transforms.RandomHorizontalFlip(),#已给定的概率随即水平翻转给定的PIL图像 transforms.ToTensor(),#将图片转换为Tensor,归一化至[0,1] transforms.Normalize([0.485,0.456,0.406],[0.229, 0.224, 0.225])#用平均值和标准偏差归一化张量图像 ]), 'val':transforms.Compose([ #transforms.Resize(56), transforms.CenterCrop(32), transforms.ToTensor(), transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225]) ]), }data_dir = 'bees vs ants' #样本文件夹image_datasets = {x:datasets.ImageFolder(os.path.join(data_dir,x), data_transforms[x]) for x in ['train','val'] }dataloaders = {x:torch.utils.data.DataLoader(image_datasets[x],batch_size =16, shuffle = True,num_workers = 0) for x in ['train','val'] }dataset_sizes = {x:len(image_datasets[x]) for x in ['train','val']}class_names = image_datasets['train'].classes device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") def imshow(inp,title = None): #print(inp.size()) inp = inp.numpy().transpose((1,2,0)) mean = np.array([0.485,0.456,0.406]) std = np.array([0.229,0.224,0.225]) inp = std * inp + mean inp = np.clip(inp,0,1) plt.imshow(inp) if title is not None: plt.title(title) plt.pause(0.001)#为了让图像更新可以暂停一会 #Get a batch of training datainputs,classes = next(iter(dataloaders['train']))#print(inputs.size())#print(inputs.size())#Make a grid from batch out = torchvision.utils.make_grid(inputs)#print(out.size())imshow(out,title=[class_names[x] for x in classes]) def train_model(model,criterion,optimizer,scheduler,num_epochs = 25): since = time.time() # best_model_wts = copy.deepcopy(model.state_dict()) best_acc = 0.0 for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch,num_epochs - 1)) print('-' * 10) #Each epoch has a training and validation phase for phase in ['train','val']: if phase == 'train': scheduler.step() model.train() #Set model to training mode else: model.eval() running_loss = 0.0 running_corrects = 0 #Iterate over data for inputs,labels in dataloaders[phase]: inputs = inputs.to(device) # print(inputs.size()) labels = labels.to(device) #print(inputs.size()) # print(labels.size()) #zero the parameter gradients(参数梯度为零) optimizer.zero_grad() #forward #track history if only in train with torch.set_grad_enabled(phase == 'train'): outputs = model(inputs) _,preds = torch.max(outputs,1) loss = criterion(outputs,labels) #backward + optimize only if in training phase if phase == 'train': loss.backward() optimizer.step() #statistics running_loss += loss.item() * inputs.size(0) running_corrects += torch.sum(preds == labels.data) if phase == 'train': epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase,epoch_loss,epoch_acc)) writer.add_scalar('Train/Loss', epoch_loss,epoch) writer.add_scalar('Train/Acc',epoch_acc,epoch) else: epoch_loss = running_loss / dataset_sizes[phase] epoch_acc = running_corrects.double() / dataset_sizes[phase] print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase,epoch_loss,epoch_acc)) writer.add_scalar('Test/Loss', epoch_loss,epoch) writer.add_scalar('Test/Acc',epoch_acc,epoch) if epoch_acc > best_acc: best_acc = epoch_acc print() writer.close() time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60 , time_elapsed % 60)) print('Best val Acc: {:4f}'.format(best_acc)) #load best model weights #model.load_state_dict()#best_model_wts) return model def visualize_model(model,num_images = 6): was_training = model.training model.eval() images_so_far = 0 plt.figure() with torch.no_grad(): for i,(inputs,labels) in enumerate(dataloaders['val']): inputs = inputs.to(device) labels = labels.to(device) outputs = model(inputs) _,preds = torch.max(outputs,1) for j in range(inputs.size()[0]): images_so_far += 1 ax = plt.subplot(num_images //2,2,images_so_far) ax.axis('off') ax.set_title('predicted: {}'.format(class_names[preds[j]])) imshow(inputs.cpu().data[j]) if images_so_far == num_images: model.train(mode = was_training) return model.train(mode=was_training) net = LeNet.LeNet5()net = net.to(device) criterion = nn.CrossEntropyLoss()optimizer = optim.SGD(net.parameters(),lr = 0.001,momentum = 0.9)exp_lr_scheduler = lr_scheduler.StepLR(optimizer,step_size = 7,gamma = 0.1) net = train_model(net,criterion,optimizer,exp_lr_scheduler,num_epochs = 25) #net1 = train_model(net,criterion,optimizer,exp_lr_scheduler,num_epochs = 25)visualize_model(net) plt.ioff()plt.show()

最终的二分类结果为：

样本图像是pytorch官网中介绍迁移学习时用到的，蚂蚁与蜜蜂的二分类图像，图像大小不一。LeNet5 的输入图像是32*32，所以进行分类时会损失一定的图像像素，导致识别率较低。

下面介绍显示loss和acc曲线，在以上训练代码中，writer = SummaryWriter('LeNet5')，表示在训练过程中会生成LeNet5文件夹，保存loss曲线和acc曲线的文件，如下图：

首先解释一下这个文件夹为什么是1，因为我之前训练了很多次，在LeNet5文件夹下有很多1文件夹中这样的文件，待会用Anaconda Prompt来显示loss和acc的时候，它只识别一个文件，所以我就重新建了一个1文件夹，并将刚刚运行完毕的文件放到文件夹中。在LeNet_train_test.py中， writer.add_scalar('Train/Loss', epoch_loss,epoch)和

writer.add_scalar('Train/Acc',epoch_acc,epoch)，这两行代码就是生成train数据集的loss和acc曲线，同理测试数据集亦是如此。

好啦，下面开始显示loss和acc:

1.打开Anaconda Prompt，再次进入pytorch虚拟环境，

2.输入tensorboard --logdir=C:\Users\Administrator\.spyder-py3\python\pytorch\LeNet\LeNet5\1,红色部分是来自上图文件夹的根目录，按回车键，会出现tensorboard的版本和一个网址，总体显示效果如下图：

复制网址到浏览器中，在此处是复制：http://8AEYUVZ5PNOFCBX:6006 到浏览器中，

最终结果如下图：

好啦，以上就是如何显示loss曲线和acc曲线以及LeNet5模型建立及训练的过程啦。

如果，文中有哪些地方描述的不恰当，请大家批评指正，不喜欢也不要喷我，好不好~~~

以上这篇pytorch绘制并显示loss曲线和acc曲线，LeNet5识别图像准确率就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。