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 torch
import torch.nn as nn
from torchsummary import summary
from torch.autograd import Variable
import 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 torch
import torch.nn as nn
import os
import numpy as np
import matplotlib.pyplot as plt
from torchvision import datasets,transforms
import torchvision
import LeNet
from torch import optim
import time
from torch.optim import lr_scheduler
from 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 data
inputs,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()