pytorch繪制并顯示loss曲線和acc曲線，LeNet5識別圖像準(zhǔn)確率

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): #定義網(wǎng)絡(luò) pytorch定義網(wǎng)絡(luò)有很多方式，推薦以下方式，結(jié)構(gòu)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)#全連接層均使用的nn.Linear()線性結(jié)構(gòu)，輸入輸出維度均為一維，故需要把數(shù)據(jù)拉為一維  
  #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)))

# -*- 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(),#已給定的概率隨即水平翻轉(zhuǎn)給定的PIL圖像
    transforms.ToTensor(),#將圖片轉(zhuǎn)換為Tensor,歸一化至[0,1]
    transforms.Normalize([0.485,0.456,0.406],[0.229, 0.224, 0.225])#用平均值和標(biāo)準(zhǔn)偏差歸一化張量圖像
    ]),
  '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' #樣本文件夾
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)#為了讓圖像更新可以暫停一會
 
#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(參數(shù)梯度為零)
    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()