这一次学习的时候静态绘制loss和correct曲线，也就是在模型训练完成后，对统计的数据进行绘制。

以minist数据训练为例子

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
import numpy as np


device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')



trainning_data =datasets.MNIST(root="data",train=True,transform=ToTensor(),download=True)
print(len(trainning_data))
test_data = datasets.MNIST(root="data",train=True,transform=ToTensor(),download=False)

train_loader = DataLoader(trainning_data, batch_size=64,shuffle=True)
test_loader = DataLoader(test_data, batch_size=64,shuffle=True)




print(len(train_loader)) #分成了多少个batch
print(len(trainning_data)) #总共多少个图像
# for x, y in train_loader:
#     print(x.shape)
#     print(y.shape)



class MinistNet(nn.Module):
    def __init__(self):
        super().__init__()
        # self.flat = nn.Flatten()
        self.conv1 = nn.Conv2d(1,1,3,1,1)
        self.hideLayer1 = nn.Linear(28*28,256)
        self.hideLayer2 = nn.Linear(256,10)
    def forward(self,x):
        x= self.conv1(x)
        x = x.view(-1,28*28)
        x = self.hideLayer1(x)
        x = torch.sigmoid(x)
        x = self.hideLayer2(x)
        # x = nn.Sigmoid(x)
        return x

model = MinistNet()
model = model.to(device)
cuda = next(model.parameters()).device
print(model)
criterion = nn.CrossEntropyLoss()
optimer = torch.optim.RMSprop(model.parameters(),lr= 0.001)

def train():
    train_losses = []
    train_acces = []
    eval_losses = []
    eval_acces = []
    #训练
    model.train()
    for epoch in range(10):
        batchsizeNum = 0
        train_loss = 0
        train_acc = 0
        train_correct = 0
        for x,y in train_loader:
            # print(epoch)
            # print(x.shape)
            # print(y.shape)
            x = x.to('cuda')
            y = y.to('cuda')
            bte = type(x)==torch.Tensor
            bte1 = type(y)==torch.Tensor
            A = x.device
            B = y.device
            pred_y = model(x)
            loss = criterion(pred_y,y)
            optimer.zero_grad()
            loss.backward()
            optimer.step()
            loss_val = loss.item()
            batchsizeNum = batchsizeNum +1
            train_acc += (pred_y.argmax(1) == y).type(torch.float).sum().item()
            train_loss += loss.item()
            # print("loss: ",loss_val,"  ",epoch, "  ", batchsizeNum)
        train_losses.append(train_loss / len(trainning_data))
        train_acces.append(train_acc / len(trainning_data))

        #测试
        model.eval()
        with torch.no_grad():
            num_batch = len(test_data)
            numSize = len(test_data)
            test_loss, test_correct = 0,0
            for x,y in test_loader:
                x = x.to(device)
                y = y.to(device)
                pred_y = model(x)
                test_loss += criterion(pred_y, y).item()
                test_correct += (pred_y.argmax(1) == y).type(torch.float).sum().item()
            test_loss /= num_batch
            test_correct /= numSize
            eval_losses.append(test_loss)
            eval_acces.append(test_correct)
            print("test result:",100 * test_correct,"%  avg loss:",test_loss)
        PATH = "dict_model_%d_dict.pth"%(epoch)
        torch.save({"epoch": epoch,
                    "model_state_dict": model.state_dict(), }, PATH)


    plt.plot(np.arange(len(train_losses)), train_losses, label="train loss")

    plt.plot(np.arange(len(train_acces)), train_acces, label="train acc")

    plt.plot(np.arange(len(eval_losses)), eval_losses, label="valid loss")

    plt.plot(np.arange(len(eval_acces)), eval_acces, label="valid acc")
    plt.legend()  # 显示图例
    plt.xlabel('epoches')
    # plt.ylabel("epoch")
    plt.title('Model accuracy&loss')
    plt.show()

    torch.save(model,"mode_con_line2.pth")#保存网络模型结构
    # torch.save(model,) #保存模型中的参数
    torch.save(model.state_dict(),"model_dict.pth")






# Press the green button in the gutter to run the script.
if __name__ == '__main__':
    train()

绘制的图如下：