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 npdevice = 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 xmodel = 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 = 0train_loss = 0train_acc = 0train_correct = 0for 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.Tensorbte1 = type(y)==torch.TensorA = x.deviceB = y.devicepred_y = model(x)loss = criterion(pred_y,y)optimer.zero_grad()loss.backward()optimer.step()loss_val = loss.item()batchsizeNum = batchsizeNum +1train_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 = 0,0for 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_batchtest_correct /= numSizeeval_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：