import torch print(torch.__version__) #注意是双下划线

!pip install torch.optim.optimize

import time import torch.optim.optimizer import torchvision from torch import nn from torch.nn import Sequential from torch.utils.data import DataLoader #from mymodel import * from torch.utils.tensorboard import SummaryWriter %load_ext tensorboard

class MyModel(nn.Module): def __init__(self) -> None: super().__init__() #初始化各层 self.model=Sequential( nn.Conv2d(3,32,5,1,2), nn.MaxPool2d(2), nn.Conv2d(32,32,5,1,2), nn.MaxPool2d(2), nn.Conv2d(32,64,5,1,2), nn.MaxPool2d(2), nn.Flatten(), nn.Linear(64*4*4,64), nn.Linear(64,10) ) def forward(self,x): output =self.model(x) return output

start_time=time.time() #开始训练与测试 for i in range(epoch): print("-------------------------第{}轮训练开始-----------------------".format(i + 1)) #训练步骤开始 #设置训练状态 mymodel.train() for data in train_dataloader: imgs, targets = data imgs=imgs.cuda() targets=targets.cuda() outputs = mymodel(imgs) loss = loss_fn(outputs, targets) #优化器优化模型 optimizer.zero_grad() loss.backward() optimizer.step() #打印每一百次的损失值，观察趋势 total_train_step = total_train_step + 1 if total_train_step % 100 == 0: end_time=time.time() print("cost_time is : {}".format(end_time-start_time)) writer.add_scalar("train_loss", loss.item(), total_train_step) print("------------------训练次数：{}，Loss:{}-------------------".format(total_train_step, loss.item())) #测试步骤开始 total_test_loss = 0 total_accuracy = 0 #设置验证状态 mymodel.eval() with torch.no_grad(): for data in test_dataloader: imgs, targets = data imgs=imgs.cuda() targets=targets.cuda() outputs = mymodel(imgs) loss = loss_fn(outputs, targets) total_test_loss = total_test_loss + loss.item() accuracy = (outputs.argmax(1) == targets).sum() total_accuracy = total_accuracy + accuracy writer.add_scalar("test_loss", total_test_loss, total_test_step) writer.add_scalar("test_accuracy", total_accuracy.item()/test_datasize, total_test_step) print("整体测试集上的loss:{}".format(total_test_loss)) print("整体测试集上的正确率：{}".format(total_accuracy.item()/test_datasize)) total_test_step = total_test_step + 1 #保存每一轮训练的参数结果 torch.save(mymodel.state_dict(), "mymodel_{}.pth".format(i + 1)) print("****模型已经保存****")

!tensorboard --logdir cnn_demo_logs #定位tensorboard读取的文件目录

!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip !unzip ngrok-stable-linux-amd64.zip

LOG_DIR = '/content/cnn_demo_logs' get_ipython().system_raw( 'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &' .format(LOG_DIR) )

get_ipython().system_raw('./ngrok http 6006 &')

! curl -s http://localhost:4040/api/tunnels | python3 -c \ "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])"