李宏毅2023机器学习作业1--homework1——模型创建 一、导入包 import torch # pytorch import torch.nn as nn from torch.utils.data import Dataset, DataLoader, random_split 二、配置项 方便更新超参数，对模型进行参数调整 device = 'cuda' if torch.cuda.is_available() else 'cpu' config = { 'seed': 5201314, # Your seed number, you can pick your lucky number. :) 'select_all': False, # Whether to use all features. 'valid_ratio': 0.2, # validation_size = train_size * valid_ratio 'n_epochs': 5000, # Number of epochs. 'batch_size': 256, 'learning_rate': 1e-5, 'early_stop': 600, # If model has not improved for this many consecutive epochs, stop training. 'save_path': './models/model.ckpt' # Your model will be saved here. } 三、创建神经网络模型 class My_Model(nn.Module): # 搭建的神经网络 Model继承了 Module类（父类） def __init__(self, input_dim): # 初始化函数 super(My_Model, self).__init__() # 必须要这一步，调用父类的初始化函数 # TODO: modify model's structure, be aware of dimensions. self.layers = nn.Sequential( nn.Linear(input_dim, 16), nn.ReLU(), nn.Linear(16, 8), nn.ReLU(), nn.Linear(8, 1) ) def forward(self, x): # 前向传播（为输入和输出中间的处理过程），x为输入 x = self.layers(x) x = x.squeeze(1) # (B, 1) -> (B) return x 四、模型训练过程 def trainer(train_loader, valid_loader, model, config, device): criterion = nn.MSELoss(reduction='mean') # Define your loss function, do not modify this. # Define your optimization algorithm. # TODO: Please check https://pytorch.org/docs/stable/optim.html to get more available algorithms. # TODO: L2 regularization (optimizer(weight decay...) or implement by your self). optimizer = torch.optim.SGD(model.parameters(), lr=config['learning_rate'], momentum=0.9) # math.inf为无限大 n_epochs, best_loss, step, early_stop_count = config['n_epochs'], math.inf, 0, 0 for epoch in range(n_epochs): model.train() # Set your model to train mode. loss_record = [] # 记录损失 for x, y in train_loader: optimizer.zero_grad() # Set gradient to zero. 梯度清0 x, y = x.to(device), y.to(device) # Move your data to device. pred = model(x) # 数据传入模型model，生成预测值pred loss = criterion(pred, y) # 预测值pred和真实值y计算损失loss loss.backward() # Compute gradient(backpropagation). optimizer.step() # Update parameters. step += 1 loss_record.append(loss.detach().item()) # 当前步骤的loss加到loss_record[] # Display current epoch number and loss on tqdm progress bar. train_pbar.set_description(f'Epoch [{epoch+1}/{n_epochs}]') train_pbar.set_postfix({'loss': loss.detach().item()}) mean_train_loss = sum(loss_record)/len(loss_record) # 计算训练集上平均损失 writer.add_scalar('Loss/train', mean_train_loss, step) model.eval() # Set your model to evaluation mode. loss_record = [] for x, y in valid_loader: x, y = x.to(device), y.to(device) with torch.no_grad(): pred = model(x) loss = criterion(pred, y) loss_record.append(loss.item()) mean_valid_loss = sum(loss_record)/len(loss_record) # 计算验证集上平均损失 print(f'Epoch [{epoch+1}/{n_epochs}]: Train loss: {mean_train_loss:.4f}, Valid loss: {mean_valid_loss:.4f}') writer.add_scalar('Loss/valid', mean_valid_loss, step) # 保存验证集上平均损失最小的模型 if mean_valid_loss < best_loss: best_loss = mean_valid_loss torch.save(model.state_dict(), config['save_path']) # Save your best model print('Saving model with loss {:.3f}...'.format(best_loss)) early_stop_count = 0 else: early_stop_count += 1 # 设置早停early_stop_count # 如果early_stop_count次数，验证集上的平均损失没有变化，模型性能没有提升，停止训练 if early_stop_count >= config['early_stop']: print('\nModel is not improving, so we halt the training session.') return 五、训练模型 # 创建模型model，将模型和数据放到相同的计算设备上 model = My_Model(input_dim=x_train.shape[1]).to(device) # 开始训练 trainer(train_loader, valid_loader, model, config, device) 六、模型测试过程 # 测试数据集的预测 def predict(test_loader, model, device): model.eval() # Set your model to evaluation mode. preds = [] for x in tqdm(test_loader): x = x.to(device) with torch.no_grad(): # 关闭梯度 pred = model(x) preds.append(pred.detach().cpu()) preds = torch.cat(preds, dim=0).numpy() return preds 七、测试模型 def save_pred(preds, file): ''' Save predictions to specified file ''' with open(file, 'w') as fp: writer = csv.writer(fp) writer.writerow(['id', 'tested_positive']) for i, p in enumerate(preds): writer.writerow([i, p]) model = My_Model(input_dim=x_train.shape[1]).to(device) model.load_state_dict(torch.load(config['save_path'])) # 加载模型 preds = predict(test_loader, model, device) # 生成预测结果preds save_pred(preds, 'pred.csv') # 保存preds到pred.csv