深度学习实战：手把手教你构建多任务、多标签模型-编程学习网

在本文中,我们将基于流行的 MovieLens 数据集,使用稀疏特征来创建一个多任务多标签模型,并逐步介绍整个过程。所以本文将涵盖数据准备、模型构建、训练循环、模型诊断,最后使用 Ray Serve 部署模型的全部流程。

1.设置环境

在深入代码之前,请确保安装了必要的库(以下不是详尽列表)：

pip install pandas scikit-learn torch ray[serve] matplotlib requests tensorboard

我们在这里使用的数据集足够小,所以可以使用 CPU 进行训练。

2.准备数据集

我们将从创建用于处理 MovieLens 数据集的下载、预处理的类开始,然后将数据分割为训练集和测试集。

MovieLens数据集包含有关用户、电影及其评分的信息,我们将用它来预测评分(回归任务)和用户是否喜欢这部电影(二元分类任务)。

import os  
 import pandas as pd  
 from sklearn.model_selection import train_test_split  
 from sklearn.preprocessing import LabelEncoder  
 import torch  
 from torch.utils.data import Dataset, DataLoader  
 import zipfile  
 import io  
 import requests  
   
 class MovieLensDataset(Dataset):  
   
     def __init__(self, dataset_version="small", data_dir="data"):  
         print("Initializing MovieLensDataset...")  
         if not os.path.exists(data_dir):  
             os.makedirs(data_dir)  
           
         if dataset_version == "small":  
             url = "https://files.grouplens.org/datasets/movielens/ml-latest-small.zip"  
             local_zip_path = os.path.join(data_dir, "ml-latest-small.zip")  
             file_path = 'ml-latest-small/ratings.csv'  
             parquet_path = os.path.join(data_dir, "ml-latest-small.parquet")  
         elif dataset_version == "full":  
             url = "https://files.grouplens.org/datasets/movielens/ml-latest.zip"  
             local_zip_path = os.path.join(data_dir, "ml-latest.zip")  
             file_path = 'ml-latest/ratings.csv'  
             parquet_path = os.path.join(data_dir, "ml-latest.parquet")  
         else:  
             raise ValueError("Invalid dataset_version. Choose 'small' or 'full'.")  
           
         if os.path.exists(parquet_path):  
             print(f"Loading dataset from {parquet_path}...")  
             movielens = pd.read_parquet(parquet_path)  
         else:  
             if not os.path.exists(local_zip_path):  
                 print(f"Downloading {dataset_version} dataset from {url}...")  
                 response = requests.get(url)  
                 with open(local_zip_path, "wb") as f:  
                     f.write(response.content)  
               
             with zipfile.ZipFile(local_zip_path, "r") as z:  
                 with z.open(file_path) as f:  
                     movielens = pd.read_csv(f, usecols=['userId', 'movieId', 'rating'], low_memory=True)  
             movielens.to_parquet(parquet_path, index=False)  
         movielens['liked'] = (movielens['rating'] >= 4).astype(int)  
         self.user_encoder = LabelEncoder()  
         self.movie_encoder = LabelEncoder()  
         movielens['user'] = self.user_encoder.fit_transform(movielens['userId'])  
         movielens['movie'] = self.movie_encoder.fit_transform(movielens['movieId'])  
         self.train_df, self.test_df = train_test_split(movielens, test_size=0.2, random_state=42)  
       
     def get_data(self, split="train"):  
         if split == "train":  
             data = self.train_df  
         elif split == "test":  
             data = self.test_df  
         else:  
             raise ValueError("Invalid split. Choose 'train' or 'test'.")  
           
         dense_features = torch.tensor(data[['user', 'movie']].values, dtype=torch.long)  
         labels = torch.tensor(data[['rating', 'liked']].values, dtype=torch.float32)  
           
         return dense_features, labels  
       
     def get_encoders(self):  
         return self.user_encoder, self.movie_encoder

定义了 MovieLensDataset,就可以将训练集和评估集加载到内存中

# Example usage with a single dataset object  
 print("Creating MovieLens dataset...")  
 # Feel free to use dataset_version="full" if you are using  
 # a GPU  
 dataset = MovieLensDataset(dataset_version="small")  
   
 print("Getting training data...")  
 train_dense_features, train_labels = dataset.get_data(split="train")  
 print("Getting testing data...")  
 test_dense_features, test_labels = dataset.get_data(split="test")  
 # Create DataLoader for training and testing  
 train_loader = DataLoader(torch.utils.data.TensorDataset(train_dense_features, train_labels), batch_size=64, shuffle=True)  
 test_loader = DataLoader(torch.utils.data.TensorDataset(test_dense_features, test_labels), batch_size=64, shuffle=False)  
 print("Accessing encoders...")  
 user_encoder, movie_encoder = dataset.get_encoders()  
 print("Setup complete.")

3.定义多任务多标签模型

我们将定义一个基本的 PyTorch 模型,处理两个任务:预测评分(回归)和用户是否喜欢这部电影(二元分类)。

模型使用稀疏嵌入来表示用户和电影,并有共享层,这些共享层会输入到两个单独的输出层。

通过在任务之间共享一些层,并为每个特定任务的输出设置单独的层,该模型利用了共享表示,同时仍然针对每个任务定制其预测。

from torch import nn  
   
 class MultiTaskMovieLensModel(nn.Module):  
     def __init__(self, n_users, n_movies, embedding_size, hidden_size):  
         super(MultiTaskMovieLensModel, self).__init__()  
         self.user_embedding = nn.Embedding(n_users, embedding_size)  
         self.movie_embedding = nn.Embedding(n_movies, embedding_size)  
         self.shared_layer = nn.Linear(embedding_size * 2, hidden_size)  
         self.shared_activation = nn.ReLU()  
         self.task1_fc = nn.Linear(hidden_size, 1)  
         self.task2_fc = nn.Linear(hidden_size, 1)  
         self.task2_activation = nn.Sigmoid()  
   
     def forward(self, x):  
         user = x[:, 0]  
         movie = x[:, 1]  
         user_embed = self.user_embedding(user)  
         movie_embed = self.movie_embedding(movie)  
         combined = torch.cat((user_embed, movie_embed), dim=1)  
         shared_out = self.shared_activation(self.shared_layer(combined))  
         rating_out = self.task1_fc(shared_out)  
         liked_out = self.task2_fc(shared_out)  
         liked_out = self.task2_activation(liked_out)  
         return rating_out, liked_out

输入 (x)：

输入 x 预期是一个 2D 张量,其中每行包含一个用户 ID 和一个电影 ID。

用户和电影嵌入：

user = x[:, 0]: 从第一列提取用户 ID。
movie = x[:, 1]: 从第二列提取电影 ID。
user_embed 和 movie_embed 是对应这些 ID 的嵌入。

连接：

combined = torch.cat((user_embed, movie_embed), dim=1): 沿特征维度连接用户和电影嵌入。

共享层：

shared_out = self.shared_activation(self.shared_layer(combined)): 将组合的嵌入通过共享的全连接层和激活函数。

任务特定输出：

rating_out = self.task1_fc(shared_out): 从第一个任务特定层输出预测评分。
liked_out = self.task2_fc(shared_out): 输出用户是否喜欢电影的原始分数。
liked_out = self.task2_activation(liked_out): 原始分数通过 sigmoid 函数转换为概率。

模型返回两个输出:

rating_out：预测的评分(回归输出)。
liked_out：用户喜欢电影的概率(分类输出)。

4.训练循环

首先,用一些任意选择的超参数(嵌入维度和隐藏层中的神经元数量)实例化我们的模型。对于回归任务将使用均方误差损失,对于分类任务,将使用二元交叉熵。

我们可以通过它们的初始值来归一化两个损失,以确保它们都大致处于相似的尺度(这里也可以使用不确定性加权来归一化损失)

然后将使用数据加载器训练模型,并跟踪两个任务的损失。损失将被绘制成图表,以可视化模型在评估集上随时间的学习和泛化情况。

import torch.optim as optim  
 import matplotlib.pyplot as plt  
   
 # Check if GPU is available  
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
 print(f"Using device: {device}")  
 embedding_size = 16  
 hidden_size = 32  
 n_users = len(dataset.get_encoders()[0].classes_)  
 n_movies = len(dataset.get_encoders()[1].classes_)  
 model = MultiTaskMovieLensModel(n_users, n_movies, embedding_size, hidden_size).to(device)  
 criterion_rating = nn.MSELoss()  
 criterion_liked = nn.BCELoss()  
 optimizer = optim.Adam(model.parameters(), lr=0.001)  
 train_rating_losses, train_liked_losses = [], []  
 eval_rating_losses, eval_liked_losses = [], []  
 epochs = 10  
   
 # used for loss normalization  
 initial_loss_rating = None  
 initial_loss_liked = None  
   
 for epoch in range(epochs):  
     model.train()  
     running_loss_rating = 0.0  
     running_loss_liked = 0.0  
       
     for dense_features, labels in train_loader:  
         optimizer.zero_grad()  
         dense_features = dense_features.to(device)  
         labels = labels.to(device)  
           
         rating_pred, liked_pred = model(dense_features)  
         rating_target = labels[:, 0].unsqueeze(1)  
         liked_target = labels[:, 1].unsqueeze(1)  
           
         loss_rating = criterion_rating(rating_pred, rating_target)  
         loss_liked = criterion_liked(liked_pred, liked_target)  
   
         # Set initial losses  
         if initial_loss_rating is None:  
             initial_loss_rating = loss_rating.item()  
         if initial_loss_liked is None:  
             initial_loss_liked = loss_liked.item()  
           
         # Normalize losses  
         loss = (loss_rating / initial_loss_rating) + (loss_liked / initial_loss_liked)  
           
         loss.backward()  
         optimizer.step()  
           
         running_loss_rating += loss_rating.item()  
         running_loss_liked += loss_liked.item()  
       
     train_rating_losses.append(running_loss_rating / len(train_loader))  
     train_liked_losses.append(running_loss_liked / len(train_loader))  
       
     model.eval()  
     eval_loss_rating = 0.0  
     eval_loss_liked = 0.0  
       
     with torch.no_grad():  
         for dense_features, labels in test_loader:  
             dense_features = dense_features.to(device)  
             labels = labels.to(device)  
               
             rating_pred, liked_pred = model(dense_features)  
             rating_target = labels[:, 0].unsqueeze(1)  
             liked_target = labels[:, 1].unsqueeze(1)  
               
             loss_rating = criterion_rating(rating_pred, rating_target)  
             loss_liked = criterion_liked(liked_pred, liked_target)  
               
             eval_loss_rating += loss_rating.item()  
             eval_loss_liked += loss_liked.item()  
       
     eval_rating_losses.append(eval_loss_rating / len(test_loader))  
     eval_liked_losses.append(eval_loss_liked / len(test_loader))  
     print(f'Epoch {epoch+1}, Train Rating Loss: {train_rating_losses[-1]}, Train Liked Loss: {train_liked_losses[-1]}, Eval Rating Loss: {eval_rating_losses[-1]}, Eval Liked Loss: {eval_liked_losses[-1]}')  
 # Plotting losses  
 plt.figure(figsize=(14, 6))  
 plt.subplot(1, 2, 1)  
 plt.plot(train_rating_losses, label='Train Rating Loss')  
 plt.plot(eval_rating_losses, label='Eval Rating Loss')  
 plt.xlabel('Epoch')  
 plt.ylabel('Loss')  
 plt.title('Rating Loss')  
 plt.legend()  
 plt.subplot(1, 2, 2)  
 plt.plot(train_liked_losses, label='Train Liked Loss')  
 plt.plot(eval_liked_losses, label='Eval Liked Loss')  
 plt.xlabel('Epoch')  
 plt.ylabel('Loss')  
 plt.title('Liked Loss')  
 plt.legend()  
 plt.tight_layout()  
 plt.show()

还可以通过利用 Tensorboard 监控训练的过程

from torch.utils.tensorboard import SummaryWriter  
 # Check if GPU is available  
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
 print(f"Using device: {device}")  
 # Model and Training Setup  
 embedding_size = 16  
 hidden_size = 32  
 n_users = len(user_encoder.classes_)  
 n_movies = len(movie_encoder.classes_)  
 model = MultiTaskMovieLensModel(n_users, n_movies, embedding_size, hidden_size).to(device)  
 criterion_rating = nn.MSELoss()  
 criterion_liked = nn.BCELoss()  
 optimizer = optim.Adam(model.parameters(), lr=0.001)  
 epochs = 10  
   
 # used for loss normalization  
 initial_loss_rating = None  
 initial_loss_liked = None  
   
 # TensorBoard setup  
 writer = SummaryWriter(log_dir='runs/multitask_movie_lens')  
   
 # Training Loop with TensorBoard Logging  
 for epoch in range(epochs):  
     model.train()  
     running_loss_rating = 0.0  
     running_loss_liked = 0.0  
     for batch_idx, (dense_features, labels) in enumerate(train_loader):  
         # Move data to GPU  
         dense_features = dense_features.to(device)  
         labels = labels.to(device)  
           
         optimizer.zero_grad()  
           
         rating_pred, liked_pred = model(dense_features)  
         rating_target = labels[:, 0].unsqueeze(1)  
         liked_target = labels[:, 1].unsqueeze(1)  
           
         loss_rating = criterion_rating(rating_pred, rating_target)  
         loss_liked = criterion_liked(liked_pred, liked_target)  
   
         # Set initial losses  
         if initial_loss_rating is None:  
             initial_loss_rating = loss_rating.item()  
         if initial_loss_liked is None:  
             initial_loss_liked = loss_liked.item()  
           
         # Normalize losses  
         loss = (loss_rating / initial_loss_rating) + (loss_liked / initial_loss_liked)  
           
         loss.backward()  
         optimizer.step()  
           
         running_loss_rating += loss_rating.item()  
         running_loss_liked += loss_liked.item()  
           
         # Log loss to TensorBoard  
         writer.add_scalar('Loss/Train_Rating', loss_rating.item(), epoch * len(train_loader) + batch_idx)  
         writer.add_scalar('Loss/Train_Liked', loss_liked.item(), epoch * len(train_loader) + batch_idx)  
       
     print(f'Epoch {epoch+1}/{epochs}, Train Rating Loss: {running_loss_rating / len(train_loader)}, Train Liked Loss: {running_loss_liked / len(train_loader)}')  
       
     # Evaluate on the test set  
     model.eval()  
     eval_loss_rating = 0.0  
     eval_loss_liked = 0.0  
     with torch.no_grad():  
         for dense_features, labels in test_loader:  
             # Move data to GPU  
             dense_features = dense_features.to(device)  
             labels = labels.to(device)  
               
             rating_pred, liked_pred = model(dense_features)  
             rating_target = labels[:, 0].unsqueeze(1)  
             liked_target = labels[:, 1].unsqueeze(1)  
               
             loss_rating = criterion_rating(rating_pred, rating_target)  
             loss_liked = criterion_liked(liked_pred, liked_target)  
             eval_loss_rating += loss_rating.item()  
             eval_loss_liked += loss_liked.item()  
       
     eval_loss_avg_rating = eval_loss_rating / len(test_loader)  
     eval_loss_avg_liked = eval_loss_liked / len(test_loader)  
     print(f'Epoch {epoch+1}/{epochs}, Eval Rating Loss: {eval_loss_avg_rating}, Eval Liked Loss: {eval_loss_avg_liked}')  
       
     # Log evaluation loss to TensorBoard  
     writer.add_scalar('Loss/Eval_Rating', eval_loss_avg_rating, epoch)  
     writer.add_scalar('Loss/Eval_Liked', eval_loss_avg_liked, epoch)  
 # Close the TensorBoard writer  
 writer.close()

我们在同一目录下运行 TensorBoard 来启动服务器,并在网络浏览器中检查训练和评估曲线。在以下 bash 命令中,将 runs/mutlitask_movie_lens 替换为包含事件文件(日志)的目录路径。

(base) $ tensorboard --logdir=runs/multitask_movie_lens
TensorFlow installation not found - running with reduced feature set.

运行结果如下：

NOTE: Using experimental fast data loading logic. To disable, pass  
    "--load_fast=false" and report issues on GitHub. More details:  
    
 Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all  
 TensorBoard 2.12.0 at  (Press CTRL+C to quit)

Tensorboard 损失曲线视图如上所示

5.推理

在训练完成后要使用 torch.save 函数将模型保存到磁盘。这个函数允许你保存模型的状态字典,其中包含模型的所有参数和缓冲区。保存的文件通常使用 .pth 或 .pt 扩展名。

import torch
torch.save(model.state_dict(), "model.pth")

状态字典包含所有模型参数(权重和偏置)，当想要将模型加载回代码中时,可以使用以下步骤：

# Initialize the model (make sure the architecture matches the saved model)  
model = MultiTaskMovieLensModel(n_users, n_movies, embedding_size, hidden_size)  
  
# Load the saved state dictionary into the model  
model.load_state_dict(torch.load("model.pth"))  
  
# Set the model to evaluation mode (important for inference)  
model.eval()

为了在一些未见过的数据上评估模型,可以对单个用户-电影对进行预测,并将它们与实际值进行比较。

def predict_and_compare(user_id, movie_id, model, user_encoder, movie_encoder, train_dataset, test_dataset):  
    user_idx = user_encoder.transform([user_id])[0]  
    movie_idx = movie_encoder.transform([movie_id])[0]  
    example_user = torch.tensor([[user_idx]], dtype=torch.long)  
    example_movie = torch.tensor([[movie_idx]], dtype=torch.long)  
    example_dense_features = torch.cat((example_user, example_movie), dim=1)  
    model.eval()  
    with torch.no_grad():  
        rating_pred, liked_pred = model(example_dense_features)  
        predicted_rating = rating_pred.item()  
        predicted_liked = liked_pred.item()  
    actual_row = train_dataset.data[(train_dataset.data['userId'] == user_id) & (train_dataset.data['movieId'] == movie_id)]  
    if actual_row.empty:  
        actual_row = test_dataset.data[(test_dataset.data['userId'] == user_id) & (test_dataset.data['movieId'] == movie_id)]  
    if not actual_row.empty:  
        actual_rating = actual_row['rating'].values[0]  
        actual_liked = actual_row['liked'].values[0]  
        return {  
            'User ID': user_id,  
            'Movie ID': movie_id,  
            'Predicted Rating': round(predicted_rating, 2),  
            'Actual Rating': actual_rating,  
            'Predicted Liked': 'Yes' if predicted_liked >= 0.5 else 'No',  
            'Actual Liked': 'Yes' if actual_liked == 1 else 'No'  
        }  
    else:  
        return None  
example_pairs = test_dataset.data.sample(n=5)  
results = []  
for _, row in example_pairs.iterrows():  
    user_id = row['userId']  
    movie_id = row['movieId']  
    result = predict_and_compare(user_id, movie_id, model, user_encoder, movie_encoder, train_dataset, test_dataset)  
    if result:  
        results.append(result)  
results_df = pd.DataFrame(results)  
results_df.head()

6.使用 Ray Serve 部署模型

最后就是将模型部署为一个服务,使其可以通过 API 访问，这里使用使用 Ray Serve。

使用 Ray Serve是因为它可以从单机无缝扩展到大型集群，可以处理不断增加的负载。Ray Serve 还集成了 Ray 的仪表板，为监控部署的健康状况、性能和资源使用提供了用户友好的界面。

步骤 1：加载训练好的模型

# Load your trained model (assuming it's saved as 'model.pth')  
n_users = 1000  # 示例值，替换为实际用户数  
n_movies = 1000  # 示例值，替换为实际电影数  
embedding_size = 16  
hidden_size = 32  
model = MultiTaskMovieLensModel(n_users, n_movies, embedding_size, hidden_size)  
model.load_state_dict(torch.load("model.pth"))  
model.eval()

步骤 2：定义模型服务类

import ray  
from ray import serve  
@serve.deployment  
class ModelServeDeployment:  
    def __init__(self, model):  
        self.model = model  
        self.model.eval()  
    async def __call__(self, request):  
        json_input = await request.json()  
        user_id = torch.tensor([json_input["user_id"]])  
        movie_id = torch.tensor([json_input["movie_id"]])  
        with torch.no_grad():  
            rating_pred, liked_pred = self.model(user_id, movie_id)  
        return {  
            "rating_prediction": rating_pred.item(),  
            "liked_prediction": liked_pred.item()  
        }

步骤 3：初始化 Ray 服务器

# 初始化 Ray 和 Ray Serve  
ray.init()  
serve.start()  
# 部署模型  
model_deployment = ModelServeDeployment.bind(model)  
serve.run(model_deployment)

现在应该能够在 localhost:8265 看到 ray 服务器

步骤 4：查询模型

最后就是测试 API 了。运行以下代码行时，应该可以看到一个响应，其中包含查询用户和电影的评分和喜欢预测

import requests  
  
# 定义服务器地址（Ray Serve 默认为 http://127.0.0.1:8000）  
url = "http://127.0.0.1:8000/ModelServeDeployment"  
# 示例输入  
data = {  
    "user_id": 123,  # 替换为实际用户 ID  
    "movie_id": 456  # 替换为实际电影 ID  
}  
# 向模型服务器发送 POST 请求  
response = requests.post(url, json=data)  
# 打印模型的响应  
print(response.json())

就是这样，我们刚刚训练并部署了一个多任务多标签模型！

文章详情

深度学习实战：手把手教你构建多任务、多标签模型