使用py torch实现基于深度学习的视频推荐算法代码

基于深度学习的视频推荐算法可以分为两个部分：视频特征提取和推荐模型。下面是一个使用PyTorch实现的视频推荐算法代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义视频特征提取模型
class VideoFeatureExtractor(nn.Module):
    def __init__(self):
        super(VideoFeatureExtractor, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 32 * 8 * 8)
        return x

# 定义推荐模型
class VideoRecommendationModel(nn.Module):
    def __init__(self, num_videos, embedding_dim):
        super(VideoRecommendationModel, self).__init__()
        self.video_embedding = nn.Embedding(num_videos, embedding_dim)
        self.user_embedding = nn.Embedding(num_users, embedding_dim)
        self.fc1 = nn.Linear(2 * embedding_dim, 64)
        self.fc2 = nn.Linear(64, 1)

    def forward(self, user_ids, video_ids):
        user_embed = self.user_embedding(user_ids)
        video_embed = self.video_embedding(video_ids)
        x = torch.cat([user_embed, video_embed], dim=1)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return torch.sigmoid(x)

# 加载数据
data = np.load('video_data.npy')
num_users, num_videos, embedding_dim = data.shape
train_data = torch.tensor(data[:int(0.8 * num_users)])
test_data = torch.tensor(data[int(0.8 * num_users):])

# 定义模型和优化器
feature_extractor = VideoFeatureExtractor()
recommendation_model = VideoRecommendationModel(num_videos, embedding_dim)
optimizer = optim.Adam(recommendation_model.parameters())

# 训练模型
for epoch in range(10):
    for user_ids, video_ids, ratings in train_data:
        optimizer.zero_grad()
        video_features = feature_extractor(video_ids)
        ratings_pred = recommendation_model(user_ids, video_ids)
        loss = nn.BCELoss()(ratings_pred, ratings)
        loss.backward()
        optimizer.step()

    # 计算测试集准确率
    test_ratings_pred = recommendation_model(test_data[:, 0], test_data[:, 1])
    test_loss = nn.BCELoss()(test_ratings_pred, test_data[:, 2])
    test_accuracy = ((test_ratings_pred > 0.5).float() == test_data[:, 2]).float().mean()
    print('Epoch %d: Test Loss %.4f, Test Accuracy %.4f' % (epoch, test_loss.item(), test_accuracy.item()))

在这个示例中，我们定义了一个视频特征提取模型和一个推荐模型，使用交叉熵损失函数进行训练，并计算测试集的准确率。在训练过程中，我们首先提取视频特征，然后将用户ID和视频ID嵌入到推荐模型中，最后计算预测评分并计算损失。