使用torch定义一个包含自编码器损失和自对抗负采样损失函数的模型，(自对抗负采样损失采用RotatE中的负采样策略)并给出实例看如何使用模型

好的，我来为你解答。

首先，我们需要导入必要的库：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

然后，我们定义模型。这里以一个简单的自编码器为例：

class AutoEncoder(nn.Module):
    def __init__(self, input_dim, hidden_dim):
        super(AutoEncoder, self).__init__()
        self.encoder = nn.Linear(input_dim, hidden_dim)
        self.decoder = nn.Linear(hidden_dim, input_dim)

    def forward(self, x):
        encoded = F.relu(self.encoder(x))
        decoded = F.relu(self.decoder(encoded))
        return decoded

接着，我们定义自对抗负采样损失函数。这里以RotatE中的负采样策略为例：

class NegativeSampling(nn.Module):
    def __init__(self, num_entities, num_relations, embedding_dim, margin=1.0, corruption_rate=0.5):
        super(NegativeSampling, self).__init__()
        self.num_entities = num_entities
        self.num_relations = num_relations
        self.embedding_dim = embedding_dim
        self.margin = margin
        self.corruption_rate = corruption_rate
        self.entity_embeddings = nn.Embedding(num_entities, embedding_dim)
        self.relation_embeddings = nn.Embedding(num_relations, embedding_dim)
        self.entity_embeddings.weight.data.uniform_(-1, 1)
        self.relation_embeddings.weight.data.uniform_(-1, 1)

    def forward(self, positive_samples):
        batch_size, num_sampled = positive_samples.shape[0], 1
        negative_samples = torch.randint(low=0, high=self.num_entities, size=(batch_size, num_sampled)).to(positive_samples.device)
        corrupted_samples = torch.where(torch.rand(batch_size, self.embedding_dim).to(positive_samples.device) < self.corruption_rate, negative_samples, positive_samples)
        positive_scores = self._calc_score(positive_samples)
        negative_scores = self._calc_score(corrupted_samples)
        loss = F.relu(self.margin - positive_scores + negative_scores).mean()
        return loss

    def _calc_score(self, samples):
        head, relation, tail = torch.chunk(samples, chunks=3, dim=1)
        head_emb = self.entity_embeddings(head).view(-1, self.embedding_dim)
        relation_emb = self.relation_embeddings(relation).view(-1, self.embedding_dim)
        tail_emb = self.entity_embeddings(tail).view(-1, self.embedding_dim)
        score = torch.sum(head_emb * relation_emb * tail_emb, dim=1)
        return score

最后，我们将两个损失函数结合起来，定义整个模型：

class Model(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_entities, num_relations, embedding_dim, margin=1.0, corruption_rate=0.5):
        super(Model, self).__init__()
        self.autoencoder = AutoEncoder(input_dim, hidden_dim)
        self.negsampling = NegativeSampling(num_entities, num_relations, embedding_dim, margin, corruption_rate)

    def forward(self, x, positive_samples):
        reconstructed = self.autoencoder(x)
        loss_ae = F.mse_loss(reconstructed, x)
        loss_ns = self.negsampling(positive_samples)
        return loss_ae, loss_ns

其中，`input_dim`表示自编码器的输入维度，`hidden_dim`表示自编码器的隐藏层维度，`num_entities`表示实体总数，`num_relations`表示关系总数，`embedding_dim`表示实体和关系的嵌入维度，`margin`表示自对抗负采样损失函数中的margin，`corruption_rate`表示负采样时，对每个实体的每个维度进行替换的概率。

使用模型时，我们需要提供自编码器的输入`x`和正样本`positive_samples`，并计算出总的损失函数：

model = Model(input_dim=100, hidden_dim=50, num_entities=10000, num_relations=100, embedding_dim=50)
x = torch.randn(32, 100)
positive_samples = torch.randint(low=0, high=10000, size=(32, 3), dtype=torch.long)
loss_ae, loss_ns = model(x, positive_samples)
total_loss = loss_ae + loss_ns

以上就是使用torch定义一个包含自编码器损失和自对抗负采样损失函数的模型的示例。