帮我写一个SAKT模型代码用paddle实现

import paddle
import paddle.nn as nn

class SAKTModel(nn.Layer):
def __init__(self, num_items, num_skills, hidden_dim):
super(SAKTModel, self).__init__()
self.embedding = nn.Embedding(num_items, hidden_dim)
self.skill_embedding = nn.Embedding(num_skills, hidden_dim)
self.transformer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=8)
self.fc = nn.Linear(hidden_dim, 1)

def forward(self, question_ids, skill_ids):
question_emb = self.embedding(question_ids)
skill_emb = self.skill_embedding(skill_ids)
input_emb = question_emb + skill_emb

output = self.transformer(input_emb)
logits = self.fc(output)

return logits.squeeze(-1)

# 定义模型的输入和输出维度
num_items = 1000 # 假设有1000个题目
num_skills = 50 # 假设有50个知识点
hidden_dim = 128

# 创建SAKT模型实例
sakt_model = SAKTModel(num_items, num_skills, hidden_dim)

# 定义损失函数和优化器
loss_fn = nn.BCEWithLogitsLoss()
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=sakt_model.parameters())
# 假设有训练数据和标签，训练模型
train_data = paddle.to_tensor(train_data)
train_labels = paddle.to_tensor(train_labels)
for epoch in range(num_epochs):
# 前向传播
logits = sakt_model(train_data)
# 计算损失
loss = loss_fn(logits, train_labels)
# 反向传播
loss.backward()
# 更新参数
optimizer.step()
optimizer.clear_grad()

# 使用训练好的模型进行预测
test_data = paddle.to_tensor(test_data)
logits = sakt_model(test_data)
predictions = paddle.where(logits > 0, paddle.ones_like(logits), paddle.zeros_like(logits))

# 输出预测结果
print(predictions)<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* [SAKT：self attentive knowledge tracing知识跟踪模型](https://blog.csdn.net/zephyr_wang/article/details/111828319)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *3* [SAKT:pytorch实施的“知识跟踪的自我专注模型”](https://download.csdn.net/download/weixin_42135753/16717758)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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