if beta is None: beta = args.preference_beta A = weight.cpu().clone() if weight.dim() == 4: #Convolution layer A = A.view(A.size(0), -1) else: raise('The weight dim must be 4!!!') affinity_matrix = -euclidean_distances(A, squared=True) preference = np.median(affinity_matrix, axis=0) * beta cluster = AffinityPropagation(preference=preference) cluster.fit(A) return cluster.labels_, cluster.cluster_centers_, cluster.cluster_centers_indices_ 帮我给这段代码打上注释
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torch.cuda.is_available()返回False解决方案
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下面是对这段代码的注释:
```python
if beta is None: # 如果 beta 没有被指定,则使用 args 中的 preference_beta 参数
beta = args.preference_beta
A = weight.cpu().clone() # 将 weight 拷贝到 CPU 上,并赋值给 A
if weight.dim() == 4: # 如果 weight 的维度是 4(即卷积层)
A = A.view(A.size(0), -1) # 将 A 的形状变为 (batch_size, num_features),方便后续计算
else:
raise('The weight dim must be 4!!!') # 如果 weight 的维度不是 4,则抛出异常
# 计算 A 中所有样本的亲和矩阵(通过欧几里得距离计算)
affinity_matrix = -euclidean_distances(A, squared=True)
# 计算每个样本的 preference(即亲和矩阵中每一列的中位数乘以 beta)
preference = np.median(affinity_matrix, axis=0) * beta
# 使用 Affinity Propagation 算法进行聚类,并返回聚类结果、聚类中心和聚类中心的索引
cluster = AffinityPropagation(preference=preference)
cluster.fit(A)
return cluster.labels_, cluster.cluster_centers_, cluster.cluster_centers_indices_
```
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