代码示例:生成随机数据并计算余弦相似度 importtorchimporttorch.nn.functionalasF# 生成随机数据num_samples=5num_features=3data=torch.randn(num_samples,num_features)# 计算余弦相似度similarity_matrix=F.cosine_similarity(data.unsqueeze(1),data.unsqueeze(0),dim=2)print("生成的随机数据:")print(data)print...
class BiLinearSimilarity(nn.Module): def __init__(self, tensor_1_dim, tensor_2_dim, activation=None): super(BiLinearSimilarity, self).__init__() self.weight_matrix = nn.Parameter(torch.Tensor(tensor_1_dim, tensor_2_dim)) self.bias = nn.Parameter(torch.Tensor(1)) self.activation = ...
def cosine_similarity(mat1, mat2): # 输入两个矩阵计算余弦相似度 sim = torch.nn.functional.cosine_similarity(mat1.view(1, -1), mat2.view(1, -1)) return sim.item() similarity_score = cosine_similarity(matrix1, matrix2) print("\nCosine Similarity:", similarity_score) 1. 2. 3. ...
tensor(0.9988) tensor(-1.) PyTorch中批量计算余弦距离 importtorch.nn.functionalasF batch_of_vectors = torch.rand((4,64)) similarity_matrix = F.cosine_similarity(batch_of_vectors.unsqueeze(1), batch_of_vectors.unsqueeze(0), dim=2) similarity_matrix te...
importtorch.nn.functionalasFbatch_of_vectors=torch.rand((4,64))similarity_matrix=F.cosine_similarity(batch_of_vectors.unsqueeze(1),batch_of_vectors.unsqueeze(0),dim=2)similarity_matrixtensor([[1.0000,0.6922,0.6480,0.6789],[0.6922,1.0000,0.7143,0.7172],[0.6480,0.7143,1.0000,0.7312],[0.6789,0.7172...
similarity_matrix = F.cosine_similarity(batch_of_vectors.unsqueeze(1), batch_of_vectors.unsqueeze(0), dim=2) similarity_matrix tensor([[1.0000,0.6922,0.6480,0.6789], [0.6922,1.0000,0.7143,0.7172], [0.6480,0.7143,1.0000,0.7312], [0.6789,0.7172,0.7312,1.0000]]) ...
similarity_matrix = F.cosine_similarity(batch_of_vectors.unsqueeze(1), batch_of_vectors.unsqueeze(0), dim=2) similarity_matrix tensor([[1.0000,0.6922,0.6480,0.6789], [0.6922,1.0000,0.7143,0.7172], [0.6480,0.7143,1.0000,0.7312], [0.6789,0.7172,0.7312,1.0000]]) ...
batch_of_vectors = torch.rand((4,64))similarity_matrix = F.cosine_similarity(batch_of_vectors.unsqueeze(1), batch_of_vectors.unsqueeze(0), dim=2)similarity_matrixtensor([[1.0000,0.6922,0.6480,0.6789], [0.6922,1.0000,0.7143,0.7172], [0.6480,0.7143,1.0000,0.7312], [0.6789,0.7172,0.7312,1.0000...
importtorch.nn.functionalasFbatch_of_vectors=torch.rand((4,64))similarity_matrix=F.cosine_similarity(batch_of_vectors.unsqueeze(1),batch_of_vectors.unsqueeze(0),dim=2)similarity_matrixtensor([[1.0000,0.6922,0.6480,0.6789],[0.6922,1.0000,0.7143,0.7172],[0.6480,0.7143,1.0000,0.7312],[0.6789,0.7172...
print(F.cosine_similarity(vector1, vector3, dim=0)) tensor(0.9988) tensor(-1.) PyTorch中批量计算余弦距离import torch.nn.functional as F batch_of_vectors = torch.rand((4, 64)) similarity_matrix = F.cosine_similarity(batch_of_vectors.unsqueeze(1), batch_of_vectors.unsqueeze(0), dim=2)...