pytorch计算余弦相似度

2023-10-08 02:38| 来源: 网络整理| 查看: 265

在pytorch中，可以使用torch.cosine_similarity函数对两个向量或者张量计算余弦相似度。先看一下pytorch源码对该函数的定义：

class CosineSimilarity(Module): r"""Returns cosine similarity between :math:`x_1` and :math:`x_2`, computed along dim. .. math :: \text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}. Args: dim (int, optional): Dimension where cosine similarity is computed. Default: 1 eps (float, optional): Small value to avoid division by zero. Default: 1e-8 Shape: - Input1: :math:`(\ast_1, D, \ast_2)` where D is at position `dim` - Input2: :math:`(\ast_1, D, \ast_2)`, same shape as the Input1 - Output: :math:`(\ast_1, \ast_2)` Examples:: >>> input1 = torch.randn(100, 128) >>> input2 = torch.randn(100, 128) >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6) >>> output = cos(input1, input2) """ __constants__ = ['dim', 'eps'] def __init__(self, dim=1, eps=1e-8): super(CosineSimilarity, self).__init__() self.dim = dim self.eps = eps def forward(self, x1, x2): return F.cosine_similarity(x1, x2, self.dim, self.eps)

可以看到该函数一共有四个参数：

x1和x2为待计算余弦相似度的张量；dim为在哪个维度上计算余弦相似度；eps是为了避免被零除而设置的一个小数值。

看一下例子：

import torch x = torch.FloatTensor(torch.rand([10])) print('x', x) y = torch.FloatTensor(torch.rand([10])) print('y', y) similarity = torch.cosine_similarity(x, y, dim=0) print('similarity', similarity) x tensor([0.2817, 0.6858, 0.1820, 0.7357, 0.7625, 0.3569, 0.4781, 0.8485, 0.1385, 0.5654]) y tensor([0.3366, 0.8959, 0.7776, 0.2475, 0.9202, 0.2845, 0.7284, 0.8150, 0.2577, 0.0085]) similarity tensor(0.8502)

再看一个例子，给定一个张量，计算多个张量与它的余弦相似度，并将计算得到的余弦相似度标准化。

import torch def get_att_dis(target, behaviored): attention_distribution = [] for i in range(behaviored.size(0)): attention_score = torch.cosine_similarity(target, behaviored[i].view(1, -1)) # 计算每一个元素与给定元素的余弦相似度 attention_distribution.append(attention_score) attention_distribution = torch.Tensor(attention_distribution) return attention_distribution / torch.sum(attention_distribution, 0) # 标准化 a = torch.FloatTensor(torch.rand(1, 10)) print('a', a) b = torch.FloatTensor(torch.rand(3, 10)) print('b', b) similarity = get_att_dis(target=a, behaviored=b) print('similarity', similarity) a tensor([[0.9255, 0.2194, 0.8370, 0.5346, 0.5152, 0.4645, 0.4926, 0.9882, 0.2783, 0.9258]]) b tensor([[0.6874, 0.4054, 0.5739, 0.8017, 0.9861, 0.0154, 0.8513, 0.8427, 0.6669, 0.0694], [0.1720, 0.6793, 0.7764, 0.4583, 0.8167, 0.2718, 0.9686, 0.9301, 0.2421, 0.0811], [0.2336, 0.4783, 0.5576, 0.6518, 0.9943, 0.6766, 0.0044, 0.7935, 0.2098, 0.0719]]) similarity tensor([0.3448, 0.3318, 0.3234])

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pytorch计算余弦相似度

pytorch计算余弦相似度

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