import torch import numpy as np from transformers import BertTokenizer, BertModel, BertForMaskedLM from sklearn.metrics.pairwise import euclidean_distances # 欧氏距离 from sklearn.metrics.pairwise import cosine_similarity # 余弦距离 model_class, tokenizer_class, pretrained_weights = (BertModel,BertTo...
similarity=max(∥x1∥2⋅∥x2∥2,ϵ)x1⋅x2. Parameters Shape: Examples:: >>> input1 = torch.randn(100, 128) >>> input2 = torch.randn(100, 128) >>> cos = nn.CosineSimilarity(dim=1, eps=1e-6) >>> output = cos(input1, input2) Pairwise...
cosine_similarity(base_y, base_z, 1, 1e-6).view(-1) elif self.distance == 'l2': dist_a = F.pairwise_distance(base_x, base_y, 2).view(-1) dist_b = F.pairwise_distance(base_y, base_z, 2).view(-1) else: assert False, "Wrong args.distance" print('fc7 norms:', base...
def pairwise_cosine(X, C, normalize=False): r"""Pairwise Cosine Similarity or Dot-product Similarity Shape: - Input: :math:`X\in\mathcal{R}^{B\times N\times D}` :math:`C\in\mathcal{R}^{K\times D}` :math:`S\in \mathcal{R}^K` (where :math:`B` is batch, :math:`N`...
# similarity matrix sim_mat = np.zeros([len(sentences), len(sentences)]) 1. 2. from sklearn.metrics.pairwise import cosine_similarity 1. for i in range(len(sentences)): for j in range(len(sentences)): if i != j: sim_mat[i][j] = cosine_similarity(sentence_vectors[i].reshape(...
>>> input1 = autograd.Variable(torch.randn(100, 128)) >>> input2 = autograd.Variable(torch.randn(100, 128)) >>> output = F.pairwise_distance(input1, input2, p=2) >>> output.backward()torch.nn.functional.cosine_similarity(x1, x2, dim=1, eps=1e-08)...
>>>input1 = autograd.Variable(torch.randn(100,128))>>>input2 = autograd.Variable(torch.randn(100,128))>>>output = F.pairwise_distance(input1, input2, p=2)>>>output.backward() torch.nn.functional.cosine_similarity(x1, x2, dim=1, eps=1e-08) ...
from .distance import CosineSimilarity as CosineSimilarity, PairwiseDistance as PairwiseDistance from .dropout import AlphaDropout as AlphaDropout, Dropout as Dropout, Dropout2d as Dropout2d, Dropout3d as Dropout3d, \ FeatureAlphaDropout as FeatureAlphaDropout ...
torch.nn.functional.pairwise_distance(x1, x2, p=2, eps=1e-06, keepdim=False)详细可见 PairwiseDistancetorch.nn.functional.cosine_similarity(x1, x2, dim=1, eps=1e-08)source计算向量v1、v2之间的距离 $$similarity = \frac{x_1 x x_2}{\max(\lVert v_1 \rVert_2 x \max(\lVert ...
pairwise_distancetorch.nn.functional.pairwise_distance(x1, x2, p=2.0, eps=1e-06, keepdim=False) [source] See torch.nn.PairwiseDistance for details cosine_similaritytorch.nn.functional.cosine_similarity(x1, x2, dim=1, eps=1e-8) → Tensor Returns cosine similarity between x1 and x2,...