_cosine_simililarity(self, x, y): # x shape: (N, 1, C) # y shape: (1, 2N, C) # v shape: (N, 2N) v = self._cosine_similarity(x.unsqueeze(1), y.unsqueeze(0)) return v def forward(self, zis, zjs): representations = torch.cat([zjs, zis], dim=0) similarity_matrix ...
()torch.nn.functional.cosine_embedding_loss()torch.nn.functional.cosine_similarity()torch.nn.functional.cross_entropy()torch.nn.functional.ctc_loss()torch.nn.functional.dropout()torch.nn.functional.dropout2d()torch.nn.functional.dropout3d()torch.nn.functional.elu()torch.nn.functional.elu_()torch....
相似度矩阵准备 # 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...
运行程序,就可以看到所有的函数、方法 import torch s = dir(torch) for i in s: print(i) 1. 2. 3. 4. 输出有一千多个结果 AVG AggregationType AnyType Argument ArgumentSpec BFloat16Storage BFloat16Tensor BenchmarkConfig BenchmarkExecutionStats Block BoolStorage BoolTensor BoolType BufferDict Byte...
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...
torch.linalg.matrix_norm: lambda input, ord="fro", dim=( -2, -1, ), keepdim=False, out=None, dtype=None: -1, torch.norm_except_dim: lambda v, pow=2, dim=0: -1, torch.nuclear_norm: lambda input, p="fro", dim=None, keepdim=False, out=None, dtype=None: -1, torch...
def forward(self, x, edge_index): """ Forward propagation pass with features an indices. :param x: Feature matrix. :param edge_index: Indices. """ edge_index, _ = remove_self_loops(edge_index, None) row, col = edge_index if self.norm: out = scatter_mean(x[col], row, dim=0...
.matrix_power: lambda input, n: -1, torch.linalg.matrix_power: lambda input, n, out=None: -1, torch.linalg.matrix_rank: lambda input, tol=None, hermitian=False: -1, torch.linalg.multi_dot: lambda tensors, out=None: -1, torch.matrix_exp: lambda input: -1, torch.linalg.matrix_...
ac,bc->ab表示两个输入Tensor和一个输出Tensor。左手侧标记输入的尺寸，用逗号分隔。右侧显示了输出...