def forward(self, embeddings):
torch.clamp(self.w, 1e-6)
centroids = get_centroids(embeddings)
cossim = get_cossim(embeddings, centroids)
sim_matrix = self.w * cossim.to(self.device) + self.b
loss, _ = calc_loss(sim_matrix)
return loss
def forward(self, embeddings, y=None):
#pdb.set_trace()
torch.clamp(self.w, 1e-6)
centroids = get_centroids(embeddings)
cossim = get_cossim(embeddings, centroids)
sim_matrix = self.w*cossim + self.b
loss, _ = calc_loss(sim_matrix)
return loss
def forward(self, embeddings):
torch.clamp(self.w, hp.re_num)
centroids = utils.get_centroids(embeddings)
cossim = utils.get_cossim(embeddings, centroids)
sim_matrix = self.w * cossim + self.b
loss, _ = utils.cal_loss(sim_matrix)
return loss
def forward(self, embeddings, embedder_net, lamb):
torch.clamp(self.w, 1e-6)
centroids = get_centroids(embeddings)
cossim = get_cossim(embeddings, centroids)
sim_matrix = self.w * cossim.to(self.device) + self.b
per_loss, _ = calc_loss(sim_matrix)
weights = embedder_net.LSTM_stack.all_weights
norm_loss = lamb * torch.sum(
torch.Tensor([
torch.norm(weights[i][j].data.to(self.device), 2)
for i in range(hp.model.num_layer) for j in range(4)
]))
loss = per_loss + norm_loss
return loss, per_loss, norm_loss