下面是一个使用PyTorch实现infoNCE损失函数的示例代码: importtorchimporttorch.nnasnnclassInfoNCELoss(nn.Module):def__init__(self,temperature=0.1):super(InfoNCELoss,self).__init__()self.temperature=temperaturedefforward(self,features,targets):batch_size=features.size(0)similarity_matrix=torch.matmul(feat...
penalty == 'l1': loss = torch.mean(torch.abs(theta - ID)) else: assert self.penalty == 'l2', 'penalty can only be l1 or l2. Got: %s' % self.penalty loss = torch.mean(torch.pow(theta - ID, 2)) return loss 结束语 最后,以上仅供参考,欢迎各位网友批评指正与留言交流。如果对...
loss =loss_fn(y,M,M_prime) # 计算损失 train_loss.append(loss.item()) batch.set_description(str(epoch) + ' Loss:%.4f'% np.mean(train_loss[-20:])) loss.backward() optim.step() # 调用编码器优化器 loss_optim.step() # 调用判别器优化器 if epoch % 10 == 0 : # 保存模型 root...
SGDClassifier背后的概念类似于我们在第2章中为Adaline所实现的随机梯度算法。 我们可以初始化SGD版本的感知机(loss='perceptron')、逻辑回归(loss='log')及带默认参数的SVM(loss='hinge'),如下: >>>fromsklearn.linear_modelimportSGDClassifier>>>ppn = SGDClassifier(loss='perceptron')>>>lr = SGDClassifier(los...
normalize_embeddings: 如果为 True,在计算loss之前,embedding将会被归一化为模为 1。 p: 距离范数 power: 如果不是 1,embedding的每一个元素都会被以 mat = mat ** self.power 方式放大 is_inverted: 应该由子类设置。如果为False,则较小的值表示靠近的embedding(距离度量相关的子类默认设置为False)。如果为Tr...
Loss Visualization Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps SmoothGrad: removing noise by adding noise DeepDream: dream-like hallucinogenic visuals ...
Minimizing a common loss function such as cross-entropy loss is equivalent to minimizing a similar metric, the Kullback-liebler divergence between two probability distributions X (the input) and Y (the target variable). The KL-divergence measures the "distance" between two probability distributions ...
在Pytorch中实现MC Dropout很容易。所有需要做的就是将模型的dropout层设置为训练模式。这允许在不同的...
Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being...
//github.com/tomgoldstein/loss-landscape">Loss Visualization Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps SmoothGrad: removing noise by adding noise ...