解析:torch.mean(x,[a,b],keepdim=True)中[a,b]的意思是,沿着将第a和第b维的维度变为1的方向做均值,其余维度不变。 直接上例子: import torch a =torch.tensor([ [[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]]).float() b = torch....
defsoftmax(x):e_x = np.exp(x - np.max(x, axis=1, keepdims=True))returne_x / np.sum(e_x, axis=1, keepdims=True) conf_scores = softmax(scores) class_preds = np.argmax(conf_scores, axis=1) print("predicted classes:", ([(class_idx, classes[class_idx])forclass_idxinclass_...
pretrain=True, pretrain_file=None, ): """ TabNet model for Qlib Args: ps: probability to generate the bernoulli mask """ # set hyper-parameters.self.d_feat = d_feat self.out_dim = out_dim self.final_out_dim = final_out_dim ...
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to(dev_id) output = model(data) # sum up batch loss test_loss += F.nll_loss(output, target, size_average=False).item() # get the index of the max log-probability pred = output.data.max(1, keepdim=True)[1] test_accuracy += pred.eq(target.data.view_as(pred)).cpu().float(...
x = torch.max(x,2, keepdim=True)[0] x = x.view(-1,1024) x = F.relu(self.bn4(self.fc1(x))) x = F.relu(self.bn5(self.fc2(x))) x = self.fc3(x) iden =Variable(torch.from_numpy(np.array([1,0,0,0,1,0,0,0,1]).astype(np.float32))).view(1,9).repeat(batch...
1模型架构 PP-YOLOE由以下方法组成: 可扩展的backbone和neck Task Alignment Learning Efficient Task-aligned head with DFL和VFL SiLU激活函数 1.1、Backbone PP-YOLOE的Backbone主要是使用RepVGG模块以及CSP的模型思想对ResNet及逆行的改进,同时也使用了SiLU激活函数、Effitive SE Attention等模块,下面我们一一道来。
defforward(self,x):x_se=x.mean((2,3),keepdim=True)x_se=self.fc(x_se)returnx*self.act(x_se) 4、CSPNet结构 CSPNet的主要思想还是Partial Dense Block,设计Partial Dense Block的目的是: 增加梯度路径:通过分裂合并策略,可以使梯度路径的数目翻倍。由于采用了跨阶段的策略,可以减轻使用显式特征映射复制...
Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified. Outputs (1 - ∞) outputs (variadic, differentiable) : T One or more outputs forming list of tensors after splitting ...
如上图所示(xDeepFM的结构),当depth和CIN part的feature maps同时设为1时,xDeepFM就是DeepFM的一个泛化,通过为FM layer学习线性回归权重实现(注意,在DeepFM中,FM layer的units直接与output unit相连,没有任何系数)。当我们进一步移去DNN part,并同时为该feature map使用一个constant sum filter(它简单采用输入求和...