log= torch.log(pred)#以自然对数e为底res=-torch.sum(one_hot*log)/target.shape[0]#这里的*是张量对应元素相乘print(res) 2.使用nll_loss计算交叉熵时,无需对标签进行onehot编码 #预测值,假设已做softmaxpred=torch.tensor([[0.2,0.3,0.5],[0.3,0.2,0.5],[0.4,0.4,0.2]])#真实类别标签target=torch...
log_softmax(inputs,dim = 1).gather(dim = 1,index = labels.view(-1,1)) loss = z * w if self.reduction == 'mean': loss /= -torch.sum(w) return loss.sum() if __name__ == '__main__': r = None #r = torch.randn(5) loss1 = nn.CrossEntropyLoss(weight=r,reduction='...
import torch x = torch.rand(5) x1 = torch.softmax(x, dim=-1) x2 = torch.nn.Softmax(dim=-1)(x) x3 = torch.nn.functional.softmax(x, dim=-1) x4 = torch.nn.functional.log_softmax(x, dim=-1) print(x1) print(x2) print(x3) print(x4) print(torch.log(x3)) # 随机输出: ...
torch.nn.functional.cross_entropy(input, target, weight=None, size_average=True) 该函数使用了 log_softmax 和 nll_loss,详细请看CrossEntropyLoss 参数:-input- (N,C) 其中,C 是类别的个数 -target- (N) 其大小是 0 <= targets[i] <= C-1 -weight(Variable, optional) – 一个可手动指定每个...
注:这里利用矩阵相乘y = WX+b,然后再利用softmax激活函数将其输出的和统一为1. 3.5手动实现交叉熵损失函数 1.#手动实现交叉熵损失函数2.defcross_entropy(y_hat, y):3.#1按行取4.return-torch.log(y_hat.gather(1, y.view(-1,1))) 注:这里主要是gather(dim,tensor)的方法的使用,dim=1,表示按行取...
pred = pred.log_softmax(dim=self.dim) num_class = pred.size()[-1] with torch.no_grad(): true_dist = torch.zeros_like(pred) true_dist.fill_(self.smoothing / (num_class - 1)) true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence) ...
log_prob = torch.nn.functional.log_softmax(score, dim=1) loss = -torch.sum(log_prob * smoothed_labels) / N optimizer.zero_grad loss.backward optimizer.step Mixup训练beta_distribution = torch.distributions.beta.Beta(alpha, alpha) forimages, labelsintrain_loader: ...
🐛 Bug RuntimeError: "log_softmax_lastdim_kernel_impl" not implemented for 'torch.LongTensor' To Reproduce torch.nn.functional.log_softmax(torch.arange(9), dim=0) Gives: ~/.local/share/miniconda3/envs/nndl/lib/python3.6/site-packages/torc...
torch.nn.functional.log_softmax(input, dim=None, _stacklevel=3)尽管在数学上等同于log(softmax(x)),但单独执行这两个操作会更慢,并且数值不稳定。 该函数使用替代公式来正确计算输出和梯度。具体细节可以看LogSoftmaxtorch.nn.functional.tanh(input) → Tensor...
120 * 1 * 1}x=torch::relu(x);// 激活函数 // {n * 120 * 1 * 1}// 做数据格式转换x=x.view({-1,120});// {n * 120}// 4. fcx=fc1->forward(x);x=torch::relu(x);// 5. fcx=fc2->forward(x);returntorch::log_softmax(x,1);// CrossEntryLoss = log_softmax + nll...