语法如下: 1 drop-shadow(offset-x offset-y standard-deviation color) 可以看出,drop-shadow 比 box-shadow 少了一个阴影的扩展半径
矩阵的var(A,0,1)可以直接写作var(A) std函数:standard-deviation计算标准差,同上; min,max函数会自动忽略缺失值,但是返回线性索引时不能忽略;;求对应位置的最值
The mean and standard-deviation are calculated per-dimension over all mini-batches of the same process
If unbiased is False, then the standard-deviation will be calculated via the biased estimator. ) tensor(0.5130) torch.std(input, dim, keepdim=False, unbiased=True, out=None) → Tensor Returns the standard-deviation If unbiased is False, then the standard-deviation will be calculated via the biased estimator. If unbiased is False, then the standard-deviation will be calculated via the biased estimator. If unbiased is False, then the standard-deviation will be calculated via the biased estimator.
mean = {} -- store the mean, to normalize the test set in the future stdv = {} -- store the standard-deviation
mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \betay=Var[x]+ϵ x−E[x]∗γ+β The mean and standard-deviation mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \betay=Var[x]+ϵ x−E[x]∗γ+β The mean and standard-deviation mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \betay=Var[x]+ϵ x−E[x]∗γ+β The mean and standard-deviation The mean and standard-deviation are calculated separately over the each group. γ\gammaγ and β\betaβ are mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \betay=Var[x]+ϵ x−E[x]∗γ+β The mean and standard-deviation
1) end mean = {} -- store the mean, to normalize the test set in the future stdv = {} -- store the standard-deviation