softmax应该很熟悉了。 如果我们希望输出能够稀疏,则可以用sparsemax。 说人话就是: 其中z的含义和softmax中的z的含义是一样的。其函数图像和softmax比较为: def sparsemax(z): sum_all_z = sum(z) z_sorted = …
Sparsemax is a type of activation/output function similar to the traditional softmax, but able to output sparse probabilities. $$ \text{sparsemax}\left(z\right) = \arg_{p∈\Delta^{K−1}}\min||\mathbf{p} - \mathbf{z}||^{2} $$ ...
In this paper, we propose two novel neural models by providing sparse posterior distributions over topics based on the Gaussian sparsemax construction, enabling efficient training by stochastic backpropagation. We construct an inference network conditioned on the input data and infer the variational ...
We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we propose a new smo...
func Sparsemax(x *Node, axes ...int) (*Node, error) { op := newSparsemaxOp(axes...) return ApplyOp(op, x) } func (op *sparsemaxOp) Arity() int { return 1 } func (op *sparsemaxOp) ReturnsPtr() bool { return false } func (op *sparsemaxOp) CallsExtern() bool ...
GLU、sparsemax**函数 技术标签:算法 查看原文 Deep Learning for Extreme Multi-label Text Classification 一、背景介绍 1、研究背景:Multi-label和二分类、多分类研究的内容本身就不太一样,并且Multi-label的数据稀疏问题比单一分类更严重,因此很难学习label之间的依赖关系。2、研究...multi-label的共现性,对loss...