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Weight Initialization Possibilities for Feedforward Neural Network with Linear Saturated Activation Functions

机译：具有线性饱和激活函数的前馈神经网络的权重初始化可能性

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Abstract: Initial weight choice is an important aspect of the training mechanism for feedforward neural networks. This paper deals with a particular topology of a feedforward neural network, where symmetric linear saturated activation functions are used in a hidden layer. Training of such a topology is a tricky procedure, since the activation functions are not fully differentiable. Thus, a proper initialization method for that case is even more important, than dealing with neural networks with sigmoid activation functions. Therefore, several initialization possibilities are examined and tested here. As a result, particular initialization methods are recommended for application, according to the class of the task to be solved.
机译：摘要：初始权重的选择是前馈神经网络训练机制的重要方面。本文讨论了前馈神经网络的特定拓扑，其中在隐藏层中使用了对称线性饱和激活函数。训练这样的拓扑结构是一个棘手的过程，因为激活功能不能完全区分。因此，对于这种情况，合适的初始化方法比处理具有S型激活函数的神经网络更为重要。因此，在此检查并测试了几种初始化可能性。结果，根据要解决的任务的类别，建议使用特定的初始化方法。

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