On weight initialization in deep neural networks

Siddharth Krishna Kumar

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arXiv · 1704.08863

On weight initialization in deep neural networks

Siddharth Krishna Kumar

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Abstract

A proper initialization of the weights in a neural network is critical to its convergence. Current insights into weight initialization come primarily from linear activation functions. In this paper, I develop a theory for weight initializations with non-linear activations. First, I derive a general weight initialization strategy for any neural network using activation functions differentiable at 0. Next, I derive the weight initialization strategy for the Rectified Linear Unit (RELU), and provide theoretical insights into why the Xavier initialization is a poor choice with RELU activations. My analysis provides a clear demonstration of the role of non-linearities in determining the proper weight initializations.

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Siddharth Krishna Kumar. 2017-04-28. On weight initialization in deep neural networks. https://arxiv.org/abs/1704.08863

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