# Weight Initialization Methods - Class review

Last updated on：8 months ago

A well chose initialization of filter weights can speed up your learning systems. I want to make a summary of some advanced weight initialization methods in this blog.

# What is Initialization?

When you train your learning systems, you should first set the parameters of the systems. Better values of parameters can later be obtained by updating.

**A well-chosen initialization can:**

Speed up the convergence of gradient descent

Increase the odds of gradient descent converging to a lower training (and generalization) error

Initializing weights to very large random values does not work well.

# Zeros initialization

Zeros initialization means you initialize your learning systems rate with 0 value, which is harmful, which leads to network failing break symmetry and $n^{[l]} = 1$.

**Symmetry breaking**

A phenomenon in which (infinitesimally) small fluctuations acting on a system crossing a critical point decide the systems’ fate, by determining which branch of a bifurcation is taken.

# Random initialization

## Gaussian distributions initialization

N means normal distribution.

**Advantages**

- Symmetry is broken so long as $W^{[l]}$ is initialized randomly.

**Shortcoming**

- As the result of fixed standard deviations, very deep models (> 8 conv layers) have difficulties to converge.

## Xavier initialization

U means continuous uniform distribution.

## He initialization

Multiply $\sqrt{\frac{2}{layers-dims[l - 1]}}$ when initializing $W^{[l]}$

**Xavier initialization vs. He initialization**

The former is for linear activation functions only, but the latter is also valid by taking ReLU/PReLU into account.

# Reference

[1] Deeplearning.ai, Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

[2] He, K., Zhang, X., Ren, S. and Sun, J., 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In *Proceedings of the IEEE international conference on computer vision* (pp. 1026-1034).

[3] Glorot, X. and Bengio, Y., 2010, March. Understanding the difficulty of training deep feedforward neural networks. In *Proceedings of the thirteenth international conference on artificial intelligence and statistics* (pp. 249-256). JMLR Workshop and Conference Proceedings.

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