January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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ReLU initialization
For ReLU specifically, Kaiming He and others, in their paper, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (https://arxiv.org/abs/1502.01852), designed a specific initialization for ReLUs as 
.
For example, in one-line Python code, this can be as follows:
>>> w = np.random.randn(n_in) * sqrt(2.0/n_in)
How do we understand this? Intuitively, a rectifying linear unit is zero for half of its input, so we would need to double the size of weight variance to keep the signal’s variance constant.
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