August 2018
Intermediate to advanced
438 pages
12h 3m
English
Content preview from Hands-On Transfer Learning with Python
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Initialization heuristics
Let's consider a dense layer with m input and n output units:
- Sample each weight from the uniform distribution
.
- Glorot and Bengio suggested a normalized version of the uniform distribution initialization:
. This is designed to have same variance of the gradients in each layer, called Glorot Uniform. - Sampling each weight from normal distribution with a mean of 0 and a variance of
. This is similar to Glorot Uniform ...
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