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About How To Use Batch Normalization With TensorFlow and TF - Keras To Train Deep Neural Networks Faster
Batch normalization is a technique that helps combat the problems of vanishing and exploding gradients when training deep neural networks. It works by normalizing layer inputs during training to speed up convergence. The document discusses how to implement batch normalization in TensorFlow using Keras layers. Experiments on MNIST with varying network depths and activation functions show that batch normalization significantly reduces training time for deeper networks and enables them to converge faster compared to networks without batch normalization.
Detailed Information
| Author: | ['susan'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 11 |
| Language: | English |
| Format: | |
| Price: | FREE |
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