- Neural Networks with Keras Cookbook
- V Kishore Ayyadevara
- 215字
- 2021-07-02 12:46:30
Speeding up the training process using batch normalization
In the previous section on the scaling dataset, we learned that optimization is slow when the input data is not scaled (that is, it is not between zero and one).
The hidden layer value could be high in the following scenarios:
- Input data values are high
- Weight values are high
- The multiplication of weight and input are high
Any of these scenarios can result in a large output value on the hidden layer.
Note that the hidden layer is the input layer to output layer. Hence, the phenomenon of high input values resulting in a slow optimization holds true when hidden layer values are large as well.
Batch normalization comes to the rescue in this scenario. We have already learned that, when input values are high, we perform scaling to reduce the input values. Additionally, we have learned that scaling can also be performed using a different method, which is to subtract the mean of the input and divide it by the standard deviation of the input. Batch normalization performs this method of scaling.
Typically, all values are scaled using the following formula:




Notice that γ and β are learned during training, along with the original parameters of the network.
- 編程的修煉
- 造個小程序:與微信一起干件正經事兒
- Flux Architecture
- jQuery開發基礎教程
- TradeStation交易應用實踐:量化方法構建贏家策略(原書第2版)
- Mastering Apache Maven 3
- 微信小程序項目開發實戰
- Getting Started with Laravel 4
- 編程可以很簡單
- Django Design Patterns and Best Practices
- Oracle 12c從入門到精通(視頻教學超值版)
- Python編程入門(第3版)
- 一步一步學Spring Boot:微服務項目實戰(第2版)
- ASP.NET jQuery Cookbook(Second Edition)
- 鋁合金陽極氧化與表面處理技術(第三版)