Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift論文翻譯
Internal Covariate Shift： 1）定義：訓練時，上一層的參數的更新改變後，下一層的輸入的分佈就會變化，使訓練變得複雜。 2）缺點： 1.學習率不能太高（參數不能變化太劇烈），進而導致訓練緩慢 2.層參數初始化必須細緻 3.使用飽和非線性激活函數（如sigmoid，正負兩邊都會飽和）的訓練很困難 3）解決方法： 歸一化輸入層 mini-batch： 1）優點：簡單高效，訓練集的
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相關文章
- 1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文翻譯（轉）
- 2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
- 3. 《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》論文筆記
- 4. 論文閱讀 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 5. 論文筆記：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 6. [論文閱讀] Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
- 7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 8. 【inv2】Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 9. Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift
- 10. (11)[ICML15] Batch Norm: Accelerating Deep Network Training by Reducing Internal Covariate Shift