Highway network——《Training Very Deep Networks》
最近在家裏聽斯坦福CS231n的課程，很系統的學習了一些深度學習的知識，很受啓發。其中，我對深度學習中的梯度流的概念印象深刻。講到RNN時，提到LSTM能夠解決普通RNN的梯度消失和梯度爆炸的問題。LSTM包含兩個隱狀態，分別爲單元狀態和隱藏狀態。其中，單元狀態是梯度傳播的主要路徑。上一時刻和下一時刻單元狀態的傳播通過一個加法和一個逐元素的矩陣乘法操作。這兩個操作降低了計算複雜度，同時也能避免梯度
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相關文章
- 1. 【論文筆記】Training Very Deep Networks - Highway Networks
- 2. 【論文筆記】Highway Network: Training Very Deep Networks (2015)
- 3. Highway Networks
- 4. [論文筆記] highway networks
- 5. Very Deep Convolutional Networks for Text Classification
- 6. Highway Networks VS Deep Residual Learning
- 7. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 8. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 9. highway network
- 10. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION