QRNN(Quasi-Recurrent Neural Networks)
keras代碼:https://github.com/DingKe/qrnn QRNN爲LSTM加速算法,模型結構如下: qrnn第一層爲卷積層,用於提取輸入特徵,第二層爲pooling層,用於減少特徵數目,但語常用的pooling層不同的是,qrnn採用fo-pool方法,具體計算如下. 卷積層: 對於輸入X,分別通過三個卷積層和非線性層得到Z,F,O,公式如下: Z=tanh(Wz∗X) F=
相關文章
- 1. Neural Networks
- 2. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
- 3. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 4. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 5. Convolutional Neural Networks: Application
- 6. C4:Convolutional Neural Networks
- 7. Convolutional Neural Networks-Week1
- 8. Neural Networks - 2
- 9. Non-local Neural Networks
- 10. 019 Recurrent Neural Networks
- 更多相關文章...
- • Docker Compose - Docker教程
- • Web 詞彙表 - 網站建設指南
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Neural Networks
- 2. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
- 3. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 4. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 5. Convolutional Neural Networks: Application
- 6. C4:Convolutional Neural Networks
- 7. Convolutional Neural Networks-Week1
- 8. Neural Networks - 2
- 9. Non-local Neural Networks
- 10. 019 Recurrent Neural Networks