2. 程式人生 > >googLeNet--Inception四部曲四Inception-ResNet and the Impact of Residual Connections on Learning

googLeNet--Inception四部曲四Inception-ResNet and the Impact of Residual Connections on Learning

阿新 • • 發佈：2019-01-30

原論文：Going deeper with convolutions

作者：Christian Szegedy，Wei Liu，Yangqing Jia，Pierre Sermanet，Scott Reed，Dragomir Anguelov，Dumitru Erhan，Vincent Vanhoucke，Andrew Rabinovich

時間：February 2012

本文的大部分觀點來自於這篇論文，並且加入了一些自己的理解。該部落格純屬讀書筆記。

Pure Inception blocks

基於對TensorFlow系統的使用，使得Inception architecture不需要過多的考慮分散式訓練帶來的諸多問題，不需要考慮模型的分割。這樣我們的模型的結構會更簡潔，計算量也更少。

Scaling of the Residuals

文章提到當卷積核的數量超過1000個時，殘差學習會變得不穩定，當隨著迭代次數增加，引數基本為0，也就是說殘差學習最終得到的只是一個恆等對映。而通過縮放殘差因子x可以使得訓練更加穩定。文章還否定了ResNet的two-phase 訓練方法，先使用較低的學習率訓練進行預熱，然後使用高學習率。但是這種做法在卷積核數量很多的情況下也行不通。所以文章最終只採用縮放殘差因子的方法進行訓練。 實驗結果如圖1所示。總體效果差強人意，畢竟殘差學習是微軟的成果，而且文章只是把效果提升歸結於模型規模的提升。 圖1

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