Training Deep Nets with Sublinear Memory Cost
《Training Deep Nets with Sublinear Memory Cost》筆記 摘要 我們提出了一種減少深度神經網絡訓練時內存消耗的系統性方法。具體來說，我們設計了一個算法，訓練一個 n n 層網絡僅耗費 O(n−−√) O ( n ) 的內存，每個mini-batch只需要一個額外的前向計算成本。由於許多最先進的模型已經達到了GPU顯存的上限，我們的算法允許探索更深入更復雜的
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相關文章
- 1. CHAPTER 11-Training Deep Neural Nets-part3
- 2. Deep Convolutional Nets for Semantic Image Segmentation with Deep Gaussian CRFs
- 3. Deep Stereo Matching with Explicit Cost Aggregation Sub-Architecture
- 4. FitNets: Hints for Thin Deep Nets
- 5. BinaryConnect： Training Deep Neural Networks with binary weights during propagations
- 6. TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS
- 7. Distributed Training using Apache MXNet with Horovod
- 8. Linear regression with one variable - Cost function
- 9. Violations Associated with Nets
- 10. Aspect Level Sentiment Classification with Deep Memory Network筆記