Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification
arXiv:1806.01593
Abstract
Learning rate scheduler has been a critical issue in the deep neural network training. Several schedulers and methods have been proposed, including step decay scheduler, adaptive method, cosine scheduler and cyclical scheduler. This paper proposes a new scheduling method, named hyperbolic-tangent decay (HTD). We run experiments on several benchmarks such as: ResNet, Wide ResNet and DenseNet for CIFAR-10 and CIFAR-100 datasets, LSTM for PAMAP2 dataset, ResNet on ImageNet and Fashion-MNIST datasets. In our experiments, HTD outperforms step decay and cosine scheduler in nearly all cases, while requiring less hyperparameters than step decay, and more flexible than cosine scheduler. Code is available at https://github.com/BIGBALLON/HTD.
arXiv commentWACV2019
References in corpus (9)
- Adam: A Method for Stochastic Optimization
- Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
- ADADELTA: An Adaptive Learning Rate Method
- Wide Residual Networks
- SGDR: Stochastic Gradient Descent with Warm Restarts
- Improving Generalization Performance by Switching from Adam to SGD
- Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearables
- CondenseNet: An Efficient DenseNet using Learned Group Convolutions
- Memory-Efficient Implementation of DenseNets