Crandi

Issues Nicklashansen Adaptive Learning Rate Schedule Github

Leo Migdal
-
issues nicklashansen adaptive learning rate schedule github

There was an error while loading. Please reload this page. PyTorch implementation of the "Learning an Adaptive Learning Rate Schedule" paper found here: https://arxiv.org/abs/1909.09712. Work in progress! A controller is optimized by PPO to generate adaptive learning rate schedules. Both the actor and the critic are MLPs with 2 hidden layers of size 32.

Three distinct child network architectures are used: 1) an MLP with 3 hidden layers, 2) LeNet-5 and 3) ResNet-18. Learning rate schedules are evaluated on three different datasets: 1) MNIST, 2) Fashion-MNIST and 3) CIFAR10. Original paper experiments with combinations of Fashion-MNIST, CIFAR10, LeNet-5 and ResNet-18 only. In each of the three settings, child networks are optimized using Adam with an initial learning rate in (1e-2, 1e-3, 1e-4) and are trained for 1000 steps on the full training set (40-50k samples)... 20-25 epochs. Learning rate schedules are evaluated based on validation loss over the course of training.

Test loss and test accuracies are in the pipeline. Experiments are made in both a discrete and continuous setting. In the discrete setting, the controller controls the learning rate by proposing one of the following actions every 10 steps: 1) increase the learning rate, 2) decrease the learning rate, 3) do nothing. In the continuous setting, the controller instead proposes a real-valued scaling factor, which allows the controller to modify learning rates with finer granularity. Maximum change per LR update has been set to 5% for simplicity (action space is not stated in the paper). In both the discrete and the continuous setting, Gaussian noise is optionally applied to learning rate updates.

Observations for the controller contain information about current training loss, validation loss, variance of predictions, variance of prediction changes, mean and variance of the weights of the output layer as well as the previous... To make credit assignment easier, the validation loss at each step is used as reward signal rather than the final validation loss. Both observations and rewards are normalized by a running mean. There was an error while loading. Please reload this page. Automate your workflow from idea to production

GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Hosted runners for every major OS make it easy to build and test all your projects. Run directly on a VM or inside a container. Use your own VMs, in the cloud or on-prem, with self-hosted runners. Save time with matrix workflows that simultaneously test across multiple operating systems and versions of your runtime.

There was an error while loading. Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs. There was an error while loading. Please reload this page.

There was an error while loading. Please reload this page. Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request. There was an error while loading.

Please reload this page. deps.dev - Dependencies and security vulnerabilities Gitingest - Export source code for LLM usage Repomix - Export source code for LLM usage uithub - Export source code for LLM usage Users that are interested in adaptive-learning-rate-schedule are comparing it to the libraries listed below

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

People Also Search

There Was An Error While Loading. Please Reload This Page.

There was an error while loading. Please reload this page. PyTorch implementation of the "Learning an Adaptive Learning Rate Schedule" paper found here: https://arxiv.org/abs/1909.09712. Work in progress! A controller is optimized by PPO to generate adaptive learning rate schedules. Both the actor and the critic are MLPs with 2 hidden layers of size 32.

Three Distinct Child Network Architectures Are Used: 1) An MLP

Three distinct child network architectures are used: 1) an MLP with 3 hidden layers, 2) LeNet-5 and 3) ResNet-18. Learning rate schedules are evaluated on three different datasets: 1) MNIST, 2) Fashion-MNIST and 3) CIFAR10. Original paper experiments with combinations of Fashion-MNIST, CIFAR10, LeNet-5 and ResNet-18 only. In each of the three settings, child networks are optimized using Adam with ...

Test Loss And Test Accuracies Are In The Pipeline. Experiments

Test loss and test accuracies are in the pipeline. Experiments are made in both a discrete and continuous setting. In the discrete setting, the controller controls the learning rate by proposing one of the following actions every 10 steps: 1) increase the learning rate, 2) decrease the learning rate, 3) do nothing. In the continuous setting, the controller instead proposes a real-valued scaling fa...

Observations For The Controller Contain Information About Current Training Loss,

Observations for the controller contain information about current training loss, validation loss, variance of predictions, variance of prediction changes, mean and variance of the weights of the output layer as well as the previous... To make credit assignment easier, the validation loss at each step is used as reward signal rather than the final validation loss. Both observations and rewards are ...

GitHub Actions Makes It Easy To Automate All Your Software

GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Hosted runners for every major OS make it easy to build and test all your projects. Run directly on a VM or inside a container. Use your own VMs, in the cloud or on-prem, with self-hosted runners. Save time with matrix workflows that simultaneously ...