Crandi

Github Nicklashansen Adaptive Learning Rate Schedule Pytorch

Leo Migdal
-
github nicklashansen adaptive learning rate schedule pytorch

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. A long long time ago, almost all neural networks were trained using a fixed learning rate and the stochastic gradient descent (SGD) optimizer. Then the whole deep learning revolution thing happened, leading to a whirlwind of new techniques and ideas. In the area of model optimization, the two most influential of these new ideas have been learning rate schedulers and adaptive optimizers. In this chapter, we will discuss the history of learning rate schedulers and optimizers, leading up to the two techniques best-known among practitioners today: OneCycleLR and the Adam optimizer. We will discuss the relative merits of these two techniques.

TLDR: you can stick to Adam (or one of its derivatives) during the development stage of the project, but you should try additionally incorporating OneCycleLR into your model as well eventually. All optimizers have a learning rate hyperparameter, which is one of the most important hyperparameters affecting model performance. Communities for your favorite technologies. Explore all Collectives Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work.

Bring the best of human thought and AI automation together at your work. Learn more Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. In the realm of deep learning, PyTorch stands as a beacon, illuminating the path for researchers and practitioners to traverse the complex landscapes of artificial intelligence. Its dynamic computational graph and user-friendly interface have solidified its position as a preferred framework for developing neural networks.

As we delve into the nuances of model training, one essential aspect that demands meticulous attention is the learning rate. To navigate the fluctuating terrains of optimization effectively, PyTorch introduces a potent ally—the learning rate scheduler. This article aims to demystify the PyTorch learning rate scheduler, providing insights into its syntax, parameters, and indispensable role in enhancing the efficiency and efficacy of model training. PyTorch, an open-source machine learning library, has gained immense popularity for its dynamic computation graph and ease of use. Developed by Facebook's AI Research lab (FAIR), PyTorch has become a go-to framework for building and training deep learning models. Its flexibility and dynamic nature make it particularly well-suited for research and experimentation, allowing practitioners to iterate swiftly and explore innovative approaches in the ever-evolving field of artificial intelligence.

At the heart of effective model training lies the learning rate—a hyperparameter crucial for controlling the step size during optimization. PyTorch provides a sophisticated mechanism, known as the learning rate scheduler, to dynamically adjust this hyperparameter as the training progresses. The syntax for incorporating a learning rate scheduler into your PyTorch training pipeline is both intuitive and flexible. At its core, the scheduler is integrated into the optimizer, working hand in hand to regulate the learning rate based on predefined policies. The typical syntax for implementing a learning rate scheduler involves instantiating an optimizer and a scheduler, then stepping through epochs or batches, updating the learning rate accordingly. The versatility of the scheduler is reflected in its ability to accommodate various parameters, allowing practitioners to tailor its behavior to meet specific training requirements.

The importance of learning rate schedulers becomes evident when considering the dynamic nature of model training. As models traverse complex loss landscapes, a fixed learning rate may hinder convergence or cause overshooting. Learning rate schedulers address this challenge by adapting the learning rate based on the model's performance during training. This adaptability is crucial for avoiding divergence, accelerating convergence, and facilitating the discovery of optimal model parameters. The provided test accuracy of approximately 95.6% suggests that the trained neural network model performs well on the test set.

People Also Search

PyTorch Implementation Of The "Learning An Adaptive Learning Rate Schedule"

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) ...

Original Paper Experiments With Combinations Of Fashion-MNIST, CIFAR10, LeNet-5 And

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 train...

In The Discrete Setting, The Controller Controls The Learning Rate

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...

Both Observations And Rewards Are Normalized By A Running Mean.

Both observations and rewards are normalized by a running mean. A long long time ago, almost all neural networks were trained using a fixed learning rate and the stochastic gradient descent (SGD) optimizer. Then the whole deep learning revolution thing happened, leading to a whirlwind of new techniques and ideas. In the area of model optimization, the two most influential of these new ideas have b...

TLDR: You Can Stick To Adam (or One Of Its

TLDR: you can stick to Adam (or one of its derivatives) during the development stage of the project, but you should try additionally incorporating OneCycleLR into your model as well eventually. All optimizers have a learning rate hyperparameter, which is one of the most important hyperparameters affecting model performance. Communities for your favorite technologies. Explore all Collectives Stack ...