pytorch scheduler example
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Title: Understanding and Implementing Learning Rate Schedulers in PyTorch
Introduction:
Learning rate schedulers play a crucial role in training deep neural networks, helping to optimize the learning process by adjusting the learning rate over time. In PyTorch, learning rate schedulers are implemented using the torch.optim.lr_scheduler module. This tutorial will guide you through the basics of learning rate schedulers and provide a practical example using PyTorch.
Before you begin, ensure you have PyTorch installed. You can install it using:
Here, we'll use the StepLR scheduler as an example. It decreases the learning rate by a factor after a specified number of epochs.
In this example, the learning rate will be multiplied by gamma every 10 epochs.
In the training loop, we use scheduler.step() to update the learning rate at the beginning of each epoch. It's crucial to call this function at the right time to ensure the correct scheduling behavior.
Learning rate schedulers are essential for optimizing the training process of deep neural networks. PyTorch provides a convenient torch.optim.lr_scheduler module to implement various scheduling strategies. Experiment with different schedulers and parameters to find the best strategy for your specific task and model.
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