Скачать или смотреть Understanding the Actual Learning Rate During PyTorch Training

Discover how to accurately determine the `learning rate` in your PyTorch training. This guide breaks down the intricacies of scheduler usage and learning rate adjustments.
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Understanding the Actual Learning Rate During PyTorch Training

When diving into the world of machine learning and neural networks, one of the most pivotal components to grasp is the learning rate. It plays a crucial role in how quickly your model learns. But what if you want to know the actual learning rate used during training in PyTorch? In this guide, we will unravel this mystery using an example code snippet.

The Problem

Imagine you are training a neural network model using PyTorch, and you have set up your learning rate and scheduler as follows:

[[See Video to Reveal this Text or Code Snippet]]

During the training process, you might wonder: What is the actual learning rate used at the moment of optimizer.step()? Is it the initial value of 0.001 or the adjusted value of 0.0001 after running a scheduler step? Let's delve into the code to clarify this.

Analyzing the Code

Here’s the key part of your training loop, which we will break down:

[[See Video to Reveal this Text or Code Snippet]]

Breakdown of the Key Elements

Loop Through Batches: As you iterate through the train_loader, you are frequently updating the model's weights.

Optimizer Step: The line optimizer.step() updates the model using the learning rate that is currently in your optimizer, which is set in the beginning of the epoch.

Understanding Scheduler: After all batches in an epoch have been processed, you call scheduler.step(). This function updates the learning rate based on the defined milestones and gamma values.

The Actual Learning Rate During Training

Given this setup, the important takeaway is:

During the First Epoch: When you call optimizer.step(), the current learning rate is 0.001. This is because you have not called the scheduler yet.

Post Scheduler Call: After you call scheduler.step() at the end of the epoch, you may see that the learning rate updates to 0.0001 for the next epoch.

Thus, the output you observe during training:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

To summarize, the actual learning rate during optimizer.step() in your setup will initially be 0.001. It only changes to 0.0001 after invoking scheduler.step(), which marks the end of the training for the epoch. Sum it up like this: Always remember that the scheduler modifies the learning rate at the end of each epoch, not during the optimization steps of that epoch.

With this understanding, you can now confidently track the learning rate throughout your model training process. If you have more questions or need further clarifications on optimizers and learning rates in PyTorch, feel free to reach out! Let’s keep the continuous learning alive in the realm of machine learning!