Using Learning Rate Schedule And Learning Rate Warmup With Tensorflow2

Leo Migdal

-Nov 17, 2025, 1:41 AM

using learning rate schedule and learning rate warmup with tensorflow2

Learn through the super-clean Baeldung Pro experience: No ads, dark-mode and 6 months free of IntelliJ Idea Ultimate to start with. When we’re training neural networks, choosing the learning rate (LR) is a crucial step. This value defines how each pass on the gradient changes the weights in each layer. In this tutorial, we’ll show how different strategies for defining the LR affect the accuracy of a model. We’ll consider the warm-up scenario, which only includes a few initial iterations.

For a more theoretical aspect of it, we refer to another article of ours. Here, we’ll focus on the implementation aspects and performance comparison of different approaches. To keep things simple, we use the well-known fashion MNIST dataset. Let’s start by loading the required libraries and this computer vision dataset with labels: The learning rate is one of the most critical hyperparameters when training neural networks with TensorFlow. It controls how much we adjust our model weights in response to the estimated error each time the model weights are updated.

If the learning rate is too small, training will take too long or might get stuck; if it's too large, training might diverge or oscillate without reaching the optimal solution. The learning rate (often denoted as α or lr) is a small positive value, typically ranging from 0.1 to 0.0001, that controls the step size during optimization. During backpropagation, the gradients indicate the direction to move to reduce the loss, while the learning rate determines how large of a step to take in that direction. Mathematically, for a weight parameter w, the update rule is: In TensorFlow, you typically set the learning rate when creating an optimizer: Let's see how different learning rates affect model training:

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Connect and share knowledge within a single location that is structured and easy to search. A Gentle Introduction to Learning Rate SchedulersImage by Author | ChatGPT Ever wondered why your neural network seems to get stuck during training, or why it starts strong but fails to reach its full potential? The culprit might be your learning rate – arguably one of the most important hyperparameters in machine learning. While a fixed learning rate can work, it often leads to suboptimal results. Learning rate schedulers offer a more dynamic approach by automatically adjusting the learning rate during training.

In this article, you’ll discover five popular learning rate schedulers through clear visualizations and hands-on examples. You’ll learn when to use each scheduler, see their behavior patterns, and understand how they can improve your model’s performance. We’ll start with the basics, explore sklearn’s approach versus deep learning requirements, then move to practical implementation using the MNIST dataset. By the end, you’ll have both the theoretical understanding and practical code to start using learning rate schedulers in your own projects. Imagine you’re hiking down a mountain in thick fog, trying to reach the valley. The learning rate is like your step size – take steps too large, and you might overshoot the valley or bounce between mountainsides.

Take steps too small, and you’ll move painfully slowly, possibly getting stuck on a ledge before reaching the bottom. | How to use learning rate schedules in TensorFlow? Discover how to implement learning rate schedules in TensorFlow to optimize your model training and improve performance with this comprehensive guide. Defining Learning Rate Schedules in TensorFlow Practical Use of Learning Rate Schedules Implementing Custom Learning Rate Schedules

The learning rate is an important hyperparameter in deep learning networks - and it directly dictates the degree to which updates to weights are performed, which are estimated to minimize some given loss function. In SGD: $$ weight_{t+1} = weight_t - lr * \frac{derror}{dweight_t} $$ With a learning rate of 0, the updated weight is just back to itself - weightt. The learning rate is effectively a knob we can turn to enable or disable learning, and it has major influence over how much learning is happening, by directly controlling the degree of weight updates. Different optimizers utilize learning rates differently - but the underlying concept stays the same.

Needless to say, learning rates have been the object of many studies, papers and practitioner's benchmarks. Generally speaking, pretty much everyone agrees that a static learning rate won't cut it, and some type of learning rate reduction happens in most techniques that tune the learning rate during training - whether... You can use a learning rate schedule to modulate how the learning rate of your optimizer changes over time. Several built-in learning rate schedules are available, such as keras.optimizers.schedules.ExponentialDecay or keras.optimizers.schedules.PiecewiseConstantDecay: A LearningRateSchedule instance can be passed in as the learning_rate argument of any optimizer. To implement your own schedule object, you should implement the __call__ method, which takes a step argument (scalar integer tensor, the current training step count).

Like for any other Keras object, you can also optionally make your object serializable by implementing the get_config and from_config methods. Instantiates a LearningRateSchedule from its config. Significantly improving your models doesn't take much time – Here's how to get started Tuning neural network models is no joke. There are so many hyperparameters to tune, and tuning all of them at once using a grid search approach could take weeks, even months. Learning rate is a hyperparameter you can tune in a couple of minutes, provided you know how.

This article will teach you how. The learning rate controls how much the weights are updated according to the estimated error. Choose too small of a value and your model will train forever and likely get stuck. Opt for a too large learning rate and your model might skip the optimal set of weights during training. You’ll need TensorFlow 2+, Numpy, Pandas, Matplotlib, and Scikit-Learn installed to follow along. Don’t feel like reading?

Watch my video instead: Join the DZone community and get the full member experience. An open-source software library for artificial intelligence and machine learning is called TensorFlow. Although it can be applied to many tasks, deep neural network training and inference are given special attention. Google Brain, the company's artificial intelligence research division, created TensorFlow. Since its initial release in 2015, it has grown to rank among the most widely used machine learning libraries worldwide.

Python, C++, and Java are just a few of the programming languages that TensorFlow is accessible. Additionally, it works with several operating systems, including Linux, macOS, Windows, Android, and iOS. An effective machine learning and artificial intelligence tool is TensorFlow. It offers a lot of capabilities and is simple to use. TensorFlow is an excellent place to start if machine learning is of interest to you.

Using Learning Rate Schedule And Learning Rate Warmup With Tensorflow2

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