The Learning rate is arguably the most important hyperparameter in TensorFlow models. Keeping the learning rate the same throughout the model training may work but not always. If you notice any curve for losses in the training periods, it is normal for the model to learn really fast in the beginning and very slow at the end. So, models learn at different rates in the different stages of training. So, it makes sense to set different learning rates for the different stages of learning.
In this tutorial, we learn how to develop the learning rate scheduler and use it in the model. We work on three different types of learning rate schedulers in detail. There are many more different types.
Here you will find the TensorFlow documentation on the learning rate scheduler. Please feel free to check:
www.tensorflow.org/api_docs/p...
The google colab notebook used in this tutorial can be found here:
github.com/rashida048/TensorF...
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