introduction and Implementation to Keras-tuner - Choosing the right set of hyperparameters for your models
Choosing the right set of hyperparameters for your models
When it comes to deep learning, a critical thing to work with is the hyperparameters. To those of you who don’t know, hyperparameters are the variables that govern the structure of your neural network. It could be — but are not limited to — the number of layers, the number of neurons, the learning rate, or the number of epochs. Whenever you create a model in deep learning, the initial model is mostly not perfect unless you hit a home run, and then you optimize the model by tweaking the hyperparameters before you pick another one.
his tutorial is not going to help you to build TensorFlow models, instead will be a guide to using keras-tuner on your existing models.
Let’s take a simple regression problem statement, and construct a very basic model with three fully-connected / dense layers, where you have 16, 32, and 1 unit(s) in the first, second, and the last layer respectively with relu activations. Let’s keep the learning rate 0.001 for the Adam optimizer, and run it on 50 epochs. Below is the code for the same if you want to follow along.
This video on TensorFlow and Keras tutorial will help you understand Deep Learning frameworks, what is TensorFlow, TensorFlow features and applications, how TensorFlow works, TensorFlow 1.0 vs TensorFlow 2.0, TensorFlow architecture with a demo. Then we will move into understanding what is Keras, models offered in Keras, what are neural networks and they work.
Hyperparameter Tuning of Keras Deep Learning Model in Python - This article will give you an overview of how to tune the deep learning model hyperparameters.
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We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs.
This video explains four reasons why deep learning has become so popular in past few years. In this deep learning tutorial python, I will cover following things in this video: Introduction; Data growth; Hardware advancements; Python and opensource ecosystem; Cloud and AI boom