introduction and Implementation to Keras-tuner

introduction and Implementation to Keras-tuner

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.

hyperparameter-tuning deep-learning keras-tuner tensorflow keras

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