Keras Tuner is a library that can be used to optimize the hyperparameters of a deep learning model, specifically those built using the Keras library. It allows for efficient searching of the hyperparameter space, making it easier to find the best set of hyperparameters for a given model and dataset.
To optimize a CNN model using Keras Tuner, the following steps can be taken:
Define the model architecture: First, the architecture of the CNN model needs to be defined using the Keras API. This includes specifying the number of layers, the type of layers, and the number of neurons in each layer.
Define the hyperparameter search space: Next, the search space for the hyperparameters needs to be defined. This includes specifying the range of values for each hyperparameter, such as the learning rate, the batch size, and the number of filters in the convolutional layers.
Create a Keras Tuner object: A Keras Tuner object can then be created, specifying the model architecture, the hyperparameter search space, and the type of search algorithm to use (such as random search or Bayesian optimization).
Train the model: The Keras Tuner object can then be used to train the model, with the search algorithm automatically tuning the hyperparameters during training.
Select the best model: After training, the Keras Tuner object can be used to select the model with the best performance, based on the chosen metric(accuracy, loss, etc).
Deploy the best model: The best model can then be deployed to a production environment for making
Note - The dataset which I had used here is imported from Tensorflow Library itself.