Recently came across this library keras tuner it will develop a neural network with maximum accuracy by tweaking the hyperparameters on its own.
Keras Tuner is a hyperparameter tuning library for Keras, which is a high-level neural networks API written in Python and runs on top of TensorFlow, Theano, or CNTK. Keras Tuner is designed to automate the process of finding the best set of hyperparameters for a given deep learning model.
Hyperparameters are parameters that are not learned by the model during training, but are set before training and define aspects of the model architecture and training process. Examples of hyperparameters include learning rate, number of layers, number of neurons in each layer, dropout rate, and batch size.
Keras Tuner provides a way to define a search space for hyperparameters and explore different combinations of hyperparameters automatically. This search space can be defined using various types of hyperparameters, such as fixed values, ranges, and choice of values. Keras Tuner uses techniques such as random search, grid search, and Bayesian optimization to explore the search space and find the best set of hyperparameters.
Keras Tuner can be used for hyperparameter tuning in convolutional neural networks (CNNs) as well. CNNs are a type of deep neural network commonly used for image classification and computer vision tasks.
When using Keras Tuner with a CNN, the hyperparameters that can be tuned include:
- Number of convolutional layers
- Number of filters in each convolutional layer
- Size of the filters in each convolutional layer
- Activation function for the convolutional layers
- Pooling type and size
- Number of dense layers
- Number of neurons in each dense layer
- Dropout rate for the dense layers
- Learning rate for the optimizer
To use Keras Tuner with a CNN, first, define the model architecture using the Keras API. Next, create a hypermodel using the Keras Tuner API, specifying the search space for each hyperparameter. Then, use the hypermodel to search for the best set of hyperparameters using techniques such as random search, grid search, or Bayesian optimization. Finally, train and evaluate the model using the best set of hyperparameters found by the tuner.
Keras Tuner also provides support for distributed hyperparameter tuning, which allows for faster exploration of the search space by parallelizing the search across multiple machines or GPUs. Overall, Keras Tuner can be a powerful tool for improving the performance of CNNs by finding the optimal set of hyperparameters for a given task.