Hyperparameter tuning for Deep Learning with scikit-learn, Keras, and TensorFlow
Hyperparameter tuning for Deep Learning with scikit-learn, Keras, and TensorFlow – PyImageSearch
“To tune the hyperparameters of a neural network, we first need to define the model architecture. Inside the model architecture, we’ll include variables for the number of nodes in a given layer and dropout rate.
We’ll also include the learning rate for the optimizer itself.
This model, once constructed, will be returned to the hyperparameter tuner. The tuner will then fit the neural network on our training data, evaluate it, and return the score.
After all trials are complete, the hyperparameter tuner will tell us which hyperparameters provided the best accuracy…”