How to tune hyperparameters for keras model?

How to tune hyperparameters for keras model?

How to tune hyperparameters for keras model?

This recipe helps you tune hyperparameters for keras model


Recipe Objective

How to tune hyperparameters for keras model.

The process of selecting the right hyperparameters in a Deep Learning or Machine Learning Model is called hyperparameter tuning.

Hyperparameters are the variables that control the training of the dataset, they have a huge impact on the learning of the model, and tuning of these hyperparameters controls the accuracy.

Step 1- Importing Libraries.

import tensorflow as tf from tensorflow import keras import kerastuner as kt from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import Layer #!pip install -U keras-tuner

Step 2- Creating a function.

Create a function for hypertuning, defining all the hyper parameters

def model_tune(new): model = keras.Sequential() model.add(keras.layers.Flatten(input_shape=(28, 28))) units = new.Int('units', min_value = 16, max_value = 128, step = 16) model.add(keras.layers.Dense(units = units, activation = 'sigmoid')) model.add(keras.layers.Dense(10)) learning_rate = new.Choice('learning_rate', values = [1e-1,1e-2, 1e-3, 1e-4]) model.compile(optimizer = keras.optimizers.Adam(learning_rate = learning_rate), loss = keras.losses.SparseCategoricalCrossentropy, metrics = ['mse']) return model

Step 3-Instantiating the hypertuning model.

We will instantiate the hypertuning model here.

tuning = kt.Hyperband(model_tune, objective = 'val_accuracy', max_epochs = 100, factor = 3, directory = 'my_dir', project_name = 'learning_hyperparameter') print(tuning)

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