How to tune hyperparameters for keras model?
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How to tune hyperparameters for keras model?

How to tune hyperparameters for keras model?

This recipe helps you tune hyperparameters for keras model

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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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