Hyperparameter Tuning with Keras Tuner _ Day 17

A Comprehensive Guide to Hyperparameter Tuning with Keras Tuner Introduction In the world of machine learning, the performance of your model can heavily depend on the choice of hyperparameters. Hyperparameter tuning, the process of finding the optimal settings for these parameters, can be time-consuming and complex. This guide will walk you through the essentials of hyperparameter tuning using Keras Tuner, helping you build more efficient and effective models. Why Hyperparameter Tuning Matters Hyperparameters are critical settings that can influence the performance of your machine learning models. These include the learning rate, the number of layers in a neural network, the number of neurons per layer, and many more. Finding the right combination of these settings can dramatically improve your model’s accuracy and efficiency. Introducing Keras Tuner Keras Tuner is an open-source library that provides a streamlined approach to hyperparameter tuning for Keras models. It supports various search algorithms, including random search, Hyperband, and Bayesian optimization. This tool not only saves time but also ensures a systematic exploration of the hyperparameter space. Step-by-Step Guide to Using Keras Tuner 1. Define Your Model with Hyperparameters Begin by defining a model-building function that includes hyperparameters: import keras_tuner as kt import tensorflow as tf...

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Sequential vs Functional Keras API Part 2 explanation _ Day 15

Keras API Example Let’s continue from day 14 which we explained the 3 Keras API types and compare them Understanding Sequential vs. Functional API in Keras with a Simple Example When building neural networks in Keras, there are two main ways to define models: the Sequential API and the Functional API. In this post, we’ll explore the differences between these two approaches using a simple mathematical example. Sequential API The Sequential API in Keras is a linear stack of layers. It’s easy to use but limited to single-input, single-output stacks of layers. Here’s a simple example to illustrate how it works. Objective: Multiply the input $x$ by 2. Add 3 to the result. Let’s implement this using the Sequential API: from keras.models import Sequential from keras.layers import Lambda # Define a simple sequential model model = Sequential() model.add(Lambda(lambda x: 2 * x, input_shape=(1,))) model.add(Lambda(lambda x: x + 3)) model.summary() Functional API The Functional API in Keras is more flexible and allows for the creation of complex models with multiple inputs and outputs. We’ll use the same mathematical operations to illustrate how it works. Objective: Multiply the input $x$ by 2. Add 3 to the result. Mathematical Operations: $y_1 = 2...

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