{"id":8064,"date":"2023-11-02T09:15:23","date_gmt":"2023-11-02T17:15:23","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8064"},"modified":"2025-04-24T17:04:59","modified_gmt":"2025-04-24T17:04:59","slug":"how-to-log-your-keras-deep-learning-experiments-with-comet","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/how-to-log-your-keras-deep-learning-experiments-with-comet\/","title":{"rendered":"How to Log Your Keras Deep Learning Experiments With Comet"},"content":{"rendered":"\n\n\n\n\n
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Image<\/a> by rawpixel.com<\/a> on Freepik<\/a><\/figcaption><\/figure>\n

Overview<\/h1>\n

Let us start by asking ourselves some questions: Have you ever wondered how Google\u2019s translation app can instantly convert entire paragraphs between two languages?<\/p>\n

How do Netflix and YouTube know what movies or videos we like, and how do they provide suitable recommendations? Or how do autonomous vehicles even become a possibility?<\/p>\n

Other practical examples of deep learning include virtual assistants, chatbots, robotics, image restoration, NLP (Natural Language Processing), and so on. Now that we have our questions let us start providing answers to them.<\/p>\n

This article will discuss deep learning using Keras and, most importantly, how we will log our models to Comet.<\/p>\n

The code for this tutorial is based on this notebook<\/a> by Ken Jee from the Z by HP Unlocked Challenge 4: Image Classification<\/a>.<\/p><\/blockquote>\n

What is Deep Learning?<\/h1>\n

Experience is the best teacher. The more we learn, the richer our experiences. The same is true for machines running AI hardware and software in the deep learning field of artificial intelligence (AI). The data that machines gather defines the experiences through which they can learn, and the quantity and quality of data determine how much they can learn.<\/p>\n

A branch of machine learning is deep learning<\/strong>. Deep learning systems can perform better with access to more data, which is the machine equivalent of more experience, in contrast to typical machine learning algorithms, many of which have a finite ability to learn regardless of the amount of data they obtain. Machines may be trained to perform specific activities such as driving a car, spotting weeds in a field of crops, diagnosing illnesses, checking machinery for flaws, and other jobs once they have acquired sufficient experience through deep learning.<\/p>\n

Neural networks are motivated by the human brain\u2019s organization. By repeatedly examining data according to a predetermined logical framework, deep learning computers try to reach the same conclusions as people. Deep learning does this via a multi-layered neural network algorithmic framework.<\/p>\n

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A typical neural network; image from Artem Oppermann<\/a><\/figcaption>\n<\/figure>\n

We will be using Keras to build a CNN-based image classifier.<\/p>\n

Keras is a user-friendly toolbox that substantially reduces the access hurdle to deep learning research and development. The Keras team purposefully incorporated the minimal entry barrier into its architecture to democratize machine learning. CNN, a subset of deep neural networks made up of several layers of artificial neurons, are frequently used to evaluate visual information.<\/p>\n

Keras<\/h1>\n

Keras<\/a>, an open-source, deep-learning library, was developed by Francois Chollet, a deep-learning researcher at Google. With Keras, users may rapidly translate code into a product because of its user-friendly design principles. This indicates that it was created following a set of criteria that aims to make it effective, dependable, and available to a broad audience. It has several uses in both business and academics as a result. It also offers comprehensive developer instructions.<\/p>\n

Convolutional neural networks (CNNs<\/strong>) are a subtype of artificial neural networks that have been popular in several applications linked to computer vision and are attracting interest in other domains.<\/p>\n

Convolution, pooling, and fully connected layers are just a few components that make up a convolutional neural network. Using a back propagation approach, it is designed to automatically and adaptively learn spatial hierarchies of features.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Introducing the Comet AI art gallery \u2014 a public forum to log experiments, test different parameters, and share your AI-generated art! Learn more about our integration with Gradio to create this one-of-a-kind space<\/a>.<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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Prerequisites<\/h2>\n

To continue this article, we must install the following on our local machine.<\/p>\n