A simple CNN In TensorFlow: Practical CIFAR-10 Guide

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Overview

Do you want to learn how to develop a powerful convolutional neural network (CNN) in TensorFlow that can recognize images from 10 different categories? If this is the case, then you are in the right place. In this article, I will help you to create and train a CNN in TensorFlow to classify the CIFAR-10 dataset into different categories.

The CIFAR-10  is a popular dataset in deep learning, consisting of 60,000 colorful images. The images belong to ten categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. The aim is to develop a CNN in TensorFlow to classify images into different categories.

For those who are not aware of what CNNs are, they are a type of deep learning algorithm involving multiple layers of filters to extract important features from the input images. They are one of the most effective deep-learning models for recognizing patterns and shapes in images, and they have achieved state-of-the-art results on many image recognition tasks. If you want to know more about CNN, you can check our previous tutorial on CNN, which discusses its associated theories.

In this article, you will learn how to:

• Load and preprocess the CIFAR-10 dataset using TensorFlow

• Build a CNN in TensorFlow from scratch by using the KERAS API

• Train and evaluate the performance of the model on the training and test datasets

• Explore some ways to improve the model’s performance and accuracy

By developing a CNN in TensorFlow, one can also benefit from the rich documentation and community support that TensorFlow provides.  Once you finish the article, you will have a solid understanding of the practical implementation of CNN in TensorFlow. So, let’s develop your first CNN model in TensorFlow for image classification.

What Is TensorFlow?

TensorFlow is an open-source software library developed by researchers at Google for running machine learning and artificial intelligence applications. 

We can develop a CNN model in TensorFlow for various tasks such as image classification, detection, and segmentation. There are various approaches to developing a CNN in TensorFlow, such as the Keras Sequential API, the Functional API, or the low-level TensorFlow Core API.

TensorFlow has high-level APIs, such as the Keras and tf. Layers, and using them, we can quickly build and train CNNs with just a few lines of code. It also supports low-level APIs that offer a lot of flexibility and customization of CNNs, defining loss functions, optimizers, and custom layers.

TensorFlow also offers many pre-trained CNN models, such as VGG-16, ResNet50, Inceptionv3, and EfficientNet1. These pre-trained models can be easily fine-tuned and used as feature extractors for new tasks.

Understanding The CIFAR-10 Dataset

CIFAR-10 is a dataset of 60,000 color images collected in 2009 by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. The dataset is popular for evaluating and comparing different image classification models, such as convolutional neural networks (CNNs), residual networks, dense networks, and capsule networks. Using the CIFAR-10 dataset, we can develop our own CNN in TensorFlow for image classification. 

The dataset is split into 50,000 color images of 32×32 pixels for training and 10,000 color images of 32×32 pixels for testing.

Each image of the dataset is an array of 3072 elements, representing 32×32 pixels of the red, green, and blue channels. The dataset is divided into ten classes: airplane [0], automobile [1], bird [2], cat [3], deer [4], dog [5], frog [6], horse [7], ship [8], and truck [9].

The dataset also permits various data augmentation techniques such as cropping, flipping, rotating, and adding noise. These augmentation techniques can be used to improve the generalization and robustness of the models. The figure below illustrates the various classes of images in the CIFAR-10 dataset.

Practical Implementation Of CNN In TensorFlow

Here, we will develop a CNN in TensorFlow for recognizing images in the CIFAR-10 Dataset. You can find the source code here: CNN in Tensorflow.

Import Libraries

Data Processing

Data processing is an essential step to develop aCNN in Tensorflow. It involves the following steps.

Load Data

Normalize The Data

The normalization reduces the variance and skewness of the data, which helps the model to learn fast, improves generalization ability, and enhances model performance on unseen data. Here, we will divide the pixels of the images by 255  to scale them within the range [0,1].

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