How to Create a Residual Network in TensorFlow and Keras

The code with an explanation is available at GitHub.

ResNet, was first introduced by Kaiming He[1]. If you are not familiar with Residual Networks and why they can more likely improve the accuracy of a network.

While creating a Sequential model in Tensor flow and Keras is not too complex, creating a residual network might have some complexities. In this article, I show you how to create a residual network from scratch.

Summary:

• Task type: classifying handwritten digits.
• Dataset: THE MNIST DATABASE
• Network Architecture: a small residual network shown in Figure 1.
• Optimizer: Adam
• Loss function: categorical_crossentropy

Code directory structure:

• main.py
• train.py
• network_model.py

#machine-learning #deep-learning #keras #tensorflow #neural-networks

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Keras vs. Tensorflow - Difference Between Tensorflow and Keras

Keras and Tensorflow are two very popular deep learning frameworks. Deep Learning practitioners most widely use Keras and Tensorflow. Both of these frameworks have large community support. Both of these frameworks capture a major fraction of deep learning production.

Which framework is better for us then?

This blog will be focusing on Keras Vs Tensorflow. There are some differences between Keras and Tensorflow, which will help you choose between the two. We will provide you better insights on both these frameworks.

What is Keras?

Keras is a high-level API built on the top of a backend engine. The backend engine may be either TensorFlow, theano, or CNTK. It provides the ease to build neural networks without worrying about the backend implementation of tensors and optimization methods.

Fast prototyping allows for more experiments. Using Keras developers can convert their algorithms into results in less time. It provides an abstraction overs lower level computations.

Major Applications of Keras

• The performance of Keras is smooth on both CPU and GPU.
• Keras provides modularity, flexibility to code, extensibility, and has an adaptation for innovation and research.
• The pythonic nature of Keras makes it easy to explore and debug the code.

What is Tensorflow?

Tensorflow is a tool designed by Google for the deep learning developer community. The aim of TensorFlow was to make deep learning applications accessible to the people. It is an open-source library available on Github. It is one of the most famous libraries to experiment with deep learning. The popularity of TensorFlow is because of the ease of building and deployment of neural net models.

Major area of focus here is numerical computation. It was built keeping the processing computation power in mind. Therefore we can run TensorFlow applications on almost kind of computer.

Major applications of Tensorflow

• From mobiles to embedded devices and distributed servers Tensorflow runs on all the platforms.
• Tensorflow is the enterprise of solving real-world and real-time problems like image analysis, robotics, generating data, and NLP.
• Developers are implementing tools for translation languages and the detection of skin cancers using Tensorflow.
• Major projects using TensorFlow are Google translate, video detection, image recognition.

#keras tutorials #keras vs tensorflow #keras #tensorflow

How to Create a Residual Network in TensorFlow and Keras

The code with an explanation is available at GitHub.

ResNet, was first introduced by Kaiming He[1]. If you are not familiar with Residual Networks and why they can more likely improve the accuracy of a network.

While creating a Sequential model in Tensor flow and Keras is not too complex, creating a residual network might have some complexities. In this article, I show you how to create a residual network from scratch.

Summary:

• Task type: classifying handwritten digits.
• Dataset: THE MNIST DATABASE
• Network Architecture: a small residual network shown in Figure 1.
• Optimizer: Adam
• Loss function: categorical_crossentropy

Code directory structure:

• main.py
• train.py
• network_model.py

#machine-learning #deep-learning #keras #tensorflow #neural-networks

Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.

Introduction to Keras

Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.

Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.

Characteristics of Keras

Keras has the following characteristics:

• It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
• Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
• Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
• It offers a consistent API that provides necessary feedback when an error occurs.
• Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

Benefits of using Keras

The following major benefits of using Keras over other deep learning frameworks are:

• The simple API structure of Keras is designed for both new developers and experts.
• The Keras interface is very user friendly and is pretty optimized for general use cases.
• In Keras, you can write custom blocks to extend it.
• Keras is the second most popular deep learning framework after TensorFlow.
• Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Keras Installation

Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.

Starting with Keras

The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

How to Create a Vanilla Neural Network with Tensorflow

Nowadays, Tensorflow is a highly demanded skill in the market, ensuring ease of production, standardizing some crucial stages of Machine Learning.

Today you’ll learn how to make your first neural network with Tensorflow; We’re going to build a Multilayer Perceptron model, also called the “Vanilla” Neural Network. Are you ready? So let’s start!

Tensorflow is a platform that focuses on Machine Learning, providing tools to create models for different ecosystems; With this, you can run Machine Learning models on mobile devices, cloud, servers, and more. Let’s check out the definition of Tensorflow by Google:

“TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.”

It’s important to know that Tensorflow provides libraries and extensions for several situations; One of the most used is the Tensorflow Hub, which allows Machine Learning Engineers to reuse already trained models to train new ones, using the Transfer Learning method.

What is a Neural Network?

Generally, the easiest way to explain them is to say that they’re related to neurons in the animal brain but created by engineers, using the name Artificial Neural Network (ANN).

Designed to recognize patterns, they act like neurons, triggering when one is detected. Artificial Neural Networks are based on the animal’s Central Nervous System but not identical, just similar. It’s a common mistake when talking about neural networks.

Neural networks are composed of input, hidden and output layers; The Input Layer is the first, where our data is added; The hidden layers are the “neurons” that adjust the weight and bias for each epoch; Finally, it’s in the output layer that we receive a response from our model, based on the target.

Epoch is the number of times our Machine Learning model pass through the entire training data; Inside each epoch, the weight and bias are computed in order to make our model find better patterns.

If you’re new to Neural Networks, check out this Google playground; You can modify the number of epochs, activation, learning rate, and more (without breaking your model).

The Multilayer Perceptron

Today we will be building a Multilayer Perceptron (MLP), also called the “Vanilla” Neural Network; **By default, this type of MLP has only a single hidden layer **and looks like the gif above.

Being one of the simplest types of neural networks, the Multilayer Perceptron is perfect to understand how a neural network works and each step of the entire process.

The Google Colaboratory

We’ll use Google Colab, a tool based on the Jupyter Notebook that allows data scientists to work quickly and intelligently. By default, Google Colab comes with the necessary packages to work with Data Science and Machine Learning.

Also, it is a great tool for anyone who focuses on performance: Google offers a GPU that speeds up processes. Simply put,** you don’t have to worry about installing packages, machine memory, or backup**, Google saves your progress to your Google Drive account.

Structure of Today’s Project

To make it easier, we’ll work on a regression data; I’ll just focus on creating our Multilayer Perceptron, but you can click here to check out other steps, like Data Cleaning and Feature Engineering, for example.

About our Data

We’ll use the “Auto MPG” data provided by UCI, click here to take a look. We’ll predict the miles per gallon (MPG) parameter based on 7 different features.

#neural-networks #keras #machine-learning #data-science #tensorflow

Implementing Neural Networks with Keras and Tensorflow

In the previous posts of this series, we covered the following topics —

1. Artificial Neural Networks — Concepts behind ANNs
2. Gradient Descent
   This post will cover the implementation of a basic neural network using Tensorflow and Keras.

#neural-networks #tensorflow #keras #deep-learning