Highlights: In this post we will learn what is TensorBoard and how to use it. For most people, neural networks can sometimes be a bit of black box. Debugging problems is also a lot easier when we can see what the problem is. Thankfully, TensorBoard is a tool that will help us to analyze neural networks and to visualize learning. Tutorial Overview: Sequential API Model Subclassing Intro The idea of TensorBoard is to help to…
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In this post we will see how we can classify handwritten digits using shallow neural network implemented with tf.keras. Table of Contents: Load the digit dataset Implementing a Neural Network Visualization and Testing 1. Load the digits dataset First, let us import all necessary libraries. After imports, we can use imported module to load digits data. The load_digits() function will just download data and we need to split it into train and test sets. We can also…
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Highlights: In this post we are going to talk more about TensorFlow Wrappers. We are going to compare things before and after TensorFlow 2.0. This post will be the introductory one to the series of posts where we are going to build a wide variety of neural networks. To use TensorFlow in our projects, we need to learn how to program using the TensorFlow API. TensorFlow has multiple APIs that can be used to interact with…
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TensorFlow uses Eager execution, which is a more convenient way to execute the code, and also more “Pythonic”. It is a default choice in the latest version TensorFlow 2.0. In TensorFlow 1.x, we first need to write a Python program that constructs a graph for our computation, the program then invokes Session.run(), which hands the graph off for execution to the C++ runtime. This type of programming is called declarative programming (specification of the computation…
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Highlights: In this post we are going to talk more about what are TensorFlow data model elements. Those are elements like Constants and Variables. So let’s see how we can create an operation like \(c = a*b\) and run it with the following lines of code. TensorFlow data model elements There are certain programming elements in TensorFlow that are essential for writing any TensorFlow code like Constants and Variables. These data model elements are used…
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