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Category: Machine Learning

#008 TF An implementation of a Convolutional Neural Network in tf.keras – MNIST dataset

In this post we will see how we can classify handwritten digits using convolutional neural network implemented in Keras. Required packages: Numpy Matplotlib Keras Tensorflow Sklearn Seaborn __future__ Keras-vis Table of Contents: Load the digit dataset Implementing a Neural Network Visualization Test our model Save a model as picture Activation Maps Saliency maps Activations Now we can plot some predictions, to see how our model works. Images for testing can be downloaded from the internet…
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#008 TF 2.0 An implementation of a Shallow Neural Network in tf.keras – digits dataset

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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#004 TF 2.0 TensorFlow Wrappers

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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#003 TF 2.0 Eager Execution- A Pythonic way of using TensorFlow

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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#002 TF 2.0 An Introduction to TensorFlow 2.0

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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