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Tag: Keras

#014 TF Implementing LeNet-5 in TensorFlow 2.0

Highlights: In this post we will show how to implement a foundamental Convolutional Neural Network like \(LeNet-5\) in TensorFlow. The LeNet-5 architecture was invented by Yann LeCun in 1998 and was the first Convolutional Neural Network. Tutorial Overview: Theory recapitulation Implementation in TensorFlow 1. Theory recapitulation The goal of \(LeNet-5 \) was to recognize handwritten digits. So, it takes as an input \(32\times32\times1 \) image. It is a grayscale image, thus the number of channels is \(1 \).…
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#009 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 TensorFlow 2.0. Required packages: Numpy Matplotlib Tensorflow Sklearn Seaborn Table of Contents: Load the digit dataset Implementing a Neural Network Visualization and Testing 1. Load the digit dataset Let start with importing all necessary libraries. After imports, we can use imported module to load mnist data. The load_data() function will automatically download and split our data into…
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#007 TF 2.0 An implementation of a Shallow Neural Network in tf.keras – Spirals dataset

Highlights: In this post we will see how we can classify a spirals dataset with a shallow neural network implemented in TensorFlow 2.0. Tutorial Overview: Imports and Dataset preparation Building a Neural Network Visualization 1. Dataset: import and preparation Let’s start by importing libraries that we will need in our code. Last time we were using a function from sklearn to create a dataset. Now we are going to make our dataset from scratch. First,…
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#006 TF 2.0 An implementation of a Shallow Neural Network in tf.keras – Moons dataset

In this post we will learn how to make a classification of Moons dataset with a shallow Neural network. The Neural Net we will implemented in TensorFlow 2.0 using Keras API. With the following code we are going to import all libraries that we will need. First, we will generate a random dataset, then we will split it into train and test set. We will also print dimensions of these datasets. With the following code…
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#011 TF YOLO V3 Object Detection in TensorFlow 2.0

YOU ONLY LOOK ONCE Highlights: Prior to Yolo majority of approaches for object detection tried to adapt the classifiers for the purpose of detection. In YOLO, an object detection has been framed as a regression problem to spatially separated bounding boxes and associated class probabilities. In this post we will learn about the YOLO Object Detection system, and how to implement such a system in TensorFlow 2.0. About Yolo:Our unified architecture is extremely fast. Our…
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