Visualizing your deep learning features using TensorBoard



Original Source Here

Introduction

TensorBoard is a tool that provides measurements and visualizations needed during machine learning workflow.

In this tutorial, I will give a quick tutorial on how to visualize your feature vectors on TensorBoard. Using a machine learning model, sometimes we want to learn embeddings of different classes. Embeddings are a way to map different inputs like (images, text, etc) to multi-dimensional vectors. Using TensorBoard, you can visualize the representations or feature vectors. You can also visualize the input data directly. TensorBoard also provides dimensionality reduction methods like PCA and t-SNE. You can easily apply PCA and t-SNE to your data with just one click and see visualizations both in 2D and 3D.

What is the Problem Statement?

We have a small dataset of different shapes (circle, triangle, rectangles) of different colors (red, green, blue). Let’s say we want to classify shapes based on colors. So, there are 3 target outputs: red, green, and blue. Using a machine learning model, we have learned a representation/ embedding for colors while classifying them. Now, we want to see how effective those representations are?

So, we will visualize these embeddings on TensorBoard.

Below is the dataset that is used.

Requirements

Note: We will use tensorflow.contrib, that’s why version 1.13.2 is required. In version 2.0, it does not exist anymore.

Implementation

  1. Create a visualization without sprites.

Sprites are blocks of several images arranged in form of a grid. Check out the image below for an example. Sprites make the visualization more impressive and intuitive.

Firstly, I will go through each step to create the code. After this, the complete code is also available. You can directly jump to that if you just want to use it.

Step 1: Firstly, import the libraries.

Step 2: Setup metadata, model, and checkpoint paths.

Step 3: Setup file writer and configurations.

Step 4: Write data to plot in CKPT format

Step 5: Write metadata in TSV format

In the TSV file, labels are written corresponding to the data indices. After this step, we are ready to plot.

Here is the complete code:

Steps to run:

  1. Open terminal and type: python visualize_without_sprite.py
  2. Now, type: tensorboard — logdir #YOUR_LOG_DIR_PATH# — host=127.0.0.1

Now, open the localhost on the web browser. And, you will see TensorBoard now.

Output visualization on TensorBoard is shown below.

AI/ML

Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot



via WordPress https://ramseyelbasheer.wordpress.com/2021/03/07/visualizing-your-deep-learning-features-using-tensorboard/

Popular posts from this blog

Fully Explained DBScan Clustering Algorithm with Python

Image
Original Source Here Fully Explained DBScan Clustering Algorithm with Python Unsupervised learning in machine learning on density-based clusters Clustering In unsupervised Learning. A photo by Author In this article, we will discuss the machine learning clustering-based algorithm that is the DBScan cluster. The approach in this cluster algorithm is density-based than another distance-based approach. The other cluster which is distance-based looks for closeness in data points but also misclassifies if the point belongs to another class. So, density-based clustering is suited in this kind of scenario. The cluster algorithms come in unsupervised learning in which we don’t rely on target variables to make clusters. In a cluster, the main concern is on maximum dense connected points. The density-based clustering can be useful in arbitrary shapes even with noise. The working function of the density-based cluster is shown below: We select a random data point. Then it will ...
Read more

Streamlit — Deploy your app in just a few minutes

Image
Original Source Here Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data… Continue reading on Medium » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/07/11/streamlit%e2%80%8a-%e2%80%8adeploy-your-app-in-just-a-few-minutes/
Read more

Hierarchical clustering explained

Image
Original Source Here Interpretation of dendrogram Each level of dendrogram has a subtle meaning to the relationship between its data members. In a regular relationship chart, one may interpret that at the top lies grandparents or the first generation, the next level corresponds to parents or second generation and the final level belongs to children or third generation. Likewise, in every branching procedure of dendrogram, all the data points having the membership at each level belongs to a certain class. However, to infer this class entity, one has to go through a few individual samples of each level within the formulated cluster and find out what feature is common in the resulting cluster. Also, these inferred classes need not be similar at the sister branches. For example, in level 2, the cats have been clustered on big ears and calm behavior but dogs have been clustered on a similar attribute of size. Inferred classes of data in dendrogram by the user (Image by Author) C...
Read more