Skip to main content

How to use TensorBoard with TensorFlow 2.0 in Google Colaboratory?


Another post starts with you beautiful people!
It is quite a wonderful moment for me that many Aspiring Data Scientists like you have connected with me through my facebook page and have started their focused journey to be a Data Scientists by following my book. If you have not then I recommend to atleast visit my last post here.

In two of my previous posts we have learnt about keras and colab. In this post I am going to share with you all that TensorFlow 2.0 has been released and one quite interesting news about this release is that our beloved deep learning library keras is in built with it. Yes! You heard it right. If you know keras then using TensorFlow 2.0 library is quite easy for you. One of the interesting benefit of using TensorFlow library is it's visualization tool known as TensorBoard. In this post we are going to learn how to use TensorFlow 2.0 with MNIST dataset and then setup TensorBoard with Google Colaboratory.

Let's start this post learning by opening a new notebook in Google Colab and install TensorFlow 2.0 library as below-

This will take a minute and will install the TensorFlow 2.0 library in your notebook. After installing the library we can import this as below- 

Next, we will load the MNIST dataset from tensorflow keras library then split the dataset as training and test dataset and then we will normalize it like below- 

Now we will visualize the performance of our model in TensorBoard. For this first we will use keras.callbacks.TensorBoard function like below-
Here see the parameters we are passing in TensorBoard function./log folder is the location where tensorflow will create logs and other parameters are boolean flags with true values which allow us to visualize different types of properties in TensorBoard.

Next, we will define our sequential keras model and then compile it like below-

After compiling the model we will train and evaluate our model like below-

Our model has accuracy of 98%. Since TensorBoard runs on a server, we will learn the tricky part- how to access a localhost server from our colab notebook? For this purpose we will use ngrok. ngrok is a cross-platform application that enables developers to expose a local development server to the Internet with minimal effort. We will download ngrok like below- 

After downloading and installing ngrok in colab, we will fire up Tensorboard using ngrok like below-

Now we will start the server with ngrok like below-
Once your server is up, it will display the url like in above output cell. Copy this url and access it in a browser. You will see your TensorBoard page like below-

See the beautiful histograms showing in your board. Now explore it more by clicking on different tabs. Here I am seeing distributions and screen is like below-

If you click on the first tab- Scalars, you will see the performance of our model like below-

You can also get whole code as a notebook from this link.
That's it! with above steps you are able to setup TensorBoard with TensorFlow 2.0 in your colab notebook. We can use TensorBoard to visualize our TensorFlow graph, plot quantitative metrics about the execution of our graph, and show additional data like images that pass through it. This visualization tool is very flexible and useful so don't wait and explore as much as you can. Till then Go chase your dreams, have an awesome day, make every second count and see you later in my next post.











Labels:

Comments

  1. yugandarApril 16, 2019 at 10:45 AM

    It was really a nice article and i was really impressed by reading this, Thanks for sharing this information. Career3s provides Python training with real time experts. please go through our website once python Training

    ReplyDelete
  2. Raj SharmaJuly 12, 2019 at 12:48 PM

    Awesome post. You Post is very informative. Thanks for Sharing.
    Data Science course in Noida

    ReplyDelete
  3. santhosh kumarJuly 23, 2019 at 1:47 PM

    Thank for sharing this beautiful blog on Tensor board, well explained. keep sharing more like this.
    Scope of data science

    ReplyDelete
  4. TechnogeekscsNovember 19, 2019 at 6:18 PM

    Very good post thank for sharing it.
    Data Analysis Courses in Pune

    ReplyDelete
  5. high technologies solutionsNovember 29, 2019 at 10:45 AM


    Nice post,thanks for posting
    Python training course in delhi
    Python training course in Noida

    ReplyDelete

Post a Comment

Popular posts from this blog

How to deploy your ML model as Fast API?

Another post starts with you beautiful people! Thank you all for showing so much interests in my last posts about object detection and recognition using YOLOv4. I was very happy to see many aspiring data scientists have learnt from my past three posts about using YOLOv4. Today I am going to share you all a new skill to learn. Most of you have seen my post about  deploying and consuming ML models as Flask API   where we have learnt to deploy and consume a keras model with Flask API  . In this post you are going to learn a new framework-  FastAPI to deploy your model as Rest API. After completing this post you will have a new industry standard skill. What is FastAPI? FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. It is easy to learn, fast to code and ready for production . Yes, you heard it right! Flask is not meant to be used in production but with FastAPI you can use you...
51 comments
Read more

How to install and compile YOLO v4 with GPU enable settings in Windows 10?

Another post starts with you beautiful people! Last year I had shared a post about  installing and compiling Darknet YOLOv3   in your Windows machine and also how to detect an object using  YOLOv3 with Keras . This year on April' 2020 the fourth generation of YOLO has arrived and since then I was curious to use this as soon as possible. Due to my project (built on YOLOv3 :)) work I could not find a chance to check this latest release. Today I got some relief and successfully able to install and compile YOLOv4 in my machine. In this post I am going to share a single shot way to do the same in your Windows 10 machine. If your machine does not have GPU then you can follow my  previous post  by just replacing YOLOv3 related files with YOLOv4 files. For GPU having Windows machine, follow my steps to avoid any issue while building the Darknet repository. My machine has following configurations: Windows 10 64 bit Intel Core i7 16 GB RAM NVIDIA GeForce G...
45 comments
Read more

Machine Learning-Linear Regression

Another post starts with you beautiful people! In my previous posts we have learnt the Python basics and advanced, statistics techniques for the Data Science track. I suggest you to please read previous post just for 10-15 min. before sleeping daily and then there is no any obstacle to stop you to become a great Data Scientist. In this post we will start our Machine Learning  track with the  Linear Regression   topic. I Have highlighted the both so please click on the link to know the formal definition of those. Machine learning-  More specifically the field of predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability. In applied machine learning we will borrow, reuse and steal algorithms from many different fields, including statistics and use them towards these ends. Linear Regression was developed in the field of statistics and is studied as a model ...
3 comments
Read more