You must have used a neural network for training a model on your data. There are so many types of architectures of neural networks that you can use to train a model, but have you ever visualized the architecture of a neural network? If you want to learn how to visualize a neural network, this article is for you. In this article, I’ll walk you through how to visualize a neural network using Python.
How to Visualize a Neural Network?
There are many tools you can use for visualizing the architecture of a neural network. Some of the best tools for visualizing a neural network are:
Out of all the tools mentioned above, in my opinion, using VisualKeras is the easiest approach for visualizing a neural network. So in the section below, I’m going to introduce you to a tutorial on how to visualize neural networks with Visualkeras using the Python programming language.
Visualize a Neural Network using Python
Before visualizing the architecture of a neural network, we must first design a neural network. If you have ever worked on a problem using a neural network, you can skip this part and learn how to visualize the architecture of your neural network as shown in the next section below. If you’ve never designed a neural network, here’s how we can design the architecture of a convolutional neural network:
So after designing the architecture of a neural network, the next task is to install the visualkeras library in your system. You can easily install it by using the pip command:
- pip install visualkeras
Now here is how we can visualize the architecture of our neural network:
import visualkeras visualkeras.layered_view(model)
This is how we can easily visualize the architecture of neural networks using the programming language Python. Hope this tutorial helps you to design and visualize some interesting neural networks and hope you will explore the architecture of more neural networks. I hope you liked this article on how to visualize neural networks using Python. Please feel free to ask your valuable questions in the comments section below.