Computational graphs in PyTorch and TensorFlow

Computational graphs in PyTorch and TensorFlow

In this article, I explain about static vs dynamic computational graphs and how to construct them in PyTorch and TensorFlow. This is the magic that allows these frameworks to calculate gradients for your neural networks.

I had explained about the back-propagation algorithm in Deep Learning context in my earlier article. This is a continuation of that, I recommend you read that article to ensure that you get the maximum benefit from this one.

I’ll cover computational graphs in PyTorch and TensorFlow. This is the magic that allows these frameworks to calculate gradients for your neural networks. I’ll start with some introduction to types of computational graphs followed by framework specific details.

Computational Graphs Types[1]

All the deep learning frameworks rely on the creation of computation graphs for the calculation of gradient values required for the gradient descent optimization. Generally, you have to build the forward propagation graph and the framework takes care of the backward differentiation for you.

But before starting with computational graphs in PyTorch, I want to discuss static and dynamic computational graphs.

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