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<a href="https://mmhaskell.com/blog/2017/9/11/deep-learning-and-deep-types-tensor-flow-and-dependent-types" target="_blank">In the last</a> <a href="https://www.mmhaskell.com/blog/2017/9/18/checking-its-all-in-place-placeholders-and-dependent-types" target="_blank">couple weeks</a> we explored one of the most complex topics I’ve presented on this blog. We examined potential runtime failures that can occur when using Tensor Flow. These included mismatched dimensions and missing placeholders. In an ideal world, we would catch these issues at compile time instead. At its current stage, the Haskell Tensor Flow library doesn’t support that. But we demonstrated that it is possible to add a layer to do this by using dependent types.

Grenade! Dependently Typed Neural Networks

Last week we dove into the world of dependent types. We linked tensors with their shapes at the type level. This gave our program some extra type safety and allowed us to avoid certain runtime errors.

Checking it’s all in Place: Placeholders and Dependent Types

In the <a href="https://mmhaskell.com/blog/2017/8/7/the-future-is-functional-haskell-and-the-ai-native-world" target="_blank">introduction to this series</a>, one primary point I made was that Haskell is a safe language. There are a lot of errors we will catch at compile time, rather than runtime. Runtime errors can often be catastrophic to a system, so being able to reduce these is paramount. This is especially true when programming an autonomous car or drone. These objects will be out in the real world where they can hurt people if they malfunction.

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