Dnngraph

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dnngraph is an open-source DSL for deep neural networks, supporting Caffe and Torch. It allows users to specify, optimize, and generate code for neural network models.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is dnngraph?

dnngraph is a Domain Specific Language (DSL) implemented in Haskell for defining and generating deep neural network models. It leverages the lens library for composable constructions and the fgl graph library for network layout specification. The tool includes optimization passes to enhance model performance, such as in-place operations for certain layer types. dnngraph supports generating code for popular deep learning frameworks like Caffe (via prototxt files) and Torch (via Lua scripts). It also provides command-line interface (CLI) tools for exporting, visualizing, and understanding network structures, making it a comprehensive solution for researchers and engineers working with neural network architectures.

Best used for

Ideal for developers and data scientists who need to programmatically define deep neural network architectures, optimize their performance, and generate compatible code for frameworks like Caffe and Torch. Especially valuable for those requiring fine-grained control over model specification and visualization.

Common actions

define neural networks

generate deep learning models

optimize network performance

visualize model architecture

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Capabilities

Key features

DSL for model specification
Graph-based network layout
Optimization passes
Caffe code generation
Torch code generation
Network visualization

Target Audience

developerdata scientist

Integrations

caffetorch

Pricing & Plans

Open Source

Free

FAQs

What programming language is dnngraph built with?

dnngraph is primarily built using Haskell, a functional programming language. It leverages Haskell's features for defining the DSL and managing the graph-based representation of neural networks, providing a robust and expressive environment for model specification.

Can dnngraph generate code for frameworks other than Caffe and Torch?

Currently, dnngraph explicitly supports code generation for Caffe (via prototxt files) and Torch (via Lua scripts). While the DSL is flexible, direct support for other deep learning frameworks would require additional backend implementations.

How can I visualize the neural network structure defined with dnngraph?

dnngraph includes a visualization module, NN.Visualize, which can generate graphical representations of your network. You can use CLI commands like `runhaskell NN/Examples/GoogLeNet.hs visualize --format pdf --output /tmp/x.pdf` to create visual outputs such as PDF files.

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