Search Results for "flux"

Flux is an elegant approach to machine learning. It's a 100% pure Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing and fusing kernels for the GPU, for the best performance. Existing Julia libraries are differentiable and can be incorporated directly...

Metalhead.jl provides standard machine learning vision models for use with Flux.jl. The architectures in this package make use of pure Flux layers, and they represent the best practices for creating modules like residual blocks, inception blocks, etc. in Flux. Metalhead also provides some building blocks for more complex models in the Layers module.

ONNX.jl is in the process of a total reconstruction and currently supports saving & loading graphs as a Umlaut.Tape. When possible, functions from NNlib or the standard library are used, but no conversion to Flux is implemented yet. See resnet18.jl for a practical example of graph loading.

CliMA Land is a next generation Land Surface Model (LSM) designed to use the broadly available remote sensing data as well as ground-based flux measurements. CliMA Land is a highly modular platform to promote research at different scales from tissue to organ, whole plant, and ecosystem. Therefore, we deliver CliMA Land via a patch of packages (also referred to as sub-modules) to reduce the time used to initialize a research project. As a result, the repository is more a collection of tutorials...

Zygote provides source-to-source automatic differentiation (AD) in Julia, and is the next-gen AD system for the Flux differentiable programming framework. For more details and benchmarks of Zygote's technique, see our paper. You may want to check out Flux for more interesting examples of Zygote usage; the documentation here focuses on internals and advanced AD usage.

Building blocks for invertible neural networks in the Julia programming language.

Trixi.jl is a numerical simulation framework for hyperbolic conservation laws written in Julia. A key objective for the framework is to be useful to both scientists and students. Therefore, next to having an extensible design with a fast implementation, Trixi.jl is focused on being easy to use for new or inexperienced users, including the installation and postprocessing procedures.

Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function...

This package was written in the early days of Flux / Zygote. Both these packages have significantly improved over time. Unfortunately, the current state of this package of has not been updated to reflect those improvements. It also seems that it might be better to gradually transition to defining the adjoints directly using ChainRules. A Ray Tracer written completely in Julia. This allows us to leverage the AD capabilities provided by Zygote to differentiate through the Ray Tracer.