https://github.com/aunum/goro

A High-level Machine Learning Library for Go
https://github.com/aunum/goro

data-science go golang machine-learning machinelearning

Last synced: about 2 months ago
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A High-level Machine Learning Library for Go

• Host: GitHub
• URL: https://github.com/aunum/goro
• Owner: aunum
• License: apache-2.0
• Created: 2020-03-29T21:00:58.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-03-04T22:32:48.000Z (7 months ago)
• Last Synced: 2024-06-18T18:39:43.740Z (3 months ago)
• Topics: data-science, go, golang, machine-learning, machinelearning
• Language: Go
• Homepage:
• Size: 18.1 MB
• Stars: 370
• Watchers: 18
• Forks: 20
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-golang-ai - goro - level Machine Learning Library for Go. (General Machine Learning libraries)

README

        
![logo](./static/logo.png)   

[![GoDoc](https://godoc.org/github.com/aunum/goro?status.svg)](https://godoc.org/github.com/aunum/goro)

[![Go Report Card](https://goreportcard.com/badge/github.com/aunum/goro)](https://goreportcard.com/report/github.com/aunum/goro)

# Overview

Goro is a high-level machine learning library for Go built on [Gorgonia](https://gorgonia.org). It aims to have the same feel as [Keras](https://keras.io/).

## Usage

```go

import (

    . "github.com/aunum/goro/pkg/v1/model"

    "github.com/aunum/goro/pkg/v1/layer"

)

// create the 'x' input e.g. mnist image

x := NewInput("x", []int{1, 28, 28})

// create the 'y' or expect output e.g. labels

y := NewInput("y", []int{10})

// create a new sequential model with the name 'mnist'

model, _ := NewSequential("mnist")

// add layers to the model

model.AddLayers(

    layer.Conv2D{Input: 1, Output: 32, Width: 3, Height: 3},

    layer.MaxPooling2D{},

    layer.Conv2D{Input: 32, Output: 64, Width: 3, Height: 3},

    layer.MaxPooling2D{},

    layer.Conv2D{Input: 64, Output: 128, Width: 3, Height: 3},

    layer.MaxPooling2D{},

    layer.Flatten{},

    layer.FC{Input: 128 * 3 * 3, Output: 100},

    layer.FC{Input: 100, Output: 10, Activation: layer.Softmax},

)

// pick an optimizer

optimizer := g.NewRMSPropSolver()

// compile the model with options

model.Compile(xi, yi,

    WithOptimizer(optimizer),

    WithLoss(m.CrossEntropy),

    WithBatchSize(100),

)

// fit the model

model.Fit(xTrain, yTrain)

// use the model to predict an 'x'

prediction, _ := model.Predict(xTest)

// fit the model with a batch

model.FitBatch(xTrainBatch, yTrainBatch)

// use the model to predict a batch of 'x'

prediction, _ = model.PredictBatch(xTestBatch)

```

## Examples

See the [examples](./examples) folder for example implementations.

There are many examples in the reinforcement learning library [Gold](https://github.com/aunum/gold).

## Docs

Each package contains a README explaining the usage, also see [GoDoc](https://godoc.org/github.com/aunum/goro).

## Contributing

Please open an MR for any issues or feature requests.

Feel free to ping @pbarker on Gopher slack.

## Roadmap

- [ ] RNN

- [ ] LSTM

- [ ] Summary

- [ ] Visualization