Overview

Goro is a high-level machine learning library for Go built on Gorgonia. It aims to have the same feel as Keras.

Usage

import (
. "github.com/aunum/goro/pkg/v1/model""github.com/aunum/goro/pkg/v1/layer"
)
// create the 'x' input e.g. mnist imagex:=NewInput("x", []int{1, 28, 28})
// create the 'y' or expect output e.g. labelsy:=NewInput("y", []int{10})
// create a new sequential model with the name 'mnist'model, _:=NewSequential("mnist")
// add layers to the modelmodel.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 optimizeroptimizer:=g.NewRMSPropSolver()
// compile the model with optionsmodel.Compile(xi, yi,
WithOptimizer(optimizer),
WithLoss(m.CrossEntropy),
WithBatchSize(100),
)
// fit the modelmodel.Fit(xTrain, yTrain)
// use the model to predict an 'x'prediction, _:=model.Predict(xTest)
// fit the model with a batchmodel.FitBatch(xTrainBatch, yTrainBatch)
// use the model to predict a batch of 'x'prediction, _=model.PredictBatch(xTestBatch)

Examples

See the examples folder for example implementations.

There are many examples in the reinforcement learning library Gold.

Docs

Each package contains a README explaining the usage, also see GoDoc.

Contributing

Please open an MR for any issues or feature requests.

Feel free to ping @pbarker on Gopher slack.

Roadmap

• RNN
• LSTM
• Summary
• Visualization