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269 Stars 11 Forks Apache License 2.0 11 Commits 1 Opened issues


A High-level Machine Learning Library for Go

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


import (
    . ""

// 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)


See the examples folder for example implementations.

There are many examples in the reinforcement learning library Gold.


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


Please open an MR for any issues or feature requests.

Feel free to ping @pbarker on Gopher slack.


  • [ ] RNN
  • [ ] LSTM
  • [ ] Summary
  • [ ] Visualization

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