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Card](https://goreportcard.com/badge/github.com/aunum/goro)](https://goreportcard.com/report/github.com/aunum/goro)\n# Overview\nGoro 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/).\n\n## Usage\n```go\nimport (\n    . \"github.com/aunum/goro/pkg/v1/model\"\n    \"github.com/aunum/goro/pkg/v1/layer\"\n)\n\n// create the 'x' input e.g. mnist image\nx := NewInput(\"x\", []int{1, 28, 28})\n\n// create the 'y' or expect output e.g. labels\ny := NewInput(\"y\", []int{10})\n\n// create a new sequential model with the name 'mnist'\nmodel, _ := NewSequential(\"mnist\")\n\n// add layers to the model\nmodel.AddLayers(\n    layer.Conv2D{Input: 1, Output: 32, Width: 3, Height: 3},\n    layer.MaxPooling2D{},\n    layer.Conv2D{Input: 32, Output: 64, Width: 3, Height: 3},\n    layer.MaxPooling2D{},\n    layer.Conv2D{Input: 64, Output: 128, Width: 3, Height: 3},\n    layer.MaxPooling2D{},\n    layer.Flatten{},\n    layer.FC{Input: 128 * 3 * 3, Output: 100},\n    layer.FC{Input: 100, Output: 10, Activation: layer.Softmax},\n)\n\n// pick an optimizer\noptimizer := g.NewRMSPropSolver()\n\n// compile the model with options\nmodel.Compile(xi, yi,\n    WithOptimizer(optimizer),\n    WithLoss(m.CrossEntropy),\n    WithBatchSize(100),\n)\n\n// fit the model\nmodel.Fit(xTrain, yTrain)\n\n// use the model to predict an 'x'\nprediction, _ := model.Predict(xTest)\n\n// fit the model with a batch\nmodel.FitBatch(xTrainBatch, yTrainBatch)\n\n// use the model to predict a batch of 'x'\nprediction, _ = model.PredictBatch(xTestBatch)\n```\n\n## Examples\nSee the [examples](./examples) folder for example implementations.\n\nThere are many examples in the reinforcement learning library [Gold](https://github.com/aunum/gold).\n\n## Docs\nEach package contains a README explaining the usage, also see [GoDoc](https://godoc.org/github.com/aunum/goro).\n\n## Contributing\nPlease open an MR for any issues or feature requests.\n\nFeel free to ping @pbarker on Gopher slack.\n\n## Roadmap\n- [ ] RNN\n- [ ] LSTM\n- [ ] Summary\n- [ ] Visualization\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faunum%2Fgoro","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faunum%2Fgoro","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faunum%2Fgoro/lists"}