https://github.com/troublete/go-qndnn
quick 'n' dirty neural network (for practical use)
Science Score: 13.0%
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Low similarity (4.9%) to scientific vocabulary
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Repository
quick 'n' dirty neural network (for practical use)
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Metadata Files
README.md
go-qndnn
quick 'n' dirty neural network (for practical use)
Introduction
This package contains a simple Go implementation for neural networks; for practical everyday-use in common use-cases. It is neither heavily optimized to be the best package around, nor does this package contain an exhaustive variety of mathematical functions. It supports Sigmoid, Tanh and ReLU. It leverages Go primitives.
```go nn := qndnn.NewNeuralNet(nil, 4, 3, 3, 1) // sigmoid is default; input (4), hidden1 (3), hidden2 (3), output (1) // qndnn.NewNeuralNet(qndnn.WithRelu(), 4, 3, 3, 1) // – to use with relu // qndnn.NewNeuralNet(qndnn.WithTanh(), 4, 3, 3, 1) // - to use with tanh
// to retrieve output with input values out, err := nn.Output([]float64{1, 2, 3, 4})
// to train on expectations err = nn.Train( []Expectation{ { Input: []float64{1, 2, 3, 4}, Output: []float64{.42}, }, }, 0.01, // learning rate qndnn.RoundStrategy(1000), // train for 1000 rounds; other options include ThresholdStrategy (see examples) )
serializedBase64, err := nn.Serialize() // to serialize net (weights, biases)
nn, err = NewNeuralNetFromSerialized(nil, serializedBase64) // deserialize serialized net into usable structure; initialized with sigmoid //nn, err = NewNeuralNetFromSerialized(qndnn.WithRelu(), serializedBase64) // - to initialize with relu //nn, err = NewNeuralNetFromSerialized(qndnn.WithTanh(), serializedBase64) // - to initialize with tanh ```
Owner
- Name: Willi
- Login: troublete
- Kind: user
- Location: Germany
- Company: @camaoag
- Website: https://troublete.com
- Repositories: 10
- Profile: https://github.com/troublete
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