Swift for TensorFlow Deep Learning Library

Get a taste of protocol-oriented differentiable programming.

This repository hosts Swift for TensorFlow's deep learning library, available both as a part of Swift for TensorFlow toolchains and as a Swift package.

Usage

This library is being automatically integrated in Swift for TensorFlow toolchains. You do not need to add this library as a Swift Package Manager dependency.

Use Google Colaboratory

Open an empty Colaboratory now to try out Swift, TensorFlow, differentiable programming, and deep learning.

For detailed usage and troubleshooting, see Usage on the Swift for TensorFlow project homepage.

Define a model

Simply import TensorFlow to get the full power of TensorFlow.

import TensorFlow

let hiddenSize: Int = 10

struct Model: Layer {
    var layer1 = Dense<Float>(inputSize: 4, outputSize: hiddenSize, activation: relu)
    var layer2 = Dense<Float>(inputSize: hiddenSize, outputSize: hiddenSize, activation: relu)
    var layer3 = Dense<Float>(inputSize: hiddenSize, outputSize: 3, activation: identity)
    
    @differentiable
    func callAsFunction(_ input: Tensor<Float>) -> Tensor<Float> {
        return input.sequenced(through: layer1, layer2, layer3)
    }
}

Initialize a model and an optimizer

var classifier = Model()
let optimizer = SGD(for: classifier, learningRate: 0.02)
Context.local.learningPhase = .training
// Dummy data.
let x: Tensor<Float> = Tensor(randomNormal: [100, 4])
let y: Tensor<Int32> = Tensor(randomUniform: [100])

Run a training loop

One way to define a training epoch is to use the gradient(at:in:) function.

for _ in 0..<1000 {
    let 𝛁model = gradient(at: classifier) { classifier -> Tensor<Float> in
        let ŷ = classifier(x)
        let loss = softmaxCrossEntropy(logits: ŷ, labels: y)
        print("Loss: \(loss)")
        return loss
    }
    optimizer.update(&classifier, along: 𝛁model)
}

Another way is to make use of methods on Differentiable or Layer that produce a backpropagation function. This allows you to compose your derivative computation with great flexibility.

for _ in 0..<1000 {
    let (ŷ, backprop) = classifier.appliedForBackpropagation(to: x)
    let (loss, 𝛁ŷ) = valueWithGradient(at: ŷ) { ŷ in softmaxCrossEntropy(logits: ŷ, labels: y) }
    print("Model output: \(ŷ), Loss: \(loss)")
    let (𝛁model, _) = backprop(𝛁ŷ)
    optimizer.update(&classifier, along: 𝛁model)
}

For more models, go to tensorflow/swift-models.

Development

Documentation covering development can be found in the Developer Guide.

Bugs

Please report bugs and feature requests using GitHub issues in this repository.

Community

Discussion about Swift for TensorFlow happens on the swift@tensorflow.org mailing list.

Contributing

We welcome contributions: please read the Contributor Guide to get started. It's always a good idea to discuss your plans on the mailing list before making any major submissions.

Code of Conduct

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

The Swift for TensorFlow community is guided by our Code of Conduct, which we encourage everybody to read before participating.