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Swift for TensorFlow Deep Learning Library
https://github.com/tensorflow/swift-apis

deep-learning differentiable-programming swift tensorflow

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Swift for TensorFlow Deep Learning Library

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README 

        
# Swift for TensorFlow Deep Learning Library



Get a taste of *protocol-oriented differentiable programming*.



This repository hosts [Swift for TensorFlow][s4tf]'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][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**][blank_colab] to try out Swift,

TensorFlow, differentiable programming, and deep learning.



> For detailed usage and troubleshooting, see [Usage][usage] on the Swift for

TensorFlow project homepage.



#### Define a model



Simply import `TensorFlow` to get the full power of TensorFlow.



```swift

import TensorFlow



let hiddenSize: Int = 10



struct Model: Layer {

    var layer1 = Dense(inputSize: 4, outputSize: hiddenSize, activation: relu)

    var layer2 = Dense(inputSize: hiddenSize, outputSize: hiddenSize, activation: relu)

    var layer3 = Dense(inputSize: hiddenSize, outputSize: 3, activation: identity)

    

    @differentiable

    func callAsFunction(_ input: Tensor) -> Tensor {

        return input.sequenced(through: layer1, layer2, layer3)

    }

}

```



#### Initialize a model and an optimizer



```swift

var classifier = Model()

let optimizer = SGD(for: classifier, learningRate: 0.02)

Context.local.learningPhase = .training

// Dummy data.

let x: Tensor = Tensor(randomNormal: [100, 4])

let y: Tensor = Tensor(randomUniform: [100])

```



#### Run a training loop



One way to define a training epoch is to use the

[`gradient(at:in:)`][gradient] function.



```swift

for _ in 0..<1000 {

    let 𝛁model = gradient(at: classifier) { classifier -> Tensor 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.



```swift

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**][swift-models].



## Development



Documentation covering development can be found in the [Developer Guide](Documentation/Development.md).



## Bugs



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



## Community



Discussion about Swift for TensorFlow happens on the

[[email protected]][forum]

mailing list.



## Contributing



We welcome contributions: please read the [Contributor Guide](CONTRIBUTING.md)

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](CODE_OF_CONDUCT.md), which we encourage everybody to read before

participating.



[s4tf]: https://github.com/tensorflow/swift

[integrated]: https://github.com/apple/swift/tree/tensorflow#customize-tensorflow-support

[blank_colab]: https://colab.research.google.com/notebook#create=true&language=swift

[usage]: https://github.com/tensorflow/swift/blob/main/Usage.md

[gradient]: https://www.tensorflow.org/swift/api_docs/Functions#/s:10TensorFlow8gradient2at2in13TangentVectorQzx_AA0A0Vyq_GxXEtAA14DifferentiableRzAA0aB13FloatingPointR_r0_lF

[swift-models]: https://github.com/tensorflow/swift-models

[toolchain]: https://github.com/tensorflow/swift/blob/main/Installation.md

[forum]: https://groups.google.com/a/tensorflow.org/d/forum/swift