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.
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.
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.
Simply import TensorFlow
to get the full power of TensorFlow.
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: {$0})
@differentiable(wrt: (self, input))
func applied(to input: Tensor<Float>) -> Tensor<Float> {
let l1 = layer1.applied(to: input)
let l2 = layer2.applied(to: l1)
return layer3.applied(to: l2)
}
}
let optimizer = SGD<Model, Float>(learningRate: 0.02)
var classifier = Model()
let x: Tensor<Float> = ...
let y: Tensor<Float> = ...
for _ in 0..<1000 {
let 𝛁model = classifier.gradient { classifier -> Tensor<Float> in
let ŷ = classifier.applied(to: x)
let loss = softmaxCrossEntropy(logits: ŷ, labels: y)
print("Loss: \(loss)")
return loss
}
optimizer.update(&classifier.allDifferentiableVariables, along: 𝛁model)
}
For more tutorials and models, go to tensorflow/swift-tutorials and tensorflow/swift-models.
- Swift for TensorFlow toolchain.
- An environment that can run the Swift for TensorFlow toolchains: Linux 18.04 or macOS with Xcode 10.
$ swift build
$ swift test
Please report bugs and feature requests using GitHub issues in this repository.
Discussion about Swift for TensorFlow happens on the swift@tensorflow.org mailing list.
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.
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The Swift for TensorFlow community is guided by our Code of Conduct, which we encourage everybody to read before participating.