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README.md

Swift MNIST Dataset

Build Status

MNIST dataset

Swift Module for MNIST database (Modified National Institute of Standards and Technology database)

This module is made by the purpose of easy to use for the Swift Chapter in Tensorflow Handbook.

Usage

Class MNIST will provide all you need.

main.swift

import MNIST

let mnist = MNIST()
let ((trainImages, trainLabels), (testImages, testLabels)) = mnist.loadData()

let imageBatch = Dataset(elements: trainImages).batched(32)
let labelBatch = Dataset(elements: trainLabels).batched(32)

for (X, y) in zip(imageBatch, labelBatch) {
  // Caculate the gradient
  let (_, grads) = valueWithGradient(at: model) { model -> Tensor<Float> in
    let logits = model(X)
    return softmaxCrossEntropy(logits: logits, labels: y)
  }

  // Update parameters by optimizer
  optimizer.update(&model.self, along: grads)
}

let logits = model(testImages)
let acc = mnist.getAccuracy(y: testLabels, logits: logits)

print("Test Accuracy: \(acc)" )

Learn more from Tensorflow Handbook for Swift

Package.swift

// swift-tools-version:5.1
// The swift-tools-version declares the minimum version of Swift required to build this package.

import PackageDescription

let package = Package(
    name: "tensorflow-handbook-swift",
    dependencies: [
        // Dependencies declare other packages that this package depends on.
        .package(url: "https://github.com/huan/swift-MNIST.git", from: "0.1.0"),
    ],
    targets: [
        // Targets are the basic building blocks of a package. A target can define a module or a test suite.
        // Targets can depend on other targets in this package, and on products in packages which this package depends on.
        .target(
            name: "s4tf",
            dependencies: ["MNIST"]),
        .testTarget(
            name: "s4tfTests",
            dependencies: ["s4tf"]),
    ]
)

History

v0.1.0 (28 Aug 2019)

First version, used from Tensorflow Handbook for Swift

Links

Author

Huan (李卓桓) zixia@zixia.net

Profile of Huan LI (李卓桓) on StackOverflow

Copyright & License

  • Code & Docs © 2019 - now Huan (李卓桓) zixia@zixia.net
  • Code released under the Apache-2.0 License
  • Docs released under Creative Commons
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