A lightweight library to calculate tensors in Swift, which has similar APIs to TensorFlow's

Qoncept, Inc.

Last update: Oct 20, 2022

Related tags

Machine Learning TensorSwift

Overview

TensorSwift

TensorSwift is a lightweight library to calculate tensors, which has similar APIs to TensorFlow's. TensorSwift is useful to simulate calculating tensors in Swift using models trained by TensorFlow.

let a = Tensor(shape: [2, 3], elements: [1, 2, 3, 4, 5, 6])
let b = Tensor(shape: [2, 3], elements: [7, 8, 9, 10, 11, 12])
let sum = a + b // Tensor(shape: [2, 3], elements: [8, 10, 12, 14, 16, 18])
let mul = a * b // Tensor(shape: [2, 3], elements: [7, 16, 27, 40, 55, 72])

let c = Tensor(shape: [3, 1], elements: [7, 8, 9])
let matmul = a.matmul(c) // Tensor(shape: [2, 1], elements: [50, 122])

let zeros = Tensor(shape: [2, 3, 4])
let ones = Tensor(shape: [2, 3, 4], element: 1)

Deep MNIST for Experts

The following code shows how to simulate Deep MNIST for Experts, a tutorial of TensorFlow, by TensorSwift.

public struct Classifier {
    public let W_conv1: Tensor
    public let b_conv1: Tensor
    public let W_conv2: Tensor
    public let b_conv2: Tensor
    public let W_fc1: Tensor
    public let b_fc1: Tensor
    public let W_fc2: Tensor
    public let b_fc2: Tensor
    
    public func classify(_ x_image: Tensor) -> Int {
        let h_conv1 = (x_image.conv2d(filter: W_conv1, strides: [1, 1, 1]) + b_conv1).relu()
        let h_pool1 = h_conv1.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1])
        
        let h_conv2 = (h_pool1.conv2d(filter: W_conv2, strides: [1, 1, 1]) + b_conv2).relu()
        let h_pool2 = h_conv2.maxPool(kernelSize: [2, 2, 1], strides: [2, 2, 1])
        
        let h_pool2_flat = h_pool2.reshaped([1, Dimension(7 * 7 * 64)])
        let h_fc1 = (h_pool2_flat.matmul(W_fc1) + b_fc1).relu()
        
        let y_conv = (h_fc1.matmul(W_fc2) + b_fc2).softmax()

        return y_conv.elements.enumerated().max { $0.1 < $1.1 }!.0
    }
}

Installation

Swift Package Manager

.Package(url: "[email protected]:qoncept/TensorSwift.git", from: "0.2.0"),

CocoaPods

pod 'TensorSwift', '~> 0.2'

Carthage

github "qoncept/TensorSwift" ~> 0.2

License

The MIT License

Comments

fatal error: `elements.count` must be greater than or equal to `shape.volume`: elements.count = 1024, shape.volume = 3136:

I have used my own images to trained by Tensorflow's DeepMnist example code. And I used the way you give in the other issue to output the Models. But when I replace your Models with mine and run, I meet the error:

fatal error: elements.count must be greater than or equal to shape.volume: elements.count = 1024, shape.volume = 3136:

I think maybe there is something wrong with my models. But the DeepMnist training is successful: And the output of models is successful, too.

What can I do to solve this problem? In order to replace the models successfully, should I do something with the Classifier Struct?

opened by LinShiwei 7
swift 3 - migration problems / tensorflow fyi

fyi - I'm getting close to having tensorflow via c_api in swift working. could use some help though. I'm a bit of a newb when it comes to tensors. checkout my branch - https://github.com/johndpope/tensorflow/tree/swift/tensorflow/swift

opened by johndpope 2
Integration with Numpy
Implement new feature: Load tensor from Numpy array file (.npy)

Supported npy file:

~~Little endian~~

np.float32 or np.float64 array

"fortran_order" is False
opened by t-ae 2
How to export weights from Python?

This is a pretty awesome project. I've trained some models on Tensorflow and want to export to the format you use loadFloatArray with. Any chance you can post some example Python code to export those weights?

Thanks for building this btw!

opened by ivanmkc 2
Update for Swift3, etc.
This PR includes

Syntax update for Swift3

Support Swift Package Manager

Separate MNIST sample project

Update podspec file (Sources directory & version)
opened by t-ae 1

Releases(0.2.3)

0.2.3(Oct 24, 2017)

Source code(tar.gz)
Source code(zip)
0.2.0(Oct 24, 2016)

Source code(tar.gz)
Source code(zip)
0.2.0-beta(Oct 24, 2016)

Source code(tar.gz)
Source code(zip)

Owner

Qoncept, Inc.

GitHub

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A lightweight library to calculate tensors in Swift, which has similar APIs to TensorFlow's

Related tags

Overview

TensorSwift

Deep MNIST for Experts

Installation

Swift Package Manager

CocoaPods

Carthage

License

Comments

fatal error: `elements.count` must be greater than or equal to `shape.volume`: elements.count = 1024, shape.volume = 3136:

swift 3 - migration problems / tensorflow fyi

Integration with Numpy

How to export weights from Python?

Update for Swift3, etc.

Releases(0.2.3)

0.2.3(Oct 24, 2017)

0.2.0(Oct 24, 2016)

0.2.0-beta(Oct 24, 2016)

Owner

Qoncept, Inc.

A simple Swift package which acts as an OpenAI client for the GPT-3 API.

This is an open-source project for the aesthetic evaluation of images based on the deep learning-caffe framework, which we completed in the Victory team of Besti.

A Swift library for creating and exporting CoreML Models in Swift

The Swift machine learning library.

A Swift deep learning library with Accelerate and Metal support.

A simple deep learning library for estimating a set of tags and extracting semantic feature vectors from given illustrations.

Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux

BrainCore is a simple but fast neural network framework written in Swift.

MLKit is a simple machine learning framework written in Swift.

Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development. bayes theorem, neural networks, and more AI.

A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms

A framework for building fast genetic algorithms in Swift.

Running Swift automatic differentiation on iOS

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