TensorSwift alternatives and similar libraries
Based on the "Machine Learning" category.
Alternatively, view TensorSwift alternatives based on common mentions on social networks and blogs.
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Bender
Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood. -
Awesome-Mobile-Machine-Learning
A curated list of awesome mobile machine learning resources for iOS, Android, and edge devices. -
AIToolbox
A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms -
Swift-Brain
Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development. bayes theorem, neural networks, and more AI. -
SwiftCoreMLTools
A Swift library for creating and exporting CoreML Models in Swift -
DL4S
Accelerated tensor operations and dynamic neural networks based on reverse mode automatic differentiation for every device that can run Swift - from watchOS to Linux -
CoreML-samples
Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools. -
Serrano
A Swift deep learning library with Accelerate and Metal support. -
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Tensorflow-iOS
The official Google-built powerful neural network library port for iOS.
Appwrite - The open-source backend cloud platform
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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Popular Comparisons
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README
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
[deep-mnist.gif](Resources/DeepMnist.gif)
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](LICENSE)
*Note that all licence references and agreements mentioned in the TensorSwift README section above
are relevant to that project's source code only.