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Training A Convolutional Neural Network On KMNIST

Download the dataset here: KMNIST Dataset

Requirements

  • macOS 10.13.5 or later, with Xcode 10.0 or later; OR
  • Ubuntu 16.04 (64-bit); OR
  • other operating systems may work, but you will have to build Swift from sources.

This repository contains TensorFlow models written in Swift.

The stable branch works with the latest Swift for TensorFlow prebuilt packages.

Model

struct KMNISTParameters : ParameterGroup {
    var k1 = Tensor<Float>(glorotUniform: [5, 5, 1, 32])
    var k2 = Tensor<Float>(glorotUniform: [5, 5, 32, 64])
    var w3 = Tensor<Float>(glorotUniform: [3136, 10])
    var b3 = Tensor<Float>(zeros: [10])
}

Inference

let c1 = x.convolved2D(withFilter: θ.k1, strides: (1, 1, 1, 1), padding: .same)
let h1 = relu(c1)
let m1 = h1.maxPooled(kernelSize: (1, 5, 5, 1), strides: (1, 2, 2, 1), padding: .same)
let c2 = m1.convolved2D(withFilter: θ.k2, strides: (1, 1, 1, 1), padding: .same)
let h2 = relu(c2)
let m2 = h2.maxPooled(kernelSize: (1, 5, 5, 1), strides: (1, 2, 2, 1), padding: .same)
let flat = m2.reshaped(to: [-1, 3136])
let z3 = flat • θ.w3 + θ.b3
let h3 = softmax(z3, alongAxis: -1)

Loss

let q = h3
let p = e_i(y_i, 10)
let H = -Σ(p * log(q))
let H_total = μ(H)

Backpropagation

let dz3 = q - p
let dw3 = flat⊺ • dz3
let db3 = Σ(dz3,0)
let dflat = dz3 • θ.w3⊺
let dm2 = #adjoint(Tensor.reshaped)(m2)(toShape: flat.shapeTensor, originalValue: flat, seed: dflat)
let dh2 = #adjoint(Tensor.maxPooled)(h2)(kernelSize: kernelSize5, strides: strides2, padding: .same, originalValue: m2, seed: dm2)
let dc2 = #adjoint(relu)(c2, originalValue: h2, seed: dh2)
let (dm1, dk2) = #adjoint(Tensor<Float>.convolved2D)(m1)(filter: θ.k2, strides: strides1, padding: .same, originalValue: c2, seed: dc2)
let dh1 = #adjoint(Tensor.maxPooled)(h1)(kernelSize: kernelSize5, strides: strides2, padding: .same, originalValue: m1, seed: dm1)
let dc1 = #adjoint(relu)(c1, originalValue: h1, seed: dh1)
let (_, dk1) = #adjoint(Tensor<Float>.convolved2D)(x)(filter: θ.k1, strides: strides1, padding: .same, originalValue: c1, seed: dc1)

let dθ = KMNISTParameters(k1: dk1, k2: dk2, w3: dw3, b3: db3)