Swift for TensorFlow Notebook and CoreML + SwiftUI demo App to demonstrate the potentiality of the SwiftCoreMLTools library (https://github.com/JacopoMangiavacchi/SwiftCoreMLTools) to adopt Transfer Learning technique and train on devices tabular data with multiple Numerical and Categorical Features using Embedding and Multi Input.
The Boston housing price dataset (Creator: Harrison, D. and Rubinfeld, D.L. )has 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.
Attribute Information (in order):
- CRIM Numerical: per capita crime rate by town
- ZN Numerical: proportion of residential land zoned for lots over 25,000 sq.ft.
- INDUS Numerical: proportion of non-retail business acres per town
- CHAS Categorical: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
- NOX Numerical: nitric oxides concentration (parts per 10 million)
- RM Numerical: average number of rooms per dwelling
- AGE Numerical: proportion of owner-occupied units built prior to 1940
- DIS Numerical: weighted distances to five Boston employment centres
- RAD Categorical: index of accessibility to radial highways
- TAX Numerical: full-value property-tax rate per ten thousand dollars
- PTRATIO Numerical: pupil-teacher ratio by town
- B Numerical: 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
- LSTAT Numerical: % lower status of the population
- MEDV Numerical: Median value of owner-occupied homes in a thousand dollar
- Categorical features are encoded with one-hot-encoding representation and feeded into Embedding layers.
- Numerical features are normalized using ZScore (x' = (x' - MEAN(X)) / STD(X)) fitting only training data
- Dataset is splitted in Train and Test
struct MultiInputs<N: Differentiable, C>: Differentiable {
var numerical: N
@noDerivative
var categorical: C
@differentiable
init(numerical: N, categorical: C) {
self.numerical = numerical
self.categorical = categorical
}
}
struct RegressionModel: Module {
var numericalLayer = Dense<Float>(inputSize: 11, outputSize: 32, activation: relu)
var embedding1 = Embedding<Float>(vocabularySize: 2, embeddingSize: 2)
var embedding2 = Embedding<Float>(vocabularySize: 9, embeddingSize: 5)
var embeddingLayer = Dense<Float>(inputSize: (4 + 45), outputSize: 64, activation: relu)
var allInputConcatLayer = Dense<Float>(inputSize: (32 + 64), outputSize: 128, activation: relu)
var hiddenLayer = Dense<Float>(inputSize: 128, outputSize: 32, activation: relu)
var outputLayer = Dense<Float>(inputSize: 32, outputSize: 1)
@differentiable
func callAsFunction(_ input: MultiInputs<[Tensor<Float>], [Tensor<Int32>]>) -> Tensor<Float> {
let numericalInput = numericalLayer(input.numerical[0])
let embeddingOutput1 = embedding1(input.categorical[0])
let embeddingOutput1Reshaped = embeddingOutput1.reshaped(to:
TensorShape([embeddingOutput1.shape[0], embeddingOutput1.shape[1] * embeddingOutput1.shape[2]]))
let embeddingOutput2 = embedding2(input.categorical[1])
let embeddingOutput2Reshaped = embeddingOutput2.reshaped(to:
TensorShape([embeddingOutput2.shape[0], embeddingOutput2.shape[1] * embeddingOutput2.shape[2]]))
let embeddingConcat = Tensor<Float>(concatenating: [embeddingOutput1Reshaped, embeddingOutput2Reshaped], alongAxis: 1)
let embeddingInput = embeddingLayer(embeddingConcat)
let allConcat = Tensor<Float>(concatenating: [numericalInput, embeddingInput], alongAxis: 1)
return allConcat.sequenced(through: allInputConcatLayer, hiddenLayer, outputLayer)
}
}
var model = RegressionModel()let coremlModel = Model(version: 4,
shortDescription: "Regression",
author: "Jacopo Mangiavacchi",
license: "MIT",
userDefined: ["SwiftCoremltoolsVersion" : "0.0.6"]) {
Input(name: "numericalInput", shape: [11])
Input(name: "categoricalInput1", shape: [1])
Input(name: "categoricalInput2", shape: [1])
Output(name: "output", shape: [1])
NeuralNetwork {
Embedding(name: "embedding1",
input: ["categoricalInput1"],
output: ["outEmbedding1"],
weight: model.embedding1.embeddings.transposed().flattened().scalars,
inputDim: 2,
outputChannels: 2)
Permute(name: "permute1",
input: ["outEmbedding1"],
output: ["outPermute1"],
axis: [2, 1, 0, 3])
Flatten(name: "flatten1",
input: ["outPermute1"],
output: ["outFlatten1"],
mode: .last)
Embedding(name: "embedding2",
input: ["categoricalInput2"],
output: ["outEmbedding2"],
weight: model.embedding2.embeddings.transposed().flattened().scalars,
inputDim: 9,
outputChannels: 5)
Permute(name: "permute2",
input: ["outEmbedding2"],
output: ["outPermute2"],
axis: [2, 1, 0, 3])
Flatten(name: "flatten2",
input: ["outPermute2"],
output: ["outFlatten2"],
mode: .last)
Concat(name: "concat",
input: ["numericalInput", "outFlatten1", "outFlatten2"],
output: ["outConcat"])
InnerProduct(name: "dense1",
input: ["outConcat"],
output: ["outDense1"],
weight: model.allInputConcatLayer.weight.transposed().flattened().scalars,
bias: model.allInputConcatLayer.bias.flattened().scalars,
inputChannels: 11 + 2 + 5,
outputChannels: 64)
ReLu(name: "Relu1",
input: ["outDense1"],
output: ["outRelu1"])
InnerProduct(name: "dense2",
input: ["outRelu1"],
output: ["outDense2"],
weight: model.hiddenLayer.weight.transposed().flattened().scalars,
bias: model.hiddenLayer.bias.flattened().scalars,
inputChannels: 64,
outputChannels: 32)
ReLu(name: "Relu2",
input: ["outDense2"],
output: ["outRelu2"])
InnerProduct(name: "dense3",
input: ["outRelu2"],
output: ["output"],
weight: model.outputLayer.weight.transposed().flattened().scalars,
bias: model.outputLayer.bias.flattened().scalars,
inputChannels: 32,
outputChannels: 1)
}
}let coremlModel = Model(version: 4,
shortDescription: "Regression",
author: "Jacopo Mangiavacchi",
license: "MIT",
userDefined: ["SwiftCoremltoolsVersion" : "0.0.6"]) {
Input(name: "numericalInput", shape: [11])
Input(name: "categoricalInput1", shape: [1])
Input(name: "categoricalInput2", shape: [1])
Output(name: "output", shape: [1])
TrainingInput(name: "numericalInput", shape: [11])
TrainingInput(name: "categoricalInput1", shape: [1])
TrainingInput(name: "categoricalInput2", shape: [1])
TrainingInput(name: "output_true", shape: [1])
NeuralNetwork(losses: [MSE(name: "lossLayer",
input: "output",
target: "output_true")],
optimizer: SGD(learningRateDefault: 0.001,
learningRateMax: 0.3,
miniBatchSizeDefault: 32,
miniBatchSizeRange: [32],
momentumDefault: 0,
momentumMax: 1.0),
epochDefault: 500,
epochSet: [500],
shuffle: true) {
Embedding(name: "embedding1",
input: ["categoricalInput1"],
output: ["outEmbedding1"],
weight: model.embedding1.embeddings.transposed().flattened().scalars,
inputDim: 2,
outputChannels: 2)
Permute(name: "permute1",
input: ["outEmbedding1"],
output: ["outPermute1"],
axis: [2, 1, 0, 3])
Flatten(name: "flatten1",
input: ["outPermute1"],
output: ["outFlatten1"],
mode: .last)
Embedding(name: "embedding2",
input: ["categoricalInput2"],
output: ["outEmbedding2"],
weight: model.embedding2.embeddings.transposed().flattened().scalars,
inputDim: 9,
outputChannels: 5)
Permute(name: "permute2",
input: ["outEmbedding2"],
output: ["outPermute2"],
axis: [2, 1, 0, 3])
Flatten(name: "flatten2",
input: ["outPermute2"],
output: ["outFlatten2"],
mode: .last)
Concat(name: "concat",
input: ["numericalInput", "outFlatten1", "outFlatten2"],
output: ["outConcat"])
InnerProduct(name: "dense1",
input: ["outConcat"],
output: ["outDense1"],
inputChannels: 11 + 2 + 5,
outputChannels: 64,
updatable: true)
ReLu(name: "Relu1",
input: ["outDense1"],
output: ["outRelu1"])
InnerProduct(name: "dense2",
input: ["outRelu1"],
output: ["outDense2"],
inputChannels: 64,
outputChannels: 32,
updatable: true)
ReLu(name: "Relu2",
input: ["outDense2"],
output: ["outRelu2"])
InnerProduct(name: "dense3",
input: ["outRelu2"],
output: ["output"],
inputChannels: 32,
outputChannels: 1,
updatable: true)
}
}