hubro-connect/no.hubroplatform.hubroconnect/FlowerClient

FlowerClient

class FlowerClient<X : Any, Y : Any>(tfliteFileBuffer: MappedByteBuffer, val layersSizes: IntArray, val spec: SampleSpec<X, Y>) : AutoCloseable

Flower client that handles TensorFlow Lite model Interpreter and sample data.

Parameters

tfliteFileBuffer

TensorFlow Lite model file.

layersSizes

Sizes of model parameters layers in bytes.

spec

Specification for the samples, see SampleSpec.

Constructors

FlowerClient

constructor(tfliteFileBuffer: MappedByteBuffer, layersSizes: IntArray, spec: SampleSpec<X, Y>)

Types

Companion

object Companion

Properties

interpreterLock

val interpreterLock: ReentrantLock

layersSizes

val layersSizes: IntArray

spec

val spec: SampleSpec<X, Y>

testSampleLock

val testSampleLock: ReentrantReadWriteLock

testSamples

val testSamples: MutableList<Sample<X, Y>>

trainingSamples

val trainingSamples: MutableList<Sample<X, Y>>

trainSampleLock

val trainSampleLock: ReentrantReadWriteLock

Functions

addSample

fun addSample(bottleneck: X, label: Y, isTraining: Boolean)

Add one sample point (bottleneck, label) for training or testing later. Thread-safe.

open override fun close()

evaluate

fun evaluate(): Pair<Float, Float>

Evaluate model loss and accuracy using testSamples and spec.

fit

fun fit(epochs: Int = 1, batchSize: Int = 32, lossCallback: (List<Float>) -> Unit? = null): List<Double>

Fit the local model using trainingSamples for epochs epochs with batch size batchSize.

getParameters

fun getParameters(): Array<ByteBuffer>

Obtain the model parameters from interpreter.

inference

fun inference(x: Array<X>): Array<Y>

Run inference on x using interpreter and return the result.

parametersFromMap

fun parametersFromMap(map: Map<String, Any>): Array<ByteBuffer>

parametersToMap

fun parametersToMap(parameters: Array<ByteBuffer>): Map<String, Any>

updateParameters

fun updateParameters(parameters: Array<ByteBuffer>): Array<ByteBuffer>

Update the model parameters in interpreter with parameters.