Question

编辑：

发现了问题！我放入预测中的样本未达到windowSize，由于某种原因，当我创建MLMultiArrays时，它直接用随机值填充了数组。所以解决方案是用这样的零填充

    for i in 0..<ModelConstants.predictionWindowSize {

        centerOfMassXDifferenceArray[i] = 0
        centerOfMassYDifferenceArray[i] = 0
        heightDifferenceArray[i] = 0
        numberOfPixelsDifferenceArray[i] = 0
        widthDifferenceArray[i] = 0
        xDifferenceArray[i] = 0
        yDifferenceArray[i] = 0

    }

    for i in 0..<currentStateArray.count {
        currentStateArray[i] = 0
    }

初始问题：

我已经在CreateML中训练了一个活动分类器，该类在我的测试中获得100％的正确性，但是当我进行设备推断时，它可以正确地猜测大约10％的时间，甚至更少。有什么想法吗？

我唯一的错误修复线索是了解LSTM的stateIn变量，现在我将数组保留为nil值。每次添加样本时都需要做出预测，以便获得新的状态变量吗？任何指针都将非常有帮助

在我的推断代码下面。

    let classifier = ActivityModel()

    struct ModelConstants {
        static let predictionWindowSize = 49
    }

    guard let centerOfMassXDifferenceArray = try? MLMultiArray(shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    guard let centerOfMassYDifferenceArray = try? MLMultiArray( shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    guard let heightDifferenceArray = try? MLMultiArray( shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    guard let numberOfPixelsDifferenceArray = try? MLMultiArray( shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    guard let widthDifferenceArray = try? MLMultiArray( shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    guard let xDifferenceArray = try? MLMultiArray( shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    guard let yDifferenceArray = try? MLMultiArray( shape: [ModelConstants.predictionWindowSize] as [NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    guard let currentStateArray = try? MLMultiArray( shape: [400 as NSNumber], dataType: MLMultiArrayDataType.double) else { return nil }

    // Populate

    if inferenceData.count > ModelConstants.predictionWindowSize {
        let range = inferenceData.index(inferenceData.endIndex, offsetBy: -ModelConstants.predictionWindowSize)..<inferenceData.endIndex
        let arraySlice = inferenceData[range]
        inferenceData = Array(arraySlice)
    }

    for i in 0..<inferenceData.count {
        let blobMetric = inferenceData[i]
        centerOfMassXDifferenceArray[i] = blobMetric.centerOfMassXDifference as NSNumber
        centerOfMassYDifferenceArray[i] = blobMetric.centerOfMassYDifference as NSNumber
        heightDifferenceArray[i] = blobMetric.heightDifference as NSNumber
        numberOfPixelsDifferenceArray[i] = blobMetric.numberOfPixelsDifference as NSNumber
        widthDifferenceArray[i] = blobMetric.widthDifference as NSNumber
        xDifferenceArray[i] = blobMetric.xDifference as NSNumber
        yDifferenceArray[i] = blobMetric.yDifference as NSNumber
    }

    // Perform prediction
    let modelPrediction = try? classifier.prediction(
        centerOfMassXDifference: centerOfMassXDifferenceArray,
        centerOfMassYDifference: centerOfMassYDifferenceArray,
        heightDifference: heightDifferenceArray,
        numberOfPixelsDifference: numberOfPixelsDifferenceArray,
        widthDifference: widthDifferenceArray,
        xDifference: xDifferenceArray,
        yDifference: yDifferenceArray,
        stateIn: currentStateArray // Update the state vector
    )

    // Reset inferenceData
    inferenceData = []

    return modelPrediction?.label

CreateML-活动分类器-应用程序推理与创建ML推理的结果不同

0 个答案: