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编辑：

发现问题了！我在预测中放入的样本没有达到 windowSize，并且由于某种原因，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它

    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
    }

最初的问题：

我已经在 CreateML 中训练了一个活动分类器，我在测试中获得了 100% 的正确性，但是当我进行设备推断时，它的猜测正确率约为 10%，甚至更少。任何想法为什么？

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。每次添加样本时是否需要进行预测，以便获得新的状态变量？任何指针都会非常有帮助

在我的推理代码下方。

    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
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