Package com.azure.resourcemanager.machinelearning.models

Class ImageModelDistributionSettings

java.lang.Object
com.azure.resourcemanager.machinelearning.models.ImageModelDistributionSettings
All Implemented Interfaces:
com.azure.json.JsonSerializable<ImageModelDistributionSettings>
Direct Known Subclasses:
ImageModelDistributionSettingsClassification, ImageModelDistributionSettingsObjectDetection
public class ImageModelDistributionSettings extends Object implements com.azure.json.JsonSerializable<ImageModelDistributionSettings>
Distribution expressions to sweep over values of model settings. <example> Some examples are: ``` ModelName = "choice('seresnext', 'resnest50')"; LearningRate = "uniform(0.001, 0.01)"; LayersToFreeze = "choice(0, 2)"; ```</example> All distributions can be specified as distribution_name(min, max) or choice(val1, val2, ..., valn) where distribution name can be: uniform, quniform, loguniform, etc For more details on how to compose distribution expressions please check the documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

  • Constructor Details

    • ImageModelDistributionSettings

      public ImageModelDistributionSettings()
      Creates an instance of ImageModelDistributionSettings class.

  • Method Details

    • amsGradient

      public String amsGradient()
      Get the amsGradient property: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
      Returns:
      the amsGradient value.

    • withAmsGradient

      public ImageModelDistributionSettings withAmsGradient(String amsGradient)
      Set the amsGradient property: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
      Parameters:
      amsGradient - the amsGradient value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • augmentations

      public String augmentations()
      Get the augmentations property: Settings for using Augmentations.
      Returns:
      the augmentations value.

    • withAugmentations

      public ImageModelDistributionSettings withAugmentations(String augmentations)
      Set the augmentations property: Settings for using Augmentations.
      Parameters:
      augmentations - the augmentations value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • beta1

      public String beta1()
      Get the beta1 property: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
      Returns:
      the beta1 value.

    • withBeta1

      public ImageModelDistributionSettings withBeta1(String beta1)
      Set the beta1 property: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
      Parameters:
      beta1 - the beta1 value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • beta2

      public String beta2()
      Get the beta2 property: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
      Returns:
      the beta2 value.

    • withBeta2

      public ImageModelDistributionSettings withBeta2(String beta2)
      Set the beta2 property: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
      Parameters:
      beta2 - the beta2 value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • distributed

      public String distributed()
      Get the distributed property: Whether to use distributer training.
      Returns:
      the distributed value.

    • withDistributed

      public ImageModelDistributionSettings withDistributed(String distributed)
      Set the distributed property: Whether to use distributer training.
      Parameters:
      distributed - the distributed value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • earlyStopping

      public String earlyStopping()
      Get the earlyStopping property: Enable early stopping logic during training.
      Returns:
      the earlyStopping value.

    • withEarlyStopping

      public ImageModelDistributionSettings withEarlyStopping(String earlyStopping)
      Set the earlyStopping property: Enable early stopping logic during training.
      Parameters:
      earlyStopping - the earlyStopping value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • earlyStoppingDelay

      public String earlyStoppingDelay()
      Get the earlyStoppingDelay property: Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
      Returns:
      the earlyStoppingDelay value.

    • withEarlyStoppingDelay

      public ImageModelDistributionSettings withEarlyStoppingDelay(String earlyStoppingDelay)
      Set the earlyStoppingDelay property: Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
      Parameters:
      earlyStoppingDelay - the earlyStoppingDelay value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • earlyStoppingPatience

      public String earlyStoppingPatience()
      Get the earlyStoppingPatience property: Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
      Returns:
      the earlyStoppingPatience value.

    • withEarlyStoppingPatience

      public ImageModelDistributionSettings withEarlyStoppingPatience(String earlyStoppingPatience)
      Set the earlyStoppingPatience property: Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
      Parameters:
      earlyStoppingPatience - the earlyStoppingPatience value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • evaluationFrequency

      public String evaluationFrequency()
      Get the evaluationFrequency property: Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
      Returns:
      the evaluationFrequency value.

    • withEvaluationFrequency

      public ImageModelDistributionSettings withEvaluationFrequency(String evaluationFrequency)
      Set the evaluationFrequency property: Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
      Parameters:
      evaluationFrequency - the evaluationFrequency value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • enableOnnxNormalization

      public String enableOnnxNormalization()
      Get the enableOnnxNormalization property: Enable normalization when exporting ONNX model.
      Returns:
      the enableOnnxNormalization value.

    • withEnableOnnxNormalization

      public ImageModelDistributionSettings withEnableOnnxNormalization(String enableOnnxNormalization)
      Set the enableOnnxNormalization property: Enable normalization when exporting ONNX model.
      Parameters:
      enableOnnxNormalization - the enableOnnxNormalization value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • gradientAccumulationStep

      public String gradientAccumulationStep()
      Get the gradientAccumulationStep property: Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
      Returns:
      the gradientAccumulationStep value.

    • withGradientAccumulationStep

      public ImageModelDistributionSettings withGradientAccumulationStep(String gradientAccumulationStep)
      Set the gradientAccumulationStep property: Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
      Parameters:
      gradientAccumulationStep - the gradientAccumulationStep value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • layersToFreeze

      public String layersToFreeze()
      Get the layersToFreeze property: Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
      Returns:
      the layersToFreeze value.

    • withLayersToFreeze

      public ImageModelDistributionSettings withLayersToFreeze(String layersToFreeze)
      Set the layersToFreeze property: Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
      Parameters:
      layersToFreeze - the layersToFreeze value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • learningRate

      public String learningRate()
      Get the learningRate property: Initial learning rate. Must be a float in the range [0, 1].
      Returns:
      the learningRate value.

    • withLearningRate

      public ImageModelDistributionSettings withLearningRate(String learningRate)
      Set the learningRate property: Initial learning rate. Must be a float in the range [0, 1].
      Parameters:
      learningRate - the learningRate value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • learningRateScheduler

      public String learningRateScheduler()
      Get the learningRateScheduler property: Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
      Returns:
      the learningRateScheduler value.

    • withLearningRateScheduler

      public ImageModelDistributionSettings withLearningRateScheduler(String learningRateScheduler)
      Set the learningRateScheduler property: Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
      Parameters:
      learningRateScheduler - the learningRateScheduler value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • modelName

      public String modelName()
      Get the modelName property: Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
      Returns:
      the modelName value.

    • withModelName

      public ImageModelDistributionSettings withModelName(String modelName)
      Set the modelName property: Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
      Parameters:
      modelName - the modelName value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • momentum

      public String momentum()
      Get the momentum property: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
      Returns:
      the momentum value.

    • withMomentum

      public ImageModelDistributionSettings withMomentum(String momentum)
      Set the momentum property: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
      Parameters:
      momentum - the momentum value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • nesterov

      public String nesterov()
      Get the nesterov property: Enable nesterov when optimizer is 'sgd'.
      Returns:
      the nesterov value.

    • withNesterov

      public ImageModelDistributionSettings withNesterov(String nesterov)
      Set the nesterov property: Enable nesterov when optimizer is 'sgd'.
      Parameters:
      nesterov - the nesterov value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • numberOfEpochs

      public String numberOfEpochs()
      Get the numberOfEpochs property: Number of training epochs. Must be a positive integer.
      Returns:
      the numberOfEpochs value.

    • withNumberOfEpochs

      public ImageModelDistributionSettings withNumberOfEpochs(String numberOfEpochs)
      Set the numberOfEpochs property: Number of training epochs. Must be a positive integer.
      Parameters:
      numberOfEpochs - the numberOfEpochs value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • numberOfWorkers

      public String numberOfWorkers()
      Get the numberOfWorkers property: Number of data loader workers. Must be a non-negative integer.
      Returns:
      the numberOfWorkers value.

    • withNumberOfWorkers

      public ImageModelDistributionSettings withNumberOfWorkers(String numberOfWorkers)
      Set the numberOfWorkers property: Number of data loader workers. Must be a non-negative integer.
      Parameters:
      numberOfWorkers - the numberOfWorkers value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • optimizer

      public String optimizer()
      Get the optimizer property: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
      Returns:
      the optimizer value.

    • withOptimizer

      public ImageModelDistributionSettings withOptimizer(String optimizer)
      Set the optimizer property: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
      Parameters:
      optimizer - the optimizer value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • randomSeed

      public String randomSeed()
      Get the randomSeed property: Random seed to be used when using deterministic training.
      Returns:
      the randomSeed value.

    • withRandomSeed

      public ImageModelDistributionSettings withRandomSeed(String randomSeed)
      Set the randomSeed property: Random seed to be used when using deterministic training.
      Parameters:
      randomSeed - the randomSeed value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • stepLRGamma

      public String stepLRGamma()
      Get the stepLRGamma property: Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
      Returns:
      the stepLRGamma value.

    • withStepLRGamma

      public ImageModelDistributionSettings withStepLRGamma(String stepLRGamma)
      Set the stepLRGamma property: Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
      Parameters:
      stepLRGamma - the stepLRGamma value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • stepLRStepSize

      public String stepLRStepSize()
      Get the stepLRStepSize property: Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
      Returns:
      the stepLRStepSize value.

    • withStepLRStepSize

      public ImageModelDistributionSettings withStepLRStepSize(String stepLRStepSize)
      Set the stepLRStepSize property: Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
      Parameters:
      stepLRStepSize - the stepLRStepSize value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • trainingBatchSize

      public String trainingBatchSize()
      Get the trainingBatchSize property: Training batch size. Must be a positive integer.
      Returns:
      the trainingBatchSize value.

    • withTrainingBatchSize

      public ImageModelDistributionSettings withTrainingBatchSize(String trainingBatchSize)
      Set the trainingBatchSize property: Training batch size. Must be a positive integer.
      Parameters:
      trainingBatchSize - the trainingBatchSize value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • validationBatchSize

      public String validationBatchSize()
      Get the validationBatchSize property: Validation batch size. Must be a positive integer.
      Returns:
      the validationBatchSize value.

    • withValidationBatchSize

      public ImageModelDistributionSettings withValidationBatchSize(String validationBatchSize)
      Set the validationBatchSize property: Validation batch size. Must be a positive integer.
      Parameters:
      validationBatchSize - the validationBatchSize value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • warmupCosineLRCycles

      public String warmupCosineLRCycles()
      Get the warmupCosineLRCycles property: Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
      Returns:
      the warmupCosineLRCycles value.

    • withWarmupCosineLRCycles

      public ImageModelDistributionSettings withWarmupCosineLRCycles(String warmupCosineLRCycles)
      Set the warmupCosineLRCycles property: Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
      Parameters:
      warmupCosineLRCycles - the warmupCosineLRCycles value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • warmupCosineLRWarmupEpochs

      public String warmupCosineLRWarmupEpochs()
      Get the warmupCosineLRWarmupEpochs property: Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
      Returns:
      the warmupCosineLRWarmupEpochs value.

    • withWarmupCosineLRWarmupEpochs

      public ImageModelDistributionSettings withWarmupCosineLRWarmupEpochs(String warmupCosineLRWarmupEpochs)
      Set the warmupCosineLRWarmupEpochs property: Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
      Parameters:
      warmupCosineLRWarmupEpochs - the warmupCosineLRWarmupEpochs value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • weightDecay

      public String weightDecay()
      Get the weightDecay property: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
      Returns:
      the weightDecay value.

    • withWeightDecay

      public ImageModelDistributionSettings withWeightDecay(String weightDecay)
      Set the weightDecay property: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
      Parameters:
      weightDecay - the weightDecay value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • validate

      public void validate()
      Validates the instance.
      Throws:
      IllegalArgumentException - thrown if the instance is not valid.

    • toJson

      public com.azure.json.JsonWriter toJson(com.azure.json.JsonWriter jsonWriter) throws IOException
      Specified by:
      toJson in interface com.azure.json.JsonSerializable<ImageModelDistributionSettings>
      Throws:
      IOException

    • fromJson

      public static ImageModelDistributionSettings fromJson(com.azure.json.JsonReader jsonReader) throws IOException
      Reads an instance of ImageModelDistributionSettings from the JsonReader.
      Parameters:
      jsonReader - The JsonReader being read.
      Returns:
      An instance of ImageModelDistributionSettings if the JsonReader was pointing to an instance of it, or null if it was pointing to JSON null.
      Throws:
      IOException - If an error occurs while reading the ImageModelDistributionSettings.