ImageClassificationBase Class

Definition

Namespace:

Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401

Assembly:

Az.MachineLearningServices.private.dll

[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageClassificationBaseTypeConverter))]
public class ImageClassificationBase : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageClassificationBase, Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Runtime.IValidates

[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageClassificationBaseTypeConverter))>]
type ImageClassificationBase = class
    interface IImageClassificationBase
    interface IJsonSerializable
    interface IImageVertical
    interface IValidates

Public Class ImageClassificationBase
Implements IImageClassificationBase, IValidates

Inheritance

Object

ImageClassificationBase

Attributes

TypeConverterAttribute

Implements

IImageClassificationBase IImageVertical IJsonSerializable IValidates

Constructors

ImageClassificationBase()

Creates an new ImageClassificationBase instance.

Properties

EarlyTerminationDelayEvaluation	Number of intervals by which to delay the first evaluation.
EarlyTerminationEvaluationInterval	Interval (number of runs) between policy evaluations.
EarlyTerminationPolicyType	[Required] Name of policy configuration
LimitSetting	[Required] Limit settings for the AutoML job.
LimitSettingMaxConcurrentTrial	Maximum number of concurrent AutoML iterations.
LimitSettingMaxTrial	Maximum number of AutoML iterations.
LimitSettingTimeout	AutoML job timeout.
ModelSettingAdvancedSetting	Settings for advanced scenarios.
ModelSettingAmsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.
ModelSettingAugmentation	Settings for using Augmentations.
ModelSettingBeta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
ModelSettingBeta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
ModelSettingCheckpointFrequency	Frequency to store model checkpoints. Must be a positive integer.
ModelSettingCheckpointModelDescription	Description for the input.
ModelSettingCheckpointModelJobInputType	[Required] Specifies the type of job.
ModelSettingCheckpointModelMode	Input Asset Delivery Mode.
ModelSettingCheckpointModelUri	[Required] Input Asset URI.
ModelSettingCheckpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.
ModelSettingDistributed	Whether to use distributed training.
ModelSettingEarlyStopping	Enable early stopping logic during training.
ModelSettingEarlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
ModelSettingEarlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
ModelSettingEnableOnnxNormalization	Enable normalization when exporting ONNX model.
ModelSettingEvaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
ModelSettingGradientAccumulationStep	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.
ModelSettingLayersToFreeze	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.
ModelSettingLearningRate	Initial learning rate. Must be a float in the range [0, 1].
ModelSettingLearningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
ModelSettingModelName	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.
ModelSettingMomentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
ModelSettingNesterov	Enable nesterov when optimizer is 'sgd'.
ModelSettingNumberOfEpoch	Number of training epochs. Must be a positive integer.
ModelSettingNumberOfWorker	Number of data loader workers. Must be a non-negative integer.
ModelSettingOptimizer	Type of optimizer.
ModelSettingRandomSeed	Random seed to be used when using deterministic training.
ModelSettingStepLrGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
ModelSettingStepLrStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
ModelSettingTrainingBatchSize	Training batch size. Must be a positive integer.
ModelSettingTrainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
ModelSettingValidationBatchSize	Validation batch size. Must be a positive integer.
ModelSettingValidationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
ModelSettingValidationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.
ModelSettingWarmupCosineLrCycle	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
ModelSettingWarmupCosineLrWarmupEpoch	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
ModelSettingWeightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
ModelSettingWeightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.
SearchSpace	Search space for sampling different combinations of models and their hyperparameters.
SweepSetting	Model sweeping and hyperparameter sweeping related settings.
SweepSettingEarlyTermination	Type of early termination policy.
SweepSettingSamplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.
ValidationData	Validation data inputs.
ValidationDataDescription	Description for the input.
ValidationDataJobInputType	[Required] Specifies the type of job.
ValidationDataMode	Input Asset Delivery Mode.
ValidationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
ValidationDataUri	[Required] Input Asset URI.

Methods

DeserializeFromDictionary(IDictionary)	Deserializes a IDictionary into an instance of ImageClassificationBase.
DeserializeFromPSObject(PSObject)	Deserializes a PSObject into an instance of ImageClassificationBase.
FromJson(JsonNode)	Deserializes a JsonNode into an instance of Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageClassificationBase.
FromJsonString(String)	Creates a new instance of ImageClassificationBase, deserializing the content from a json string.
ToJson(JsonObject, SerializationMode)	Serializes this instance of ImageClassificationBase into a JsonNode.
ToJsonString()	Serializes this instance to a json string.
ToString()
Validate(IEventListener)	Validates that this object meets the validation criteria.