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ImageModelDistributionSettingsClassification Class

Definition

Distribution expressions to sweep over values of model settings. Some examples are:

ModelName = "choice('seresnext', 'resnest50')";
LearningRate = "uniform(0.001, 0.01)";
LayersToFreeze = "choice(0, 2)";
```</example>
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.
[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageModelDistributionSettingsClassificationTypeConverter))]
public class ImageModelDistributionSettingsClassification : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageModelDistributionSettingsClassification, Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Runtime.IValidates
[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageModelDistributionSettingsClassificationTypeConverter))>]
type ImageModelDistributionSettingsClassification = class
    interface IImageModelDistributionSettingsClassification
    interface IJsonSerializable
    interface IImageModelDistributionSettings
    interface IValidates
Public Class ImageModelDistributionSettingsClassification
Implements IImageModelDistributionSettingsClassification, IValidates
Inheritance
ImageModelDistributionSettingsClassification
Attributes
Implements

Constructors

ImageModelDistributionSettingsClassification()

Creates an new ImageModelDistributionSettingsClassification instance.

Properties

AmsGradient

Enable AMSGrad when optimizer is 'adam' or 'adamw'.

Augmentation

Settings for using Augmentations.

Beta1

Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].

Beta2

Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].

Distributed

Whether to use distributer training.

EarlyStopping

Enable early stopping logic during training.

EarlyStoppingDelay

Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.

EarlyStoppingPatience

Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.

EnableOnnxNormalization

Enable normalization when exporting ONNX model.

EvaluationFrequency

Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.

GradientAccumulationStep

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.

LayersToFreeze

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.

LearningRate

Initial learning rate. Must be a float in the range [0, 1].

LearningRateScheduler

Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.

ModelName

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.

Momentum

Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].

Nesterov

Enable nesterov when optimizer is 'sgd'.

NumberOfEpoch

Number of training epochs. Must be a positive integer.

NumberOfWorker

Number of data loader workers. Must be a non-negative integer.

Optimizer

Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.

RandomSeed

Random seed to be used when using deterministic training.

StepLrGamma

Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].

StepLrStepSize

Value of step size when learning rate scheduler is 'step'. Must be a positive integer.

TrainingBatchSize

Training batch size. Must be a positive integer.

TrainingCropSize

Image crop size that is input to the neural network for the training dataset. Must be a positive integer.

ValidationBatchSize

Validation batch size. Must be a positive integer.

ValidationCropSize

Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.

ValidationResizeSize

Image size to which to resize before cropping for validation dataset. Must be a positive integer.

WarmupCosineLrCycle

Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].

WarmupCosineLrWarmupEpoch

Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.

WeightDecay

Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

WeightedLoss

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.

Methods

DeserializeFromDictionary(IDictionary)

Deserializes a IDictionary into an instance of ImageModelDistributionSettingsClassification.

DeserializeFromPSObject(PSObject)

Deserializes a PSObject into an instance of ImageModelDistributionSettingsClassification.

FromJson(JsonNode)

Deserializes a JsonNode into an instance of Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageModelDistributionSettingsClassification.

FromJsonString(String)

Creates a new instance of ImageModelDistributionSettingsClassification, deserializing the content from a json string.

ToJson(JsonObject, SerializationMode)

Serializes this instance of ImageModelDistributionSettingsClassification into a JsonNode.

ToJsonString()

Serializes this instance to a json string.

ToString()
Validate(IEventListener)

Validates that this object meets the validation criteria.

Applies to