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ImageModelDistributionSettings Class

Definition

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)";
```&lt;/example&gt;
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.
public class ImageModelDistributionSettings : System.ClientModel.Primitives.IJsonModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettings>, System.ClientModel.Primitives.IPersistableModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettings>
public class ImageModelDistributionSettings
type ImageModelDistributionSettings = class
    interface IJsonModel<ImageModelDistributionSettings>
    interface IPersistableModel<ImageModelDistributionSettings>
type ImageModelDistributionSettings = class
Public Class ImageModelDistributionSettings
Implements IJsonModel(Of ImageModelDistributionSettings), IPersistableModel(Of ImageModelDistributionSettings)
Public Class ImageModelDistributionSettings
Inheritance
ImageModelDistributionSettings
Derived
Implements

Constructors

ImageModelDistributionSettings()

Initializes a new instance of ImageModelDistributionSettings.

Properties

AmsGradient

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

Augmentations

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'.

NumberOfEpochs

Number of training epochs. Must be a positive integer.

NumberOfWorkers

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.

ValidationBatchSize

Validation batch size. Must be a positive integer.

WarmupCosineLRCycles

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

WarmupCosineLRWarmupEpochs

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].

Explicit Interface Implementations

IJsonModel<ImageModelDistributionSettings>.Create(Utf8JsonReader, ModelReaderWriterOptions)

Reads one JSON value (including objects or arrays) from the provided reader and converts it to a model.

IJsonModel<ImageModelDistributionSettings>.Write(Utf8JsonWriter, ModelReaderWriterOptions)

Writes the model to the provided Utf8JsonWriter.

IPersistableModel<ImageModelDistributionSettings>.Create(BinaryData, ModelReaderWriterOptions)

Converts the provided BinaryData into a model.

IPersistableModel<ImageModelDistributionSettings>.GetFormatFromOptions(ModelReaderWriterOptions)

Gets the data interchange format (JSON, Xml, etc) that the model uses when communicating with the service.

IPersistableModel<ImageModelDistributionSettings>.Write(ModelReaderWriterOptions)

Writes the model into a BinaryData.

Applies to