ImageModelSettings Class

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

ImageModelSettings.cs

Settings used for training the model. 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 ImageModelSettings

type ImageModelSettings = class

Public Class ImageModelSettings

Inheritance

Object

ImageModelSettings

Derived

Azure.ResourceManager.MachineLearning.Models.ImageModelSettingsClassification

Azure.ResourceManager.MachineLearning.Models.ImageModelSettingsObjectDetection

Constructors

ImageModelSettings()

Initializes a new instance of ImageModelSettings.

Properties

AdvancedSettings	Settings for advanced scenarios.
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].
CheckpointFrequency	Frequency to store model checkpoints. Must be a positive integer.
CheckpointModel	The pretrained checkpoint model for incremental training.
CheckpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.
Distributed	Whether to use distributed 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.
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].