ImageModelSettingsClassification Class

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

ImageModelSettingsClassification.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 ImageModelSettingsClassification : Azure.ResourceManager.MachineLearning.Models.ImageModelSettings

type ImageModelSettingsClassification = class
    inherit ImageModelSettings

Public Class ImageModelSettingsClassification
Inherits ImageModelSettings

Inheritance

Object

ImageModelSettings

ImageModelSettingsClassification

Constructors

ImageModelSettingsClassification()

Initializes a new instance of ImageModelSettingsClassification.

Properties

AdvancedSettings	Settings for advanced scenarios. (Inherited from ImageModelSettings)
AmsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'. (Inherited from ImageModelSettings)
Augmentations	Settings for using Augmentations. (Inherited from ImageModelSettings)
Beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
Beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
CheckpointFrequency	Frequency to store model checkpoints. Must be a positive integer. (Inherited from ImageModelSettings)
CheckpointModel	The pretrained checkpoint model for incremental training. (Inherited from ImageModelSettings)
CheckpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training. (Inherited from ImageModelSettings)
Distributed	Whether to use distributed training. (Inherited from ImageModelSettings)
EarlyStopping	Enable early stopping logic during training. (Inherited from ImageModelSettings)
EarlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. (Inherited from ImageModelSettings)
EarlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. (Inherited from ImageModelSettings)
EnableOnnxNormalization	Enable normalization when exporting ONNX model. (Inherited from ImageModelSettings)
EvaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. (Inherited from ImageModelSettings)
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. (Inherited from ImageModelSettings)
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. (Inherited from ImageModelSettings)
LearningRate	Initial learning rate. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
LearningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. (Inherited from ImageModelSettings)
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. (Inherited from ImageModelSettings)
Momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
Nesterov	Enable nesterov when optimizer is 'sgd'. (Inherited from ImageModelSettings)
NumberOfEpochs	Number of training epochs. Must be a positive integer. (Inherited from ImageModelSettings)
NumberOfWorkers	Number of data loader workers. Must be a non-negative integer. (Inherited from ImageModelSettings)
Optimizer	Type of optimizer. (Inherited from ImageModelSettings)
RandomSeed	Random seed to be used when using deterministic training. (Inherited from ImageModelSettings)
StepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
StepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer. (Inherited from ImageModelSettings)
TrainingBatchSize	Training batch size. Must be a positive integer. (Inherited from ImageModelSettings)
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. (Inherited from ImageModelSettings)
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.
WarmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
WarmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. (Inherited from ImageModelSettings)
WeightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. (Inherited from ImageModelSettings)
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.