ImageModelDistributionSettingsClassification Class

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

ImageModelDistributionSettingsClassification.cs

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)";
```</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.

public class ImageModelDistributionSettingsClassification : Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettings

type ImageModelDistributionSettingsClassification = class
    inherit ImageModelDistributionSettings

Public Class ImageModelDistributionSettingsClassification
Inherits ImageModelDistributionSettings

Inheritance

Object

ImageModelDistributionSettings

ImageModelDistributionSettingsClassification

Constructors

ImageModelDistributionSettingsClassification()

Initializes a new instance of ImageModelDistributionSettingsClassification.

Properties

AmsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'. (Inherited from ImageModelDistributionSettings)
Augmentations	Settings for using Augmentations. (Inherited from ImageModelDistributionSettings)
Beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings)
Beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings)
Distributed	Whether to use distributer training. (Inherited from ImageModelDistributionSettings)
EarlyStopping	Enable early stopping logic during training. (Inherited from ImageModelDistributionSettings)
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 ImageModelDistributionSettings)
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 ImageModelDistributionSettings)
EnableOnnxNormalization	Enable normalization when exporting ONNX model. (Inherited from ImageModelDistributionSettings)
EvaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. (Inherited from ImageModelDistributionSettings)
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 ImageModelDistributionSettings)
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 ImageModelDistributionSettings)
LearningRate	Initial learning rate. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings)
LearningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. (Inherited from ImageModelDistributionSettings)
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 ImageModelDistributionSettings)
Momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings)
Nesterov	Enable nesterov when optimizer is 'sgd'. (Inherited from ImageModelDistributionSettings)
NumberOfEpochs	Number of training epochs. Must be a positive integer. (Inherited from ImageModelDistributionSettings)
NumberOfWorkers	Number of data loader workers. Must be a non-negative integer. (Inherited from ImageModelDistributionSettings)
Optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. (Inherited from ImageModelDistributionSettings)
RandomSeed	Random seed to be used when using deterministic training. (Inherited from ImageModelDistributionSettings)
StepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. (Inherited from ImageModelDistributionSettings)
StepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer. (Inherited from ImageModelDistributionSettings)
TrainingBatchSize	Training batch size. Must be a positive integer. (Inherited from ImageModelDistributionSettings)
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 ImageModelDistributionSettings)
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 ImageModelDistributionSettings)
WarmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. (Inherited from ImageModelDistributionSettings)
WeightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. (Inherited from ImageModelDistributionSettings)
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.