ImageModelDistributionSettings Class
Definition
Important
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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>
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 ImageModelDistributionSettingstype ImageModelDistributionSettings = class
interface IJsonModel<ImageModelDistributionSettings>
interface IPersistableModel<ImageModelDistributionSettings>type ImageModelDistributionSettings = classPublic 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. |
