ImageModelDistributionSettingsClassificationArgs

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.
@property amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'.
@property augmentations Settings for using Augmentations.
@property beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
@property beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
@property distributed Whether to use distributer training.
@property earlyStopping Enable early stopping logic during training.
@property earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
is tracked for early stopping. Must be a positive integer.
@property earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
the run is stopped. Must be a positive integer.
@property enableOnnxNormalization Enable normalization when exporting ONNX model.
@property evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
@property 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.
@property 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.
@property learningRate Initial learning rate. Must be a float in the range [0, 1].
@property learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
@property 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.
@property momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
@property nesterov Enable nesterov when optimizer is 'sgd'.
@property numberOfEpochs Number of training epochs. Must be a positive integer.
@property numberOfWorkers Number of data loader workers. Must be a non-negative integer.
@property optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
@property randomSeed Random seed to be used when using deterministic training.
@property stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
@property stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
@property trainingBatchSize Training batch size. Must be a positive integer.
@property trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
@property validationBatchSize Validation batch size. Must be a positive integer.
@property validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
@property validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer.
@property warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
@property warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
@property weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
@property 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.