Package version
Enable AMSGrad when optimizer is 'adam' or 'adamw'.
Settings for using Augmentations.
Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
Whether to use distributer training.
Enable early stopping logic during training.
Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
Enable normalization when exporting ONNX model.
Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
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.
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.
Initial learning rate. Must be a float in the range [0, 1].
Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
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.
Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
Enable nesterov when optimizer is 'sgd'.
Number of training epochs. Must be a positive integer.
Number of data loader workers. Must be a non-negative integer.
Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
Random seed to be used when using deterministic training.
If validation data is not defined, this specifies the split ratio for splitting train data into random train and validation subsets. Must be a float in the range [0, 1].
Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
Training batch size. Must be a positive integer.
Validation batch size. Must be a positive integer.
Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
Generated using TypeDoc
Distribution expressions to sweep over values of model settings.
Some examples are:
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.
ModelName = "choice('seresnext', 'resnest50')"; LearningRate = "uniform(0.001, 0.01)"; LayersToFreeze = "choice(0, 2)";