ImageModelSettingsClassification Class

Model settings for AutoML Image Classification tasks.

Inheritance

azure.ai.ml.entities._job.automl.image.image_model_settings.ImageModelDistributionSettings

ImageModelSettingsClassification

Constructor

ImageModelSettingsClassification(*, advanced_settings: str | None = None, ams_gradient: bool | None = None, beta1: float | None = None, beta2: float | None = None, checkpoint_frequency: int | None = None, checkpoint_run_id: str | None = None, distributed: bool | None = None, early_stopping: bool | None = None, early_stopping_delay: int | None = None, early_stopping_patience: int | None = None, enable_onnx_normalization: bool | None = None, evaluation_frequency: int | None = None, gradient_accumulation_step: int | None = None, layers_to_freeze: int | None = None, learning_rate: float | None = None, learning_rate_scheduler: LearningRateScheduler | None = None, model_name: str | None = None, momentum: float | None = None, nesterov: bool | None = None, number_of_epochs: int | None = None, number_of_workers: int | None = None, optimizer: StochasticOptimizer | None = None, random_seed: int | None = None, step_lr_gamma: float | None = None, step_lr_step_size: int | None = None, training_batch_size: int | None = None, validation_batch_size: int | None = None, warmup_cosine_lr_cycles: float | None = None, warmup_cosine_lr_warmup_epochs: int | None = None, weight_decay: float | None = None, training_crop_size: int | None = None, validation_crop_size: int | None = None, validation_resize_size: int | None = None, weighted_loss: int | None = None, **kwargs: Any)

Parameters

Name	Description
advanced_settings Required	str Settings for advanced scenarios.
ams_gradient Required	bool Enable AMSGrad when optimizer is 'adam' or 'adamw'.
beta1 Required	float Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
beta2 Required	float Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
checkpoint_frequency Required	int Frequency to store model checkpoints. Must be a positive integer.
checkpoint_run_id Required	str The id of a previous run that has a pretrained checkpoint for incremental training.
distributed Required	bool Whether to use distributed training.
early_stopping Required	bool Enable early stopping logic during training.
early_stopping_delay Required	int Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
early_stopping_patience Required	int Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
enable_onnx_normalization Required	bool Enable normalization when exporting ONNX model.
evaluation_frequency Required	int Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
gradient_accumulation_step Required	int 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.
layers_to_freeze Required	int 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.
learning_rate Required	float Initial learning rate. Must be a float in the range [0, 1].
learning_rate_scheduler Required	str or LearningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. Possible values include: "None", "WarmupCosine", "Step".
model_name Required	str 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 Required	float Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
nesterov Required	bool Enable nesterov when optimizer is 'sgd'.
number_of_epochs Required	int Number of training epochs. Must be a positive integer.
number_of_workers Required	int Number of data loader workers. Must be a non-negative integer.
optimizer Required	str or StochasticOptimizer Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
random_seed Required	int Random seed to be used when using deterministic training.
step_lr_gamma Required	float Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
step_lr_step_size Required	int Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
training_batch_size Required	int Training batch size. Must be a positive integer.
validation_batch_size Required	int Validation batch size. Must be a positive integer.
warmup_cosine_lr_cycles Required	float Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
warmup_cosine_lr_warmup_epochs Required	int Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
weight_decay Required	float Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
training_crop_size Required	int Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
validation_crop_size Required	int Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
validation_resize_size Required	int Image size to which to resize before cropping for validation dataset. Must be a positive integer.
weighted_loss Required	int 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.

Keyword-Only Parameters

Name	Description
advanced_settings Required
ams_gradient Required
beta1 Required
beta2 Required
checkpoint_frequency Required
checkpoint_run_id Required
distributed Required
early_stopping Required
early_stopping_delay Required
early_stopping_patience Required
enable_onnx_normalization Required
evaluation_frequency Required
gradient_accumulation_step Required
layers_to_freeze Required
learning_rate Required
learning_rate_scheduler Required
model_name Required
momentum Required
nesterov Required
number_of_epochs Required
number_of_workers Required
optimizer Required
random_seed Required
step_lr_gamma Required
step_lr_step_size Required
training_batch_size Required
validation_batch_size Required
warmup_cosine_lr_cycles Required
warmup_cosine_lr_warmup_epochs Required
weight_decay Required
training_crop_size Required
validation_crop_size Required
validation_resize_size Required
weighted_loss Required

Examples

Defining the automl image classification model settings.

   from azure.ai.ml import automl

   image_classification_model_settings = automl.ImageModelSettingsClassification(
       checkpoint_frequency=5,
       early_stopping=False,
       gradient_accumulation_step=2,
   )