com.pulumi.azurenative.machinelearningservices.outputs.ImageModelDistributionSettingsClassificationResponse Maven / Gradle / Ivy
// *** WARNING: this file was generated by pulumi-java-gen. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***
package com.pulumi.azurenative.machinelearningservices.outputs;
import com.pulumi.core.annotations.CustomType;
import java.lang.String;
import java.util.Objects;
import java.util.Optional;
import javax.annotation.Nullable;
@CustomType
public final class ImageModelDistributionSettingsClassificationResponse {
/**
* @return Enable AMSGrad when optimizer is 'adam' or 'adamw'.
*
*/
private @Nullable String amsGradient;
/**
* @return Settings for using Augmentations.
*
*/
private @Nullable String augmentations;
/**
* @return Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
*/
private @Nullable String beta1;
/**
* @return Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
*/
private @Nullable String beta2;
/**
* @return Whether to use distributer training.
*
*/
private @Nullable String distributed;
/**
* @return Enable early stopping logic during training.
*
*/
private @Nullable String earlyStopping;
/**
* @return Minimum number of epochs or validation evaluations to wait before primary metric improvement
* is tracked for early stopping. Must be a positive integer.
*
*/
private @Nullable String earlyStoppingDelay;
/**
* @return Minimum number of epochs or validation evaluations with no primary metric improvement before
* the run is stopped. Must be a positive integer.
*
*/
private @Nullable String earlyStoppingPatience;
/**
* @return Enable normalization when exporting ONNX model.
*
*/
private @Nullable String enableOnnxNormalization;
/**
* @return Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
*
*/
private @Nullable String evaluationFrequency;
/**
* @return 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.
*
*/
private @Nullable String gradientAccumulationStep;
/**
* @return 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.
*
*/
private @Nullable String layersToFreeze;
/**
* @return Initial learning rate. Must be a float in the range [0, 1].
*
*/
private @Nullable String learningRate;
/**
* @return Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
*
*/
private @Nullable String learningRateScheduler;
/**
* @return 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.
*
*/
private @Nullable String modelName;
/**
* @return Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
*
*/
private @Nullable String momentum;
/**
* @return Enable nesterov when optimizer is 'sgd'.
*
*/
private @Nullable String nesterov;
/**
* @return Number of training epochs. Must be a positive integer.
*
*/
private @Nullable String numberOfEpochs;
/**
* @return Number of data loader workers. Must be a non-negative integer.
*
*/
private @Nullable String numberOfWorkers;
/**
* @return Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
*
*/
private @Nullable String optimizer;
/**
* @return Random seed to be used when using deterministic training.
*
*/
private @Nullable String randomSeed;
/**
* @return Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
*
*/
private @Nullable String stepLRGamma;
/**
* @return Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
*
*/
private @Nullable String stepLRStepSize;
/**
* @return Training batch size. Must be a positive integer.
*
*/
private @Nullable String trainingBatchSize;
/**
* @return Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
*
*/
private @Nullable String trainingCropSize;
/**
* @return Validation batch size. Must be a positive integer.
*
*/
private @Nullable String validationBatchSize;
/**
* @return Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
*
*/
private @Nullable String validationCropSize;
/**
* @return Image size to which to resize before cropping for validation dataset. Must be a positive integer.
*
*/
private @Nullable String validationResizeSize;
/**
* @return Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
*
*/
private @Nullable String warmupCosineLRCycles;
/**
* @return Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
*
*/
private @Nullable String warmupCosineLRWarmupEpochs;
/**
* @return Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
*
*/
private @Nullable String weightDecay;
/**
* @return 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.
*
*/
private @Nullable String weightedLoss;
private ImageModelDistributionSettingsClassificationResponse() {}
/**
* @return Enable AMSGrad when optimizer is 'adam' or 'adamw'.
*
*/
public Optional amsGradient() {
return Optional.ofNullable(this.amsGradient);
}
/**
* @return Settings for using Augmentations.
*
*/
public Optional augmentations() {
return Optional.ofNullable(this.augmentations);
}
/**
* @return Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
*/
public Optional beta1() {
return Optional.ofNullable(this.beta1);
}
/**
* @return Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
*/
public Optional beta2() {
return Optional.ofNullable(this.beta2);
}
/**
* @return Whether to use distributer training.
*
*/
public Optional distributed() {
return Optional.ofNullable(this.distributed);
}
/**
* @return Enable early stopping logic during training.
*
*/
public Optional earlyStopping() {
return Optional.ofNullable(this.earlyStopping);
}
/**
* @return Minimum number of epochs or validation evaluations to wait before primary metric improvement
* is tracked for early stopping. Must be a positive integer.
*
*/
public Optional earlyStoppingDelay() {
return Optional.ofNullable(this.earlyStoppingDelay);
}
/**
* @return Minimum number of epochs or validation evaluations with no primary metric improvement before
* the run is stopped. Must be a positive integer.
*
*/
public Optional earlyStoppingPatience() {
return Optional.ofNullable(this.earlyStoppingPatience);
}
/**
* @return Enable normalization when exporting ONNX model.
*
*/
public Optional enableOnnxNormalization() {
return Optional.ofNullable(this.enableOnnxNormalization);
}
/**
* @return Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
*
*/
public Optional evaluationFrequency() {
return Optional.ofNullable(this.evaluationFrequency);
}
/**
* @return 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.
*
*/
public Optional gradientAccumulationStep() {
return Optional.ofNullable(this.gradientAccumulationStep);
}
/**
* @return 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.
*
*/
public Optional layersToFreeze() {
return Optional.ofNullable(this.layersToFreeze);
}
/**
* @return Initial learning rate. Must be a float in the range [0, 1].
*
*/
public Optional learningRate() {
return Optional.ofNullable(this.learningRate);
}
/**
* @return Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
*
*/
public Optional learningRateScheduler() {
return Optional.ofNullable(this.learningRateScheduler);
}
/**
* @return 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.
*
*/
public Optional modelName() {
return Optional.ofNullable(this.modelName);
}
/**
* @return Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
*
*/
public Optional momentum() {
return Optional.ofNullable(this.momentum);
}
/**
* @return Enable nesterov when optimizer is 'sgd'.
*
*/
public Optional nesterov() {
return Optional.ofNullable(this.nesterov);
}
/**
* @return Number of training epochs. Must be a positive integer.
*
*/
public Optional numberOfEpochs() {
return Optional.ofNullable(this.numberOfEpochs);
}
/**
* @return Number of data loader workers. Must be a non-negative integer.
*
*/
public Optional numberOfWorkers() {
return Optional.ofNullable(this.numberOfWorkers);
}
/**
* @return Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
*
*/
public Optional optimizer() {
return Optional.ofNullable(this.optimizer);
}
/**
* @return Random seed to be used when using deterministic training.
*
*/
public Optional randomSeed() {
return Optional.ofNullable(this.randomSeed);
}
/**
* @return Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
*
*/
public Optional stepLRGamma() {
return Optional.ofNullable(this.stepLRGamma);
}
/**
* @return Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
*
*/
public Optional stepLRStepSize() {
return Optional.ofNullable(this.stepLRStepSize);
}
/**
* @return Training batch size. Must be a positive integer.
*
*/
public Optional trainingBatchSize() {
return Optional.ofNullable(this.trainingBatchSize);
}
/**
* @return Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
*
*/
public Optional trainingCropSize() {
return Optional.ofNullable(this.trainingCropSize);
}
/**
* @return Validation batch size. Must be a positive integer.
*
*/
public Optional validationBatchSize() {
return Optional.ofNullable(this.validationBatchSize);
}
/**
* @return Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
*
*/
public Optional validationCropSize() {
return Optional.ofNullable(this.validationCropSize);
}
/**
* @return Image size to which to resize before cropping for validation dataset. Must be a positive integer.
*
*/
public Optional validationResizeSize() {
return Optional.ofNullable(this.validationResizeSize);
}
/**
* @return Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
*
*/
public Optional warmupCosineLRCycles() {
return Optional.ofNullable(this.warmupCosineLRCycles);
}
/**
* @return Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
*
*/
public Optional warmupCosineLRWarmupEpochs() {
return Optional.ofNullable(this.warmupCosineLRWarmupEpochs);
}
/**
* @return Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
*
*/
public Optional weightDecay() {
return Optional.ofNullable(this.weightDecay);
}
/**
* @return 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.
*
*/
public Optional weightedLoss() {
return Optional.ofNullable(this.weightedLoss);
}
public static Builder builder() {
return new Builder();
}
public static Builder builder(ImageModelDistributionSettingsClassificationResponse defaults) {
return new Builder(defaults);
}
@CustomType.Builder
public static final class Builder {
private @Nullable String amsGradient;
private @Nullable String augmentations;
private @Nullable String beta1;
private @Nullable String beta2;
private @Nullable String distributed;
private @Nullable String earlyStopping;
private @Nullable String earlyStoppingDelay;
private @Nullable String earlyStoppingPatience;
private @Nullable String enableOnnxNormalization;
private @Nullable String evaluationFrequency;
private @Nullable String gradientAccumulationStep;
private @Nullable String layersToFreeze;
private @Nullable String learningRate;
private @Nullable String learningRateScheduler;
private @Nullable String modelName;
private @Nullable String momentum;
private @Nullable String nesterov;
private @Nullable String numberOfEpochs;
private @Nullable String numberOfWorkers;
private @Nullable String optimizer;
private @Nullable String randomSeed;
private @Nullable String stepLRGamma;
private @Nullable String stepLRStepSize;
private @Nullable String trainingBatchSize;
private @Nullable String trainingCropSize;
private @Nullable String validationBatchSize;
private @Nullable String validationCropSize;
private @Nullable String validationResizeSize;
private @Nullable String warmupCosineLRCycles;
private @Nullable String warmupCosineLRWarmupEpochs;
private @Nullable String weightDecay;
private @Nullable String weightedLoss;
public Builder() {}
public Builder(ImageModelDistributionSettingsClassificationResponse defaults) {
Objects.requireNonNull(defaults);
this.amsGradient = defaults.amsGradient;
this.augmentations = defaults.augmentations;
this.beta1 = defaults.beta1;
this.beta2 = defaults.beta2;
this.distributed = defaults.distributed;
this.earlyStopping = defaults.earlyStopping;
this.earlyStoppingDelay = defaults.earlyStoppingDelay;
this.earlyStoppingPatience = defaults.earlyStoppingPatience;
this.enableOnnxNormalization = defaults.enableOnnxNormalization;
this.evaluationFrequency = defaults.evaluationFrequency;
this.gradientAccumulationStep = defaults.gradientAccumulationStep;
this.layersToFreeze = defaults.layersToFreeze;
this.learningRate = defaults.learningRate;
this.learningRateScheduler = defaults.learningRateScheduler;
this.modelName = defaults.modelName;
this.momentum = defaults.momentum;
this.nesterov = defaults.nesterov;
this.numberOfEpochs = defaults.numberOfEpochs;
this.numberOfWorkers = defaults.numberOfWorkers;
this.optimizer = defaults.optimizer;
this.randomSeed = defaults.randomSeed;
this.stepLRGamma = defaults.stepLRGamma;
this.stepLRStepSize = defaults.stepLRStepSize;
this.trainingBatchSize = defaults.trainingBatchSize;
this.trainingCropSize = defaults.trainingCropSize;
this.validationBatchSize = defaults.validationBatchSize;
this.validationCropSize = defaults.validationCropSize;
this.validationResizeSize = defaults.validationResizeSize;
this.warmupCosineLRCycles = defaults.warmupCosineLRCycles;
this.warmupCosineLRWarmupEpochs = defaults.warmupCosineLRWarmupEpochs;
this.weightDecay = defaults.weightDecay;
this.weightedLoss = defaults.weightedLoss;
}
@CustomType.Setter
public Builder amsGradient(@Nullable String amsGradient) {
this.amsGradient = amsGradient;
return this;
}
@CustomType.Setter
public Builder augmentations(@Nullable String augmentations) {
this.augmentations = augmentations;
return this;
}
@CustomType.Setter
public Builder beta1(@Nullable String beta1) {
this.beta1 = beta1;
return this;
}
@CustomType.Setter
public Builder beta2(@Nullable String beta2) {
this.beta2 = beta2;
return this;
}
@CustomType.Setter
public Builder distributed(@Nullable String distributed) {
this.distributed = distributed;
return this;
}
@CustomType.Setter
public Builder earlyStopping(@Nullable String earlyStopping) {
this.earlyStopping = earlyStopping;
return this;
}
@CustomType.Setter
public Builder earlyStoppingDelay(@Nullable String earlyStoppingDelay) {
this.earlyStoppingDelay = earlyStoppingDelay;
return this;
}
@CustomType.Setter
public Builder earlyStoppingPatience(@Nullable String earlyStoppingPatience) {
this.earlyStoppingPatience = earlyStoppingPatience;
return this;
}
@CustomType.Setter
public Builder enableOnnxNormalization(@Nullable String enableOnnxNormalization) {
this.enableOnnxNormalization = enableOnnxNormalization;
return this;
}
@CustomType.Setter
public Builder evaluationFrequency(@Nullable String evaluationFrequency) {
this.evaluationFrequency = evaluationFrequency;
return this;
}
@CustomType.Setter
public Builder gradientAccumulationStep(@Nullable String gradientAccumulationStep) {
this.gradientAccumulationStep = gradientAccumulationStep;
return this;
}
@CustomType.Setter
public Builder layersToFreeze(@Nullable String layersToFreeze) {
this.layersToFreeze = layersToFreeze;
return this;
}
@CustomType.Setter
public Builder learningRate(@Nullable String learningRate) {
this.learningRate = learningRate;
return this;
}
@CustomType.Setter
public Builder learningRateScheduler(@Nullable String learningRateScheduler) {
this.learningRateScheduler = learningRateScheduler;
return this;
}
@CustomType.Setter
public Builder modelName(@Nullable String modelName) {
this.modelName = modelName;
return this;
}
@CustomType.Setter
public Builder momentum(@Nullable String momentum) {
this.momentum = momentum;
return this;
}
@CustomType.Setter
public Builder nesterov(@Nullable String nesterov) {
this.nesterov = nesterov;
return this;
}
@CustomType.Setter
public Builder numberOfEpochs(@Nullable String numberOfEpochs) {
this.numberOfEpochs = numberOfEpochs;
return this;
}
@CustomType.Setter
public Builder numberOfWorkers(@Nullable String numberOfWorkers) {
this.numberOfWorkers = numberOfWorkers;
return this;
}
@CustomType.Setter
public Builder optimizer(@Nullable String optimizer) {
this.optimizer = optimizer;
return this;
}
@CustomType.Setter
public Builder randomSeed(@Nullable String randomSeed) {
this.randomSeed = randomSeed;
return this;
}
@CustomType.Setter
public Builder stepLRGamma(@Nullable String stepLRGamma) {
this.stepLRGamma = stepLRGamma;
return this;
}
@CustomType.Setter
public Builder stepLRStepSize(@Nullable String stepLRStepSize) {
this.stepLRStepSize = stepLRStepSize;
return this;
}
@CustomType.Setter
public Builder trainingBatchSize(@Nullable String trainingBatchSize) {
this.trainingBatchSize = trainingBatchSize;
return this;
}
@CustomType.Setter
public Builder trainingCropSize(@Nullable String trainingCropSize) {
this.trainingCropSize = trainingCropSize;
return this;
}
@CustomType.Setter
public Builder validationBatchSize(@Nullable String validationBatchSize) {
this.validationBatchSize = validationBatchSize;
return this;
}
@CustomType.Setter
public Builder validationCropSize(@Nullable String validationCropSize) {
this.validationCropSize = validationCropSize;
return this;
}
@CustomType.Setter
public Builder validationResizeSize(@Nullable String validationResizeSize) {
this.validationResizeSize = validationResizeSize;
return this;
}
@CustomType.Setter
public Builder warmupCosineLRCycles(@Nullable String warmupCosineLRCycles) {
this.warmupCosineLRCycles = warmupCosineLRCycles;
return this;
}
@CustomType.Setter
public Builder warmupCosineLRWarmupEpochs(@Nullable String warmupCosineLRWarmupEpochs) {
this.warmupCosineLRWarmupEpochs = warmupCosineLRWarmupEpochs;
return this;
}
@CustomType.Setter
public Builder weightDecay(@Nullable String weightDecay) {
this.weightDecay = weightDecay;
return this;
}
@CustomType.Setter
public Builder weightedLoss(@Nullable String weightedLoss) {
this.weightedLoss = weightedLoss;
return this;
}
public ImageModelDistributionSettingsClassificationResponse build() {
final var _resultValue = new ImageModelDistributionSettingsClassificationResponse();
_resultValue.amsGradient = amsGradient;
_resultValue.augmentations = augmentations;
_resultValue.beta1 = beta1;
_resultValue.beta2 = beta2;
_resultValue.distributed = distributed;
_resultValue.earlyStopping = earlyStopping;
_resultValue.earlyStoppingDelay = earlyStoppingDelay;
_resultValue.earlyStoppingPatience = earlyStoppingPatience;
_resultValue.enableOnnxNormalization = enableOnnxNormalization;
_resultValue.evaluationFrequency = evaluationFrequency;
_resultValue.gradientAccumulationStep = gradientAccumulationStep;
_resultValue.layersToFreeze = layersToFreeze;
_resultValue.learningRate = learningRate;
_resultValue.learningRateScheduler = learningRateScheduler;
_resultValue.modelName = modelName;
_resultValue.momentum = momentum;
_resultValue.nesterov = nesterov;
_resultValue.numberOfEpochs = numberOfEpochs;
_resultValue.numberOfWorkers = numberOfWorkers;
_resultValue.optimizer = optimizer;
_resultValue.randomSeed = randomSeed;
_resultValue.stepLRGamma = stepLRGamma;
_resultValue.stepLRStepSize = stepLRStepSize;
_resultValue.trainingBatchSize = trainingBatchSize;
_resultValue.trainingCropSize = trainingCropSize;
_resultValue.validationBatchSize = validationBatchSize;
_resultValue.validationCropSize = validationCropSize;
_resultValue.validationResizeSize = validationResizeSize;
_resultValue.warmupCosineLRCycles = warmupCosineLRCycles;
_resultValue.warmupCosineLRWarmupEpochs = warmupCosineLRWarmupEpochs;
_resultValue.weightDecay = weightDecay;
_resultValue.weightedLoss = weightedLoss;
return _resultValue;
}
}
}
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