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A native Pulumi package for creating and managing Azure resources.
// *** 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.inputs;
import com.pulumi.core.Output;
import com.pulumi.core.annotations.Import;
import java.lang.String;
import java.util.Objects;
import java.util.Optional;
import javax.annotation.Nullable;
/**
* Distribution expressions to sweep over values of model settings.
* <example>
* Some examples are:
*
*/
public final class ImageModelDistributionSettingsClassificationArgs extends com.pulumi.resources.ResourceArgs {
public static final ImageModelDistributionSettingsClassificationArgs Empty = new ImageModelDistributionSettingsClassificationArgs();
/**
* Enable AMSGrad when optimizer is 'adam' or 'adamw'.
*
*/
@Import(name="amsGradient")
private @Nullable Output amsGradient;
/**
* @return Enable AMSGrad when optimizer is 'adam' or 'adamw'.
*
*/
public Optional> amsGradient() {
return Optional.ofNullable(this.amsGradient);
}
/**
* Settings for using Augmentations.
*
*/
@Import(name="augmentations")
private @Nullable Output augmentations;
/**
* @return Settings for using Augmentations.
*
*/
public Optional> augmentations() {
return Optional.ofNullable(this.augmentations);
}
/**
* Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
*/
@Import(name="beta1")
private @Nullable Output beta1;
/**
* @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);
}
/**
* Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
*/
@Import(name="beta2")
private @Nullable Output beta2;
/**
* @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);
}
/**
* Whether to use distributer training.
*
*/
@Import(name="distributed")
private @Nullable Output distributed;
/**
* @return Whether to use distributer training.
*
*/
public Optional> distributed() {
return Optional.ofNullable(this.distributed);
}
/**
* Enable early stopping logic during training.
*
*/
@Import(name="earlyStopping")
private @Nullable Output earlyStopping;
/**
* @return Enable early stopping logic during training.
*
*/
public Optional> earlyStopping() {
return Optional.ofNullable(this.earlyStopping);
}
/**
* Minimum number of epochs or validation evaluations to wait before primary metric improvement
* is tracked for early stopping. Must be a positive integer.
*
*/
@Import(name="earlyStoppingDelay")
private @Nullable Output earlyStoppingDelay;
/**
* @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);
}
/**
* Minimum number of epochs or validation evaluations with no primary metric improvement before
* the run is stopped. Must be a positive integer.
*
*/
@Import(name="earlyStoppingPatience")
private @Nullable Output earlyStoppingPatience;
/**
* @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);
}
/**
* Enable normalization when exporting ONNX model.
*
*/
@Import(name="enableOnnxNormalization")
private @Nullable Output enableOnnxNormalization;
/**
* @return Enable normalization when exporting ONNX model.
*
*/
public Optional> enableOnnxNormalization() {
return Optional.ofNullable(this.enableOnnxNormalization);
}
/**
* Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
*
*/
@Import(name="evaluationFrequency")
private @Nullable Output evaluationFrequency;
/**
* @return Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
*
*/
public Optional> evaluationFrequency() {
return Optional.ofNullable(this.evaluationFrequency);
}
/**
* 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.
*
*/
@Import(name="gradientAccumulationStep")
private @Nullable Output gradientAccumulationStep;
/**
* @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);
}
/**
* 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.
*
*/
@Import(name="layersToFreeze")
private @Nullable Output layersToFreeze;
/**
* @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);
}
/**
* Initial learning rate. Must be a float in the range [0, 1].
*
*/
@Import(name="learningRate")
private @Nullable Output learningRate;
/**
* @return Initial learning rate. Must be a float in the range [0, 1].
*
*/
public Optional> learningRate() {
return Optional.ofNullable(this.learningRate);
}
/**
* Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
*
*/
@Import(name="learningRateScheduler")
private @Nullable Output learningRateScheduler;
/**
* @return Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
*
*/
public Optional> learningRateScheduler() {
return Optional.ofNullable(this.learningRateScheduler);
}
/**
* 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.
*
*/
@Import(name="modelName")
private @Nullable Output modelName;
/**
* @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);
}
/**
* Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
*
*/
@Import(name="momentum")
private @Nullable Output momentum;
/**
* @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);
}
/**
* Enable nesterov when optimizer is 'sgd'.
*
*/
@Import(name="nesterov")
private @Nullable Output nesterov;
/**
* @return Enable nesterov when optimizer is 'sgd'.
*
*/
public Optional> nesterov() {
return Optional.ofNullable(this.nesterov);
}
/**
* Number of training epochs. Must be a positive integer.
*
*/
@Import(name="numberOfEpochs")
private @Nullable Output numberOfEpochs;
/**
* @return Number of training epochs. Must be a positive integer.
*
*/
public Optional> numberOfEpochs() {
return Optional.ofNullable(this.numberOfEpochs);
}
/**
* Number of data loader workers. Must be a non-negative integer.
*
*/
@Import(name="numberOfWorkers")
private @Nullable Output numberOfWorkers;
/**
* @return Number of data loader workers. Must be a non-negative integer.
*
*/
public Optional> numberOfWorkers() {
return Optional.ofNullable(this.numberOfWorkers);
}
/**
* Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
*
*/
@Import(name="optimizer")
private @Nullable Output optimizer;
/**
* @return Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
*
*/
public Optional> optimizer() {
return Optional.ofNullable(this.optimizer);
}
/**
* Random seed to be used when using deterministic training.
*
*/
@Import(name="randomSeed")
private @Nullable Output randomSeed;
/**
* @return Random seed to be used when using deterministic training.
*
*/
public Optional> randomSeed() {
return Optional.ofNullable(this.randomSeed);
}
/**
* Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
*
*/
@Import(name="stepLRGamma")
private @Nullable Output stepLRGamma;
/**
* @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);
}
/**
* Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
*
*/
@Import(name="stepLRStepSize")
private @Nullable Output stepLRStepSize;
/**
* @return Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
*
*/
public Optional> stepLRStepSize() {
return Optional.ofNullable(this.stepLRStepSize);
}
/**
* Training batch size. Must be a positive integer.
*
*/
@Import(name="trainingBatchSize")
private @Nullable Output trainingBatchSize;
/**
* @return Training batch size. Must be a positive integer.
*
*/
public Optional> trainingBatchSize() {
return Optional.ofNullable(this.trainingBatchSize);
}
/**
* Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
*
*/
@Import(name="trainingCropSize")
private @Nullable Output trainingCropSize;
/**
* @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);
}
/**
* Validation batch size. Must be a positive integer.
*
*/
@Import(name="validationBatchSize")
private @Nullable Output validationBatchSize;
/**
* @return Validation batch size. Must be a positive integer.
*
*/
public Optional> validationBatchSize() {
return Optional.ofNullable(this.validationBatchSize);
}
/**
* Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
*
*/
@Import(name="validationCropSize")
private @Nullable Output validationCropSize;
/**
* @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);
}
/**
* Image size to which to resize before cropping for validation dataset. Must be a positive integer.
*
*/
@Import(name="validationResizeSize")
private @Nullable Output validationResizeSize;
/**
* @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);
}
/**
* Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
*
*/
@Import(name="warmupCosineLRCycles")
private @Nullable Output warmupCosineLRCycles;
/**
* @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);
}
/**
* Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
*
*/
@Import(name="warmupCosineLRWarmupEpochs")
private @Nullable Output warmupCosineLRWarmupEpochs;
/**
* @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);
}
/**
* Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
*
*/
@Import(name="weightDecay")
private @Nullable Output weightDecay;
/**
* @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);
}
/**
* 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.
*
*/
@Import(name="weightedLoss")
private @Nullable Output weightedLoss;
/**
* @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);
}
private ImageModelDistributionSettingsClassificationArgs() {}
private ImageModelDistributionSettingsClassificationArgs(ImageModelDistributionSettingsClassificationArgs $) {
this.amsGradient = $.amsGradient;
this.augmentations = $.augmentations;
this.beta1 = $.beta1;
this.beta2 = $.beta2;
this.distributed = $.distributed;
this.earlyStopping = $.earlyStopping;
this.earlyStoppingDelay = $.earlyStoppingDelay;
this.earlyStoppingPatience = $.earlyStoppingPatience;
this.enableOnnxNormalization = $.enableOnnxNormalization;
this.evaluationFrequency = $.evaluationFrequency;
this.gradientAccumulationStep = $.gradientAccumulationStep;
this.layersToFreeze = $.layersToFreeze;
this.learningRate = $.learningRate;
this.learningRateScheduler = $.learningRateScheduler;
this.modelName = $.modelName;
this.momentum = $.momentum;
this.nesterov = $.nesterov;
this.numberOfEpochs = $.numberOfEpochs;
this.numberOfWorkers = $.numberOfWorkers;
this.optimizer = $.optimizer;
this.randomSeed = $.randomSeed;
this.stepLRGamma = $.stepLRGamma;
this.stepLRStepSize = $.stepLRStepSize;
this.trainingBatchSize = $.trainingBatchSize;
this.trainingCropSize = $.trainingCropSize;
this.validationBatchSize = $.validationBatchSize;
this.validationCropSize = $.validationCropSize;
this.validationResizeSize = $.validationResizeSize;
this.warmupCosineLRCycles = $.warmupCosineLRCycles;
this.warmupCosineLRWarmupEpochs = $.warmupCosineLRWarmupEpochs;
this.weightDecay = $.weightDecay;
this.weightedLoss = $.weightedLoss;
}
public static Builder builder() {
return new Builder();
}
public static Builder builder(ImageModelDistributionSettingsClassificationArgs defaults) {
return new Builder(defaults);
}
public static final class Builder {
private ImageModelDistributionSettingsClassificationArgs $;
public Builder() {
$ = new ImageModelDistributionSettingsClassificationArgs();
}
public Builder(ImageModelDistributionSettingsClassificationArgs defaults) {
$ = new ImageModelDistributionSettingsClassificationArgs(Objects.requireNonNull(defaults));
}
/**
* @param amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'.
*
* @return builder
*
*/
public Builder amsGradient(@Nullable Output amsGradient) {
$.amsGradient = amsGradient;
return this;
}
/**
* @param amsGradient Enable AMSGrad when optimizer is 'adam' or 'adamw'.
*
* @return builder
*
*/
public Builder amsGradient(String amsGradient) {
return amsGradient(Output.of(amsGradient));
}
/**
* @param augmentations Settings for using Augmentations.
*
* @return builder
*
*/
public Builder augmentations(@Nullable Output augmentations) {
$.augmentations = augmentations;
return this;
}
/**
* @param augmentations Settings for using Augmentations.
*
* @return builder
*
*/
public Builder augmentations(String augmentations) {
return augmentations(Output.of(augmentations));
}
/**
* @param beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder beta1(@Nullable Output beta1) {
$.beta1 = beta1;
return this;
}
/**
* @param beta1 Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder beta1(String beta1) {
return beta1(Output.of(beta1));
}
/**
* @param beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder beta2(@Nullable Output beta2) {
$.beta2 = beta2;
return this;
}
/**
* @param beta2 Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder beta2(String beta2) {
return beta2(Output.of(beta2));
}
/**
* @param distributed Whether to use distributer training.
*
* @return builder
*
*/
public Builder distributed(@Nullable Output distributed) {
$.distributed = distributed;
return this;
}
/**
* @param distributed Whether to use distributer training.
*
* @return builder
*
*/
public Builder distributed(String distributed) {
return distributed(Output.of(distributed));
}
/**
* @param earlyStopping Enable early stopping logic during training.
*
* @return builder
*
*/
public Builder earlyStopping(@Nullable Output earlyStopping) {
$.earlyStopping = earlyStopping;
return this;
}
/**
* @param earlyStopping Enable early stopping logic during training.
*
* @return builder
*
*/
public Builder earlyStopping(String earlyStopping) {
return earlyStopping(Output.of(earlyStopping));
}
/**
* @param earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
* is tracked for early stopping. Must be a positive integer.
*
* @return builder
*
*/
public Builder earlyStoppingDelay(@Nullable Output earlyStoppingDelay) {
$.earlyStoppingDelay = earlyStoppingDelay;
return this;
}
/**
* @param earlyStoppingDelay Minimum number of epochs or validation evaluations to wait before primary metric improvement
* is tracked for early stopping. Must be a positive integer.
*
* @return builder
*
*/
public Builder earlyStoppingDelay(String earlyStoppingDelay) {
return earlyStoppingDelay(Output.of(earlyStoppingDelay));
}
/**
* @param earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
* the run is stopped. Must be a positive integer.
*
* @return builder
*
*/
public Builder earlyStoppingPatience(@Nullable Output earlyStoppingPatience) {
$.earlyStoppingPatience = earlyStoppingPatience;
return this;
}
/**
* @param earlyStoppingPatience Minimum number of epochs or validation evaluations with no primary metric improvement before
* the run is stopped. Must be a positive integer.
*
* @return builder
*
*/
public Builder earlyStoppingPatience(String earlyStoppingPatience) {
return earlyStoppingPatience(Output.of(earlyStoppingPatience));
}
/**
* @param enableOnnxNormalization Enable normalization when exporting ONNX model.
*
* @return builder
*
*/
public Builder enableOnnxNormalization(@Nullable Output enableOnnxNormalization) {
$.enableOnnxNormalization = enableOnnxNormalization;
return this;
}
/**
* @param enableOnnxNormalization Enable normalization when exporting ONNX model.
*
* @return builder
*
*/
public Builder enableOnnxNormalization(String enableOnnxNormalization) {
return enableOnnxNormalization(Output.of(enableOnnxNormalization));
}
/**
* @param evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
*
* @return builder
*
*/
public Builder evaluationFrequency(@Nullable Output evaluationFrequency) {
$.evaluationFrequency = evaluationFrequency;
return this;
}
/**
* @param evaluationFrequency Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
*
* @return builder
*
*/
public Builder evaluationFrequency(String evaluationFrequency) {
return evaluationFrequency(Output.of(evaluationFrequency));
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder gradientAccumulationStep(@Nullable Output gradientAccumulationStep) {
$.gradientAccumulationStep = gradientAccumulationStep;
return this;
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder gradientAccumulationStep(String gradientAccumulationStep) {
return gradientAccumulationStep(Output.of(gradientAccumulationStep));
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder layersToFreeze(@Nullable Output layersToFreeze) {
$.layersToFreeze = layersToFreeze;
return this;
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder layersToFreeze(String layersToFreeze) {
return layersToFreeze(Output.of(layersToFreeze));
}
/**
* @param learningRate Initial learning rate. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder learningRate(@Nullable Output learningRate) {
$.learningRate = learningRate;
return this;
}
/**
* @param learningRate Initial learning rate. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder learningRate(String learningRate) {
return learningRate(Output.of(learningRate));
}
/**
* @param learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
*
* @return builder
*
*/
public Builder learningRateScheduler(@Nullable Output learningRateScheduler) {
$.learningRateScheduler = learningRateScheduler;
return this;
}
/**
* @param learningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
*
* @return builder
*
*/
public Builder learningRateScheduler(String learningRateScheduler) {
return learningRateScheduler(Output.of(learningRateScheduler));
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder modelName(@Nullable Output modelName) {
$.modelName = modelName;
return this;
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder modelName(String modelName) {
return modelName(Output.of(modelName));
}
/**
* @param momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder momentum(@Nullable Output momentum) {
$.momentum = momentum;
return this;
}
/**
* @param momentum Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder momentum(String momentum) {
return momentum(Output.of(momentum));
}
/**
* @param nesterov Enable nesterov when optimizer is 'sgd'.
*
* @return builder
*
*/
public Builder nesterov(@Nullable Output nesterov) {
$.nesterov = nesterov;
return this;
}
/**
* @param nesterov Enable nesterov when optimizer is 'sgd'.
*
* @return builder
*
*/
public Builder nesterov(String nesterov) {
return nesterov(Output.of(nesterov));
}
/**
* @param numberOfEpochs Number of training epochs. Must be a positive integer.
*
* @return builder
*
*/
public Builder numberOfEpochs(@Nullable Output numberOfEpochs) {
$.numberOfEpochs = numberOfEpochs;
return this;
}
/**
* @param numberOfEpochs Number of training epochs. Must be a positive integer.
*
* @return builder
*
*/
public Builder numberOfEpochs(String numberOfEpochs) {
return numberOfEpochs(Output.of(numberOfEpochs));
}
/**
* @param numberOfWorkers Number of data loader workers. Must be a non-negative integer.
*
* @return builder
*
*/
public Builder numberOfWorkers(@Nullable Output numberOfWorkers) {
$.numberOfWorkers = numberOfWorkers;
return this;
}
/**
* @param numberOfWorkers Number of data loader workers. Must be a non-negative integer.
*
* @return builder
*
*/
public Builder numberOfWorkers(String numberOfWorkers) {
return numberOfWorkers(Output.of(numberOfWorkers));
}
/**
* @param optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
*
* @return builder
*
*/
public Builder optimizer(@Nullable Output optimizer) {
$.optimizer = optimizer;
return this;
}
/**
* @param optimizer Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
*
* @return builder
*
*/
public Builder optimizer(String optimizer) {
return optimizer(Output.of(optimizer));
}
/**
* @param randomSeed Random seed to be used when using deterministic training.
*
* @return builder
*
*/
public Builder randomSeed(@Nullable Output randomSeed) {
$.randomSeed = randomSeed;
return this;
}
/**
* @param randomSeed Random seed to be used when using deterministic training.
*
* @return builder
*
*/
public Builder randomSeed(String randomSeed) {
return randomSeed(Output.of(randomSeed));
}
/**
* @param stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder stepLRGamma(@Nullable Output stepLRGamma) {
$.stepLRGamma = stepLRGamma;
return this;
}
/**
* @param stepLRGamma Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder stepLRGamma(String stepLRGamma) {
return stepLRGamma(Output.of(stepLRGamma));
}
/**
* @param stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
*
* @return builder
*
*/
public Builder stepLRStepSize(@Nullable Output stepLRStepSize) {
$.stepLRStepSize = stepLRStepSize;
return this;
}
/**
* @param stepLRStepSize Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
*
* @return builder
*
*/
public Builder stepLRStepSize(String stepLRStepSize) {
return stepLRStepSize(Output.of(stepLRStepSize));
}
/**
* @param trainingBatchSize Training batch size. Must be a positive integer.
*
* @return builder
*
*/
public Builder trainingBatchSize(@Nullable Output trainingBatchSize) {
$.trainingBatchSize = trainingBatchSize;
return this;
}
/**
* @param trainingBatchSize Training batch size. Must be a positive integer.
*
* @return builder
*
*/
public Builder trainingBatchSize(String trainingBatchSize) {
return trainingBatchSize(Output.of(trainingBatchSize));
}
/**
* @param trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
*
* @return builder
*
*/
public Builder trainingCropSize(@Nullable Output trainingCropSize) {
$.trainingCropSize = trainingCropSize;
return this;
}
/**
* @param trainingCropSize Image crop size that is input to the neural network for the training dataset. Must be a positive integer.
*
* @return builder
*
*/
public Builder trainingCropSize(String trainingCropSize) {
return trainingCropSize(Output.of(trainingCropSize));
}
/**
* @param validationBatchSize Validation batch size. Must be a positive integer.
*
* @return builder
*
*/
public Builder validationBatchSize(@Nullable Output validationBatchSize) {
$.validationBatchSize = validationBatchSize;
return this;
}
/**
* @param validationBatchSize Validation batch size. Must be a positive integer.
*
* @return builder
*
*/
public Builder validationBatchSize(String validationBatchSize) {
return validationBatchSize(Output.of(validationBatchSize));
}
/**
* @param validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
*
* @return builder
*
*/
public Builder validationCropSize(@Nullable Output validationCropSize) {
$.validationCropSize = validationCropSize;
return this;
}
/**
* @param validationCropSize Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.
*
* @return builder
*
*/
public Builder validationCropSize(String validationCropSize) {
return validationCropSize(Output.of(validationCropSize));
}
/**
* @param validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer.
*
* @return builder
*
*/
public Builder validationResizeSize(@Nullable Output validationResizeSize) {
$.validationResizeSize = validationResizeSize;
return this;
}
/**
* @param validationResizeSize Image size to which to resize before cropping for validation dataset. Must be a positive integer.
*
* @return builder
*
*/
public Builder validationResizeSize(String validationResizeSize) {
return validationResizeSize(Output.of(validationResizeSize));
}
/**
* @param warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder warmupCosineLRCycles(@Nullable Output warmupCosineLRCycles) {
$.warmupCosineLRCycles = warmupCosineLRCycles;
return this;
}
/**
* @param warmupCosineLRCycles Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
*
* @return builder
*
*/
public Builder warmupCosineLRCycles(String warmupCosineLRCycles) {
return warmupCosineLRCycles(Output.of(warmupCosineLRCycles));
}
/**
* @param warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
*
* @return builder
*
*/
public Builder warmupCosineLRWarmupEpochs(@Nullable Output warmupCosineLRWarmupEpochs) {
$.warmupCosineLRWarmupEpochs = warmupCosineLRWarmupEpochs;
return this;
}
/**
* @param warmupCosineLRWarmupEpochs Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
*
* @return builder
*
*/
public Builder warmupCosineLRWarmupEpochs(String warmupCosineLRWarmupEpochs) {
return warmupCosineLRWarmupEpochs(Output.of(warmupCosineLRWarmupEpochs));
}
/**
* @param weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
*
* @return builder
*
*/
public Builder weightDecay(@Nullable Output weightDecay) {
$.weightDecay = weightDecay;
return this;
}
/**
* @param weightDecay Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
*
* @return builder
*
*/
public Builder weightDecay(String weightDecay) {
return weightDecay(Output.of(weightDecay));
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder weightedLoss(@Nullable Output weightedLoss) {
$.weightedLoss = weightedLoss;
return this;
}
/**
* @param 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.
*
* @return builder
*
*/
public Builder weightedLoss(String weightedLoss) {
return weightedLoss(Output.of(weightedLoss));
}
public ImageModelDistributionSettingsClassificationArgs build() {
return $;
}
}
}
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