ImageClassificationTrainer.Options Class

Definition

Namespace:

Microsoft.ML.Vision

Assembly:

Microsoft.ML.Vision.dll

Package:

Microsoft.ML.Vision v3.0.1

Options class for ImageClassificationTrainer.

public sealed class ImageClassificationTrainer.Options : Microsoft.ML.Trainers.TrainerInputBaseWithLabel

type ImageClassificationTrainer.Options = class
    inherit TrainerInputBaseWithLabel

Public NotInheritable Class ImageClassificationTrainer.Options
Inherits TrainerInputBaseWithLabel

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

ImageClassificationTrainer.Options

Constructors

ImageClassificationTrainer.Options()

Fields

Arch	Specifies the model architecture to be used in the case of image classification training using transfer learning. The default Architecture is Resnet_v2_50.
BatchSize	Number of samples to use for mini-batch training. The default value for BatchSize is 10.
EarlyStoppingCriteria	Early stopping technique parameters to be used to terminate training when training metric stops improving. By default EarlyStopping is turned on and the monitoring metric is Accuracy.
Epoch	Number of training iterations. The default value for Epoch is 200.
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
FinalModelPrefix	Final model and checkpoint files/folder prefix for storing graph files. The default prefix is "custom_retrained_model_based_on_".
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
LearningRate	Learning rate to use during optimization. The default value for Learning Rate is 0.01.
LearningRateScheduler	A class that performs learning rate scheduling. The default learning rate scheduler is exponential learning rate decay.
MetricsCallback	Callback to report statistics on accuracy/cross entropy during training phase. Metrics Callback is set to null by default.
PredictedLabelColumnName	Name of the tensor that will contain the predicted label from output scores of the last layer when transfer learning is done. The default tensor name is "PredictedLabel".
ReuseTrainSetBottleneckCachedValues	Indicates to not re-compute cached bottleneck trainset values if already available in the bin folder. This parameter is set to false by default.
ReuseValidationSetBottleneckCachedValues	Indicates to not re-compute cached bottleneck validationset values if already available in the bin folder. This parameter is set to false by default.
ScoreColumnName	Name of the tensor that will contain the output scores of the last layer when transfer learning is done. The default tensor name is "Score".
TestOnTrainSet	Indicates to evaluate the model on train set after every epoch. Test on trainset is set to true by default.
TrainSetBottleneckCachedValuesFileName	Indicates the file name within the workspace to store trainset bottleneck values for caching, default file name is "trainSetBottleneckFile.csv".
ValidationSet	Validation set.
ValidationSetBottleneckCachedValuesFileName	Indicates the file name within the workspace to store validationset bottleneck values for caching, default file name is "validationSetBottleneckFile.csv".
ValidationSetFraction	When validation set is not passed then a fraction of train set is used as validation. To disable this behavior set ValidationSetFraction to null. Accepts value between 0 and 1.0, default value is 0.1 or 10% of the trainset.
WorkspacePath	Indicates the path where the image bottleneck cache files and trained model are saved, default is a new temporary directory.