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// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using Microsoft.ML.Data;
using Microsoft.ML.Runtime;
using Microsoft.ML.Transforms;
using Microsoft.ML.Vision;
using static Microsoft.ML.TensorFlow.TensorFlowUtils;
namespace Microsoft.ML
{
/// <summary>
/// Collection of extension methods for <see cref="T:Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers" /> to create instances of ImageClassification trainer components.
/// </summary>
/// <remarks>
/// This requires additional nuget dependencies to link against Tensorflow native dlls. See <see cref="T:Microsoft.ML.Vision.ImageClassificationTrainer"/> for more information.
/// </remarks>
public static class VisionCatalog
{
/// <summary>
/// Retrain the dnn model on new data.
/// usage of this API requires additional NuGet dependencies on TensorFlow redist, see linked document for more information.
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </summary>
/// <param name="catalog"></param>
/// <param name="inputColumnNames"> The names of the model inputs.</param>
/// <param name="outputColumnNames">The names of the requested model outputs.</param>
/// <param name="labelColumnName">Name of the label column.</param>
/// <param name="dnnLabel">Name of the node in DNN graph that is used as label during training in Dnn.
/// The value of <paramref name="labelColumnName"/> from <see cref="IDataView"/> is fed to this node.</param>
/// <param name="optimizationOperation">The name of the optimization operation in the Dnn graph.</param>
/// <param name="modelPath">Path to model file to retrain.</param>
/// <param name="epoch">Number of training iterations.</param>
/// <param name="batchSize">Number of samples to use for mini-batch training.</param>
/// <param name="lossOperation">The name of the operation in the Dnn graph to compute training loss (Optional).</param>
/// <param name="metricOperation">The name of the operation in the Dnn graph to compute performance metric during training (Optional).</param>
/// <param name="learningRateOperation">The name of the operation in the Dnn graph which sets optimizer learning rate (Optional).</param>
/// <param name="learningRate">Learning rate to use during optimization (Optional).</param>
/// <param name="addBatchDimensionInput">Add a batch dimension to the input e.g. input = [224, 224, 3] => [-1, 224, 224, 3].
/// This parameter is used to deal with models that have unknown shape but the internal operators in the model require data to have batch dimension as well.</param>
/// <remarks>
/// The support for retraining is under preview.
/// </remarks>
internal static DnnRetrainEstimator RetrainDnnModel(
this ModelOperationsCatalog catalog,
string[] outputColumnNames,
string[] inputColumnNames,
string labelColumnName,
string dnnLabel,
string optimizationOperation,
string modelPath,
int epoch = 10,
int batchSize = 20,
string lossOperation = null,
string metricOperation = null,
string learningRateOperation = null,
float learningRate = 0.01f,
bool addBatchDimensionInput = false)
{
var options = new DnnRetrainEstimator.Options()
{
ModelLocation = modelPath,
InputColumns = inputColumnNames,
OutputColumns = outputColumnNames,
LabelColumn = labelColumnName,
TensorFlowLabel = dnnLabel,
OptimizationOperation = optimizationOperation,
LossOperation = lossOperation,
MetricOperation = metricOperation,
Epoch = epoch,
LearningRateOperation = learningRateOperation,
LearningRate = learningRate,
BatchSize = batchSize,
AddBatchDimensionInputs = addBatchDimensionInput
};
var env = CatalogUtils.GetEnvironment(catalog);
return new DnnRetrainEstimator(env, options, LoadDnnModel(env, modelPath, true));
}
/// <summary>
/// Create <see cref="Microsoft.ML.Vision.ImageClassificationTrainer"/> using advanced options, which trains a Deep Neural Network(DNN) to classify images.
/// </summary>
/// <param name="catalog">Catalog</param>
/// <param name="options">An <see cref="ImageClassificationTrainer.Options"/> object specifying advanced
/// options for <see cref="ImageClassificationTrainer"/>.</param>
/// <example>
/// <format type="text/markdown">
/// <]
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/// </example>
public static ImageClassificationTrainer ImageClassification(
this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
ImageClassificationTrainer.Options options) =>
new ImageClassificationTrainer(CatalogUtils.GetEnvironment(catalog), options);
/// <summary>
/// Create <see cref="Microsoft.ML.Vision.ImageClassificationTrainer"/>, which trains a Deep Neural Network(DNN) to classify images.
/// </summary>
/// <param name="catalog">Catalog</param>
/// <param name="labelColumnName">The name of the labels column. The default for this parameter is "label".</param>
/// <param name="featureColumnName">The name of the input features column. The default for this parameter is "Features".</param>
/// <param name="scoreColumnName">The name of the output score column. The default for this parameter is "Score"</param>
/// <param name="predictedLabelColumnName">The name of the output predicted label columns. The default for this parameter is "PredictedLabel"</param>
/// <param name="validationSet">The validation set used while training to improve model quality. The default for this parameter is null.</param>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]></format>
/// </example>
public static ImageClassificationTrainer ImageClassification(
this MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog,
string labelColumnName = DefaultColumnNames.Label,
string featureColumnName = DefaultColumnNames.Features,
string scoreColumnName = DefaultColumnNames.Score,
string predictedLabelColumnName = DefaultColumnNames.PredictedLabel,
IDataView validationSet = null)
{
Contracts.CheckValue(catalog, nameof(catalog));
return new ImageClassificationTrainer(CatalogUtils.GetEnvironment(catalog), labelColumnName,
featureColumnName, scoreColumnName, predictedLabelColumnName, validationSet);
}
}
}
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