Shulou(Shulou.com)06/01 Report--

This article introduces what are the features of ML.NET 0.10, the content is very detailed, interested friends can refer to, hope to be helpful to you.

IDataView is treated as a separate library package

The IDataView component provides a very efficient way to process tabular data, especially for machine learning and advanced analysis applications. It is designed to handle high-dimensional data and large data sets efficiently. It is also suitable for dealing with a single data block node that belongs to a larger distributed dataset.

In ML.NET 0.10, IDataView is split into a single assembly and a NuGet class library package. This is a very important step for interacting with other API and frameworks.

After it is split, other class libraries will be able to reference it directly without having to reference the entire ML.NET. This helps third-party class libraries also use the powerful capabilities provided by IDataView.

The field-aware decomposer trainer supports multiple feature columns

In previous versions of ML.NET, only a single feature column could be provided when using the field-aware decomposer (FFM) trainer.

In the new version, additional feature columns are supported in the Options parameter.

Var ffmArgs = new FieldAwareFactorizationMachineTrainer.Options (); / / Create the multiple field names.ffmArgs.FeatureColumn = nameof (MyObservationClass.MyField1); / / first field ffmArgs.ExtraFeatureColumns = new [] {nameof (MyObservationClass.MyField2), nameof (MyObservationClass.MyField3)}; / / additional field var pipeline = mlContext.BinaryClassification.Trainers.FieldAwareFactorizationMachine (ffmArgs); var model = pipeline.Fit (dataView); multiple prediction tags are supported

In previous versions, even if multi-category classification problems were predicted, only a single tag could be returned.

Now, this defect has finally been fixed. (in fact, many predictions have been processed in the internal logic, but the past API only returned a single result.)

Sample page from the community

As part of ML.NET Samples, a special page has now been added-- multiple examples provided by the community.

There are many good examples:

Photo query WPF application, which internally runs the TensorFlow model and is exported to ONNX format.

UWP applications using ML.NET:

What about ML.NET 0.10 features to share here, I hope the above content can be of some help to you, can learn more knowledge. If you think the article is good, you can share it for more people to see.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.