ONNX API documentation. (#419)

* ONNX API documentation.

Author: codemzs

src/Microsoft.ML/Models/OnnxConverter.cs

@@ -10,9 +10,54 @@ namespace Microsoft.ML.Models
     public sealed partial class OnnxConverter
     {
         /// <summary>
-        /// Converts the model to ONNX format.
+        /// <see href="https://onnx.ai/">ONNX</see> is an intermediate representation format 
+        /// for machine learning models. It is used to make models portable such that you can 
+        /// train a model using a toolkit and run it in another tookit's runtime, for example,
+        /// you can create a model using ML.NET (or any ONNX compatible toolkit), convert it to ONNX and 
+        /// then the ONNX model can be converted into say, CoreML, TensorFlow or WinML model 
+        /// to run on the respective runtime.
+        /// 
+        /// This API converts an ML.NET model to ONNX format by inspecting the transform pipeline 
+        /// from the end, checking for components that know how to save themselves as ONNX. 
+        /// The first item in the transform pipeline that does not know how to save itself 
+        /// as ONNX, is considered the "input" to the ONNX pipeline. (Ideally this would be the 
+        /// original loader itself, but this may not be possible if the user used unsavable 
+        /// transforms in defining the pipe.) All the columns in the source that are a type the 
+        /// ONNX knows how to deal with will be tracked. Intermediate transformations of the 
+        /// data appearing as new columns will appear in the output block of the ONNX, with names 
+        /// derived from the corresponding column names. The ONNX JSON will be serialized to a 
+        /// path defined through the Json option.
+        ///
+        /// This API supports the following arguments:
+        /// <see cref="Onnx"/> indicates the file to write the ONNX protocol buffer file to. This is optional.
+        /// <see cref="Json"/> indicates the file to write the JSON representation of the ONNX model. This is optional.
+        /// <see cref="Name"/> indicates the name property in the ONNX model. If left unspecified, it will 
+        /// be the extension-less name of the file specified in the onnx indicates the protocol buffer file
+        /// to write the ONNX representation to.
+        /// <see cref="Domain"/> indicates the domain name of the model. ONNX uses reverse domain name space indicators.
+        /// For example com.microsoft.cognitiveservices. This is a required field.
+        /// <see cref="InputsToDrop"/> is a string array of input column names to omit from the input mapping.
+        /// A common scenario might be to drop the label column, for instance, since it may not be practically
+        /// useful for the pipeline. Note that any columns depending on these naturally cannot be saved.
+        /// <see cref="OutputsToDrop"/> is similar, except for the output schema. Note that the pipeline handler 
+        /// is currently not intelligent enough to drop intermediate calculations that produce this value: this will
+        /// merely omit that value from the actual output.
+        /// 
+        /// Transforms that can be exported to ONNX
+        /// 1. Concat
+        /// 2. KeyToVector
+        /// 3. NAReplace
+        /// 4. Normalize
+        /// 5. Term
+        /// 6. Categorical
+        /// 
+        /// Learners that can be exported to ONNX
+        /// 1. FastTree
+        /// 2. LightGBM
+        /// 3. Logistic Regression
+        /// 
         /// See <a href="https://github.com/dotnet/machinelearning/blob/master/test/Microsoft.ML.Tests/OnnxTests.cs"/>
-        /// for an example.
+        /// for an example on how to train a model and then convert that model to ONNX.
         /// </summary>
         /// <param name="model">Model that needs to be converted to ONNX format.</param>
         public void Convert(PredictionModel model)