internal static OvaModelParameters Create(IHost host, TScalarPredictor[] predictors)
{
Contracts.CheckValue(host, nameof(host));
host.CheckNonEmpty(predictors, nameof(predictors));
return(Create(host, OutputFormula.ProbabilityNormalization, predictors));
}
private TensorFlowTransform(IHostEnvironment env, TFSession session, string[] inputs, string[] outputs)
{
Contracts.CheckValue(env, nameof(env));
_host = env.Register(nameof(RegistrationName));
_host.CheckValue(session, nameof(session));
_host.CheckNonEmpty(inputs, nameof(inputs));
_host.CheckNonEmpty(outputs, nameof(outputs));
Session = session;
foreach (var input in inputs)
{
_host.CheckNonWhiteSpace(input, nameof(inputs));
if (Session.Graph[input] == null)
{
throw _host.ExceptParam(nameof(inputs), $"Input column '{input}' does not exist in the model");
}
var tfInput = new TFOutput(Session.Graph[input]);
if (!TensorFlowUtils.IsTypeSupported(tfInput.OutputType))
{
throw _host.ExceptParam(nameof(session), $"Input type '{tfInput.OutputType}' of input column '{input}' is not supported in TensorFlow");
}
}
var newNames = new HashSet <string>();
foreach (var output in outputs)
{
_host.CheckNonWhiteSpace(output, nameof(outputs));
if (!newNames.Add(output))
{
throw _host.ExceptParam(nameof(outputs), $"Output column '{output}' specified multiple times");
}
if (Session.Graph[output] == null)
{
throw _host.ExceptParam(nameof(outputs), $"Output column '{output}' does not exist in the model");
}
}
Inputs = inputs;
TFInputTypes = new TFDataType[Inputs.Length];
TFInputShapes = new TFShape[Inputs.Length];
for (int i = 0; i < Inputs.Length; i++)
{
var tfInput = new TFOutput(Graph[Inputs[i]]);
TFInputTypes[i] = tfInput.OutputType;
TFInputShapes[i] = Graph.GetTensorShape(tfInput);
if (TFInputShapes[i].NumDimensions != -1)
{
var newShape = new long[TFInputShapes[i].NumDimensions];
newShape[0] = TFInputShapes[i][0] == -1 ? BatchSize : TFInputShapes[i][0];
for (int j = 1; j < TFInputShapes[i].NumDimensions; j++)
{
newShape[j] = TFInputShapes[i][j];
}
TFInputShapes[i] = new TFShape(newShape);
}
}
Outputs = outputs;
OutputTypes = new ColumnType[Outputs.Length];
TFOutputTypes = new TFDataType[Outputs.Length];
for (int i = 0; i < Outputs.Length; i++)
{
var tfOutput = new TFOutput(Graph[Outputs[i]]);
var shape = Graph.GetTensorShape(tfOutput);
int[] dims = shape.NumDimensions > 0 ? shape.ToIntArray().Skip(shape[0] == -1 ? BatchSize : 0).ToArray() : new[] { 0 };
var type = TensorFlowUtils.Tf2MlNetType(tfOutput.OutputType);
OutputTypes[i] = new VectorType(type, dims);
TFOutputTypes[i] = tfOutput.OutputType;
}
}
/// <summary>
/// Create a OVA predictor from an array of predictors.
/// </summary>
public static OvaPredictor Create(IHost host, TScalarPredictor[] predictors)
{
Contracts.CheckValue(host, nameof(host));
host.CheckNonEmpty(predictors, nameof(predictors));
return(Create(host, true, predictors));
}
public void PrintOverallResults(IChannel ch, string filename, params Dictionary <string, IDataView>[] metrics)
{
Host.CheckValue(ch, nameof(ch));
Host.CheckNonEmpty(metrics, nameof(metrics));
PrintOverallResultsCore(ch, filename, metrics);
}
