public static IDataSource BuildTensors(GraphFactory graph, IDataSource existing, IReadOnlyList <Mnist.Image> images)
{
var dataTable = BrightWireProvider.CreateDataTableBuilder();
dataTable.AddColumn(ColumnType.Tensor, "Image");
dataTable.AddColumn(ColumnType.Vector, "Target", true);
foreach (var image in images)
{
var data = image.AsFloatTensor;
dataTable.Add(data.Tensor, data.Label);
}
if (existing != null)
{
return(existing.CloneWith(dataTable.Build()));
}
else
{
return(graph.CreateDataSource(dataTable.Build()));
}
}
static IDataSource _BuildTensors(GraphFactory graph, IDataSource existing,
IReadOnlyList <Mnist.Image> images)
{
// convolutional neural networks expect a 3D tensor => vector mapping
var dataTable = BrightWireProvider.CreateDataTableBuilder();
dataTable.AddColumn(ColumnType.Tensor, "Image");
dataTable.AddColumn(ColumnType.Vector, "Target", isTarget: true);
foreach (var image in images)
{
var data = image.AsFloatTensor;
dataTable.Add(data.Tensor, data.Label);
}
// reuse the network used for training when building the test data source
if (existing != null)
{
return(existing.CloneWith(dataTable.Build()));
}
return(graph.CreateDataSource(dataTable.Build()));
}