/// <summary>
/// Create a constant tensor based on a shape
/// Used by Zeros and Ones
/// </summary>
/// <param name="value">Value for tensor</param>
/// <param name="tfshape">Shape of the tensor</param>
/// <param name="dtype">Optional Type of the Zero value. Default: Double</param>
/// <param name="operName">Operation name, optional.</param>
/// <returns></returns>
/// see https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/python/framework/constant_op.py
public TFOutput Constant(object value, TFShape tfshape, TFDataType dtype = TFDataType.Double, string operName = null)
{
//convert the .net type to relevant tensorflow type
object dtvalue = TFTensor.FetchSimple(dtype, value);
var shape = tfshape.ToArray();
var idx = new int [shape.Length];
for (int i = 0; i < shape.Length; i++)
{
if (shape [i] > Int32.MaxValue)
{
throw new ArgumentOutOfRangeException("Shape can not be longer than 32 bits");
}
}
Array data = null;
if (tfshape.IsLongArray)
{
data = Array.CreateInstance(dtvalue.GetType(), tfshape.ToArray());
}
else
{
data = Array.CreateInstance(dtvalue.GetType(), tfshape.ToIntArray());
}
TFTensor.Set(data, dtype, shape, idx, 0, value);
TFTensor tensor_value = new TFTensor(data);
return(Const(tensor_value, tensor_value.TensorType, operName));
}
//
// Converts a shape to a tensor, to a TFOutput
//
TFOutput ShapeTensorOutput(TFShape shape)
{
if (shape.IsLongArray)
{
return(Const(shape.ToArray(), TFDataType.Int64));
}
else
{
return(Const(shape.ToIntArray(), TFDataType.Int32));
}
}
