private Constant InGeneric <T>(T value, int?[] shape = null, KerasSharp.DataType?dtype = null, string name = null)
{
if (dtype == null)
{
dtype = floatx();
}
CNTKDataType _dtype = In(dtype.Value);
int[] _shape = shape.Select(x => x.Value).ToArray();
Constant c = _constant(value, shape, dtype, name);
if (!Out(c.Shape).IsEqual(_shape))
{
throw new Exception();
}
if (dtype == null || c.DataType == _dtype)
{
return(c);
}
throw new Exception();
}
/// <summary>
/// ニューラルネットワークモデルの入力設定
/// </summary>
/// <param name="width">入力画像の幅</param>
/// <param name="height">入力画像の高さ</param>
/// <param name="channel">入力画像のチャンネル</param>
/// <param name="dataType">データタイプ</param>
/// <param name="device">CPU/GPUの使用設定</param>
/// <returns></returns>
public static Function Input(int width, int height, int channel, CNTK.DataType dataType = DataType.Float, CNTK.DeviceDescriptor device = null, string name = "")
{
if (device == null)
{
// CPU or GPUの設定（GPU優先）
GetDevice();
}
else
{
_device = device;
}
_dataType = dataType;
return(CognitiveCSharpKit.Data.Variable(width, height, channel, dataType, name));
}
public Tensor placeholder(int?[] shape = null, int?ndim = null, DataType?dtype = null, bool sparse = false, string name = null)
{
log(new { shape, ndim, dtype, sparse, name });
// https://github.com/fchollet/keras/blob/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras/backend/cntk_backend.py
if (shape == null)
{
if (ndim != null)
{
shape = new int?[ndim.Value];
}
}
var cntk_shape = shape.Select(s => s == null ? NDShape.FreeDimension : s.Value);
//if (dynamic_axis_num > len(cntk_shape)
//{
// raise ValueError('CNTK backend: creating placeholder with '
// '%d dimension is not supported, at least '
// '%d dimensions are needed.'
// % (len(cntk_shape, dynamic_axis_num)))
//}
if (dtype == null)
{
dtype = floatx();
}
if (name is null)
{
name = String.Empty;
}
// cntk_shape = cntk_shape[dynamic_axis_num:]
NDShape _shape = NDShape.CreateNDShape(cntk_shape);
CNTKDataType _dtype = In(dtype.Value);
Variable v = Variable.InputVariable(shape: _shape, dataType: _dtype, isSparse: sparse, name: name);
var x = Out(v);
x._keras_shape = shape;
x._uses_learning_phase = false;
return(x);
}
public Constant InGeneric <T>(T value, int[] shape = null, KerasSharp.DataType?dtype = null, string name = null)
{
if (dtype == null)
{
dtype = floatx();
}
CNTKDataType _dtype = In(dtype.Value);
int[] _shape;
if (shape == null)
{
if (value is Array)
{
_shape = (value as Array).GetLength();
}
else
{
_shape = new int[] { };
}
}
else
{
_shape = shape;
}
Constant c = _constant(value, _shape, _dtype, name);
if (!Out(c.Shape).IsEqual(_shape))
{
throw new Exception();
}
if (dtype == null || c.DataType == _dtype)
{
return(c);
}
throw new Exception();
}
public Constant _constant <T>(T value, int[] shape, CNTKDataType dtype, string name)
{
if (value is Array)
{
NDArrayView x = In(value as Array, Out(dtype));
if (name != null)
{
return(new Constant(x, name));
}
else
{
return(new Constant(x));
}
}
if (shape == null || shape.Length == 0)
{
if (dtype == CNTKDataType.Double)
{
return(Constant.Scalar <double>(value.To <double>(), device: DeviceDescriptor.CPUDevice));
}
if (dtype == CNTKDataType.Float)
{
return(Constant.Scalar <float>(value.To <float>(), device: DeviceDescriptor.CPUDevice));
}
}
if (name == null)
{
return(new Constant(shape: InShape(shape), dataType: dtype,
initValue: (dynamic)value, device: DeviceDescriptor.CPUDevice));
}
return(new Constant(shape: InShape(shape), dataType: dtype,
initValue: (dynamic)value, device: DeviceDescriptor.CPUDevice, name: name));
}
public static Variable Variable(Variable var, CNTK.DataType dataType = DataType.Float)
{
return(CNTKLib.InputVariable(var.Shape, dataType));
}
public static Variable Variable(int width, int height, int channel, CNTK.DataType dataType = DataType.Float, string name = "")
{
return(CNTKLib.InputVariable(new int[] { width, height, channel }, dataType, name));
}
/// <summary>
/// 入力データ
/// </summary>
/// <param name="dataCount"></param>
/// <returns></returns>
public static Variable Variable(int dataLength, CNTK.DataType dataType = DataType.Float)
{
return(CNTKLib.InputVariable(new int[] { dataLength }, dataType));
}
/// <summary>
/// データタイプの設定
/// </summary>
/// <param name="dataType"></param>
public static void SetDataType(CNTK.DataType dataType)
{
_dataType = dataType;
}
public static Variable LabelData(this Function function, CNTK.DataType dataType = DataType.Float)
{
return(CNTKLib.InputVariable(function.Output.Shape, dataType));
}