public Tensor CreateVariable(float[] data, long[] shape, string name = "")
{
var arr = new CNTK.NDArrayView(BackendUtil.CastShapeInt(shape), data, DeviceManager.Current);
var v = new CNTK.Variable(BackendUtil.CastShapeInt(shape), VariableKind.Parameter, CNTK.DataType.Float, arr, false, new AxisVector(), false, name, name);
return(Out(v));
}
private Tensor CreateVariable(int[] data, long[] shape, string name = "")
{
shape = BackendUtil.Row2ColMajor(shape);
var arr = new CNTK.NDArrayView(BackendUtil.CastShapeInt(shape), Array.ConvertAll(data, x => (float)x), DeviceManager.Current);
var v = new CNTK.Variable(BackendUtil.CastShapeInt(shape), VariableKind.Input, CNTK.DataType.Float, arr, false, new AxisVector(), false, name, name);
return(Out(v));
}
public Tensor RandomUniform(long[] shape, float min, float max, int?seed = null)
{
var result = Data.RandUniform <float>(BackendUtil.CastShapeInt(shape));
if (min != 0 || max != 1)
{
result = Data.Constant <float>((max - min), result.Dimensions) * result + Data.Constant <float>(min, result.Dimensions);
}
return(Out(result));
}
public Tensor RandomUniform(long[] shape, float min, float max, int?seed = null)
{
if (seed.HasValue)
{
return(Out(C.UniformRandom(BackendUtil.CastShapeInt(shape), CNTK.DataType.Float, min, max, (uint)seed.Value)));
}
else
{
return(Out(C.UniformRandom(BackendUtil.CastShapeInt(shape), CNTK.DataType.Float, min, max)));
}
}
public Tensor RandomNormal(long[] shape, float mean, float stddev, int?seed = null)
{
if (seed.HasValue)
{
return(Out(C.NormalRandom(BackendUtil.CastShapeInt(shape), CNTK.DataType.Float, mean, stddev, (uint)seed.Value)));
}
else
{
return(Out(C.NormalRandom(BackendUtil.CastShapeInt(shape), CNTK.DataType.Float, mean, stddev)));
}
}
public Tensor RandomNormal(long[] shape, float mean, float stddev, int?seed = null)
{
var result = Data.RandNormal <float>(BackendUtil.CastShapeInt(shape));
if (mean != 0 || stddev != 1)
{
result = Data.Constant <float>(stddev, result.Dimensions) * result + Data.Constant <float>(mean, result.Dimensions);
}
return(Out(result));
}
public Tensor Constant(float value, long[] shape)
{
shape = BackendUtil.Row2ColMajor(shape);
return(Out(Data.Constant <float>(value, BackendUtil.CastShapeInt(shape))));
}
private NDArray In(float value, params long[] shape)
{
NDArrayTensor tensor = new NDArrayTensor(Data.Constant(value, BackendUtil.CastShapeInt(shape)));
return(tensor.InternalTensor);
}
public SiaTensor RandomNormal(long[] shape, float mean, float stddev, int? seed = null)
{
return Out(tf.random_normal(BackendUtil.CastShapeInt(shape), mean, stddev, seed: seed));
}
public SiaTensor RandomUniform(long[] shape, float min, float max, int? seed = null)
{
return Out(tf.random_uniform(BackendUtil.CastShapeInt(shape), min, max, seed: seed));
}
public SiaTensor Reshape(SiaTensor x, params long[] shape)
{
return Out(tf.reshape(In(x), BackendUtil.CastShapeInt(shape)));
}
/// <summary>
/// Reshapes the data frame to specified new shape.
/// </summary>
/// <param name="newShape">The new shape.</param>
public void Reshape(params long[] newShape)
{
UnderlayingVariable = UnderlayingVariable.reshape(new Shape(BackendUtil.CastShapeInt(newShape)));
}
public Tensor Reshape(Tensor x, params long[] shape)
{
return(Out(C.Reshape(In(x), BackendUtil.CastShapeInt(shape))));
}
public Tensor RandomBernoulli(long[] shape, float p)
{
return(Out(C.BernoulliRandom(BackendUtil.CastShapeInt(shape), CNTK.DataType.Float, p)));
}
private Variable In(float value, params long[] shape)
{
return(new Parameter(BackendUtil.CastShapeInt(shape), CNTK.DataType.Float, value));
}
public void Load(float[] data, long[] shape)
{
UnderlayingVariable = np.array <float>(data);
UnderlayingVariable.reshape(BackendUtil.CastShapeInt(shape));
}
private Variable In(float value, params long[] shape)
{
Constant c = _constant(value, BackendUtil.CastShapeInt(shape));
return(c);
}
public Tensor Constant(float value, long[] shape)
{
return(Out(Data.Constant <float>(value, BackendUtil.CastShapeInt(shape))));
}
