static public RNN RNN(long inputSize, long hiddenSize, long numLayers = 1, RNN.NonLinearities nonLinearity = NN.RNN.NonLinearities.Tanh, bool bias = true, bool batchFirst = false, double dropout = 0.0, bool bidirectional = false)
{
var res = THSNN_RNN_ctor(inputSize, hiddenSize, numLayers, (long)nonLinearity, bias, batchFirst, dropout, bidirectional, out var boxedHandle);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new RNN(res, boxedHandle));
}
static public RNNCell RNNCell(long inputSize, long hiddenSize, RNN.NonLinearities nonLinearity = NN.RNN.NonLinearities.Tanh, bool bias = true)
{
var res = THSNN_RNNCell_ctor(inputSize, hiddenSize, (long)nonLinearity, bias, out var boxedHandle);
if (res == IntPtr.Zero)
{
Torch.CheckForErrors();
}
return(new RNNCell(res, boxedHandle));
}
