public NDArrayList Call(NDArrayList args)
{
NativeMethods.MXInvokeCachedOpEx(handle, args.Length, MxUtil.GetNDArrayHandles(args), out var num_outputs,
out var outputs, out var out_stypes);
var result = new NDArrayList();
for (var i = 0; i < num_outputs; i++)
{
result.Add(new NDArray(outputs[i]).ToSType((StorageStype)out_stypes[i]));
}
return(result.ToArray());
}
public static NDArrayList Grad(NDArrayList heads, NDArrayList variables, NDArrayList head_grads = null,
bool retain_graph = false, bool create_graph = true, bool train_mode = true)
{
var(head_handles, head_grads_handles) = ParseHead(heads, head_grads);
//var grad_handles = new IntPtr[head_handles.Length];
//var grad_stypes = new int[head_handles.Length];
NativeMethods.MXAutogradBackwardEx(head_handles.Length, head_handles, head_grads_handles, variables.Length,
MxUtil.GetNDArrayHandles(variables), Convert.ToInt32(retain_graph),
Convert.ToInt32(create_graph), Convert.ToInt32(train_mode), out var grad_handles, out var grad_stypes);
var result = new NDArrayList();
foreach (var item in grad_handles)
{
result.Add(new NDArray(item));
}
return(result.ToArray());
}
public void UpdateMetric(EvalMetric metric, NDArrayList labels, bool pre_sliced = false)
{
var labels_slice = new NDArrayList();
var i = 0;
train_execs.Zip(slices, (e, s) =>
{
if (!pre_sliced)
{
foreach (var label in labels)
{
labels_slice.Add(label.Slice(s.Begin, s.End.Value));
}
}
else
{
labels_slice.Add(labels[i]);
}
metric.Update(labels_slice.ToArray(), e.Outputs.ToArray());
i++;
return(true);
});
}
public void LoadDataBatch(DataBatch data_batch)
{
ExecuterManager.LoadData(data_batch, data_arrays.ToArray());
ExecuterManager.LoadData(data_batch, label_arrays.ToArray());
}