/// <summary>
/// Maintains moving averages of variables.
/// </summary>
/// <param name="var_list"></param>
/// <returns></returns>
public Operation apply(RefVariable[] var_list = null)
{
if (var_list == null)
{
var_list = variables.trainable_variables() as RefVariable[];
}
foreach (var var in var_list)
{
if (!_averages.ContainsKey(var))
{
ops.init_scope();
var slot_creator = new SlotCreator();
var value = var.initialized_value();
var avg = slot_creator.create_slot(var,
value,
name,
colocate_with_primary: true);
ops.add_to_collection(tf.GraphKeys.MOVING_AVERAGE_VARIABLES, var);
_averages[var] = avg;
}
else
{
// avg = slot_creator.create_zeros_slot(
throw new NotImplementedException("");
}
}
return(tf_with(ops.name_scope(name), scope =>
{
var decay = ops.convert_to_tensor(_decay, name: "decay");
if (_num_updates.HasValue)
{
throw new NotImplementedException("ExponentialMovingAverage.apply");
}
var updates = new List <Tensor>();
foreach (var var in var_list)
{
var zero_debias = false;// _averages[var] in zero_debias_true
var ama = moving_averages.assign_moving_average(_averages[var], var, decay, zero_debias: zero_debias);
updates.Add(ama);
}
return control_flow_ops.group(updates.ToArray(), name: scope);
}));
}
/// <summary>
/// Maintains moving averages of variables.
/// </summary>
/// <param name="var_list"></param>
/// <returns></returns>
public Operation apply(RefVariable[] var_list = null)
{
if (var_list == null)
{
var_list = variables.trainable_variables() as RefVariable[];
}
foreach (var var in var_list)
{
if (!_averages.Contains(var))
{
ops.init_scope();
var slot = new SlotCreator();
var.initialized_value();
// var avg = slot.create_zeros_slot
}
}
throw new NotImplementedException("");
}