public void Connect(Layer layer)
{
_layer = layer;
if (args.InputTensors != null)
{
KerasInputs.AddRange(args.InputTensors);
}
// Wire up Node to Layers.
layer.InboundNodes.Add(this);
foreach (var kt in KerasInputs)
{
if (kt.KerasHistory == null)
{
continue;
}
var(inbound_layer, _, _) = kt.KerasHistory;
if (inbound_layer != null)
{
inbound_layer.OutboundNodes.Add(this);
}
}
// Set metadata on outputs.
var node_index = layer.InboundNodes.Count - 1;
foreach (var(i, tensor) in enumerate(Outputs))
{
tensor.KerasHistory = new KerasHistory(layer, node_index, i);
}
}
/// <summary>
/// Serializes `Node` for Functional API's `get_config`.
/// </summary>
/// <returns></returns>
public List <NodeConfig> serialize(Func <string, int, string> make_node_key, Dictionary <string, int> node_conversion_map)
{
return(KerasInputs.Select(x => {
var kh = x.KerasHistory;
var node_key = make_node_key(kh.Layer.Name, kh.NodeIndex);
var new_node_index = node_conversion_map.Get(node_key, 0);
return new NodeConfig
{
Name = kh.Layer.Name,
NodeIndex = new_node_index,
TensorIndex = kh.TensorIndex
};
}).ToList());
}
/// <summary>
/// Maps Keras Tensors to computed Tensors using `tensor_dict`.
/// </summary>
/// <param name="tensor_dict"></param>
/// <returns></returns>
public Tensors MapArguments(Dictionary <int, Queue <Tensor> > tensor_dict)
{
if (_single_positional_tensor_passed)
{
var kt_id = _keras_inputs_ids_and_indices[0];
return(tensor_dict[kt_id].Dequeue());
}
else
{
var flat_arguments = KerasInputs.Select(x => x).ToArray();
foreach (var(kt_index, kt_id) in enumerate(_keras_inputs_ids_and_indices))
{
flat_arguments[kt_index] = tensor_dict[kt_id].Dequeue();
}
return(flat_arguments);
}
}
/// <summary>
/// Maps Keras Tensors to computed Tensors using `tensor_dict`.
/// </summary>
/// <param name="tensor_dict"></param>
/// <returns></returns>
public Tensors MapArguments(Dictionary <long, Queue <Tensor> > tensor_dict)
{
if (KerasInputs.Count() == 1)
{
var kt_id = KerasInputs[0].Id;
return(tensor_dict[kt_id].Dequeue());
}
else
{
var flat_arguments = KerasInputs.Select(x => x).ToArray();
foreach (var(kt_index, kt) in enumerate(KerasInputs))
{
flat_arguments[kt_index] = tensor_dict[kt.Id].Dequeue();
}
return(flat_arguments);
}
}
public Node(Layer layer, NodeArgs args)
{
this.args = args;
this.Layer = layer;
if (args.InputTensors != null)
{
KerasInputs.AddRange(args.InputTensors);
}
foreach (var(i, ele) in enumerate(KerasInputs))
{
_keras_inputs_ids_and_indices[i] = ele.GetHashCode();
}
// Wire up Node to Layers.
layer.InboundNodes.Add(this);
foreach (var kt in KerasInputs)
{
if (kt.KerasHistory == null)
{
continue;
}
var(inbound_layer, _, _) = kt.KerasHistory;
if (inbound_layer != null)
{
inbound_layer.OutboundNodes.Add(this);
}
}
// Set metadata on outputs.
var node_index = layer.InboundNodes.Count - 1;
foreach (var(i, tensor) in enumerate(Outputs))
{
tensor.KerasHistory = new KerasHistory(layer, node_index, i, tensor);
}
// Cached for performance.
FlatInputIds = KerasInputs.Select(x => x.GetHashCode()).ToArray();
FlatOutputIds = Outputs.Select(x => x.GetHashCode()).ToArray();
}
public override string ToString()
=> $"{Layer.Name}, {KerasInputs.Count} inputs: {string.Join(",", KerasInputs.Select(x => x.name))}";
