public TensorFlowOpLayer(TensorFlowOpLayerArgs args)
: base(new LayerArgs
{
Name = TF_OP_LAYER_NAME_PREFIX + args.Name,
Trainable = args.Trainable,
DType = args.DType,
Autocast = false
})
{
this.args = args;
built = true;
}
public TensorFlowOpLayer(TensorFlowOpLayerArgs args)
: base(new LayerArgs
{
Name = "tf_op_layer_" + args.Name,
Trainable = args.Trainable,
DType = args.DType,
Autocast = false
})
{
this.args = args;
built = true;
}
public static void CreateKerasHistoryHelper(Tensors tensors, List <Operation> processed_ops, List <Layer> created_layers)
{
foreach (var tensor in tensors)
{
if (tensor.KerasHistory != null)
{
continue;
}
var op = tensor.op;
if (!processed_ops.Contains(op))
{
var layer_inputs = new List <Tensor>();
var constants = new Dictionary <int, NDArray>();
foreach (var(i, op_input) in enumerate(op.inputs._inputs))
{
if (uses_keras_history(op_input))
{
layer_inputs.Add(op_input);
}
else
{
tf_with(ops.init_scope(), delegate
{
constants[i] = keras.backend.eval_in_eager_or_function(op_input);
});
}
}
// recursively
CreateKerasHistoryHelper(layer_inputs, processed_ops, created_layers);
var opLayerArgs = new TensorFlowOpLayerArgs
{
NodeDef = op.node_def,
Constants = constants,
Name = op.name
};
var op_layer = new TensorFlowOpLayer(opLayerArgs);
created_layers.Add(op_layer);
op_layer.SetConnectivityMetadata(layer_inputs, op.outputs);
processed_ops.Add(op);
}
}
}
public Layer GetOpLayer(TensorFlowOpLayerArgs args) => new TensorFlowOpLayer(args);
