/// <summary>
/// Adds a layer instance on top of the layer stack.
/// </summary>
/// <param name="layer"></param>
public void add(ILayer layer)
{
built = false;
var set_inputs = false;
if (_layers.Count == 0)
{
if (layer is InputLayer)
{
set_inputs = true;
}
else
{
if (layer.BatchInputShape != null)
{
// Instantiate an input layer.
var x = keras.Input(
batch_input_shape: layer.BatchInputShape,
dtype: layer.DType,
name: layer.Name + "_input");
// This will build the current layer
// and create the node connecting the current layer
// to the input layer we just created.
layer.Apply(x);
set_inputs = true;
}
}
if (set_inputs)
{
// If an input layer (placeholder) is available.
outputs = layer.InboundNodes.Last().Outputs;
inputs = layer_utils.get_source_inputs(outputs[0]);
built = true;
_has_explicit_input_shape = true;
}
}
else if (outputs != null)
{
outputs = layer.Apply(outputs);
built = true;
}
if (set_inputs || _is_graph_network)
{
_init_graph_network(inputs, outputs);
_is_graph_network = true;
}
else
{
_self_tracked_trackables.add(layer);
_handle_deferred_layer_dependencies(layer);
}
}
/// <summary>
/// Adds a layer instance on top of the layer stack.
/// </summary>
/// <param name="layer"></param>
public void add(ILayer layer)
{
built = false;
var set_inputs = false;
if (_layers.Count == 0)
{
if (layer is InputLayer)
{
set_inputs = true;
}
else
{
if (layer.BatchInputShape != null)
{
// Instantiate an input layer.
var x = keras.Input(
batch_input_shape: layer.BatchInputShape,
dtype: layer.DType,
name: layer.Name + "_input");
// This will build the current layer
// and create the node connecting the current layer
// to the input layer we just created.
layer.Apply(x);
set_inputs = true;
}
}
if (set_inputs)
{
// If an input layer (placeholder) is available.
outputs = layer.InboundNodes[^ 1].Outputs;
// Set forward pass.
protected override Tensor[] call(Tensor inputs, bool is_training = false, Tensor state = null)
{
var x = fc1.Apply(inputs);
throw new NotImplementedException("");
}
