public static KerasSymbol[] CreateInput(
Shape shape = null,
Shape batch_shape = null,
string name = null,
DType dtype = null,
bool sparse = false,
KerasSymbol tensor = null)
{
if (batch_shape == null && tensor == null)
{
Debug.Assert(shape != null, "Please provide to Input either a `shape` or a `batch_shape` argument. Note that `shape` does not include the batch dimension.");
}
if (shape != null && batch_shape != null)
{
var list = shape.Data.ToList();
list.Insert(0, 0);
batch_shape = new Shape(list);
}
if (dtype == null)
{
dtype = K.FloatX();
}
var input_layer = new InputLayer(batch_input_shape: batch_shape, name: name, dtype: dtype, sparse: sparse, input_tensor: tensor);
// Return tensor including _keras_shape and _keras_history.
// Note that in this case train_output and test_output are the same pointer.
var outputs = input_layer._inbound_nodes[0].output_tensors;
return(outputs);
}
public override void Build(Shape input_shape = null)
{
if (input_shape != null && this.inputs == null && this.inputs.Count > 0)
{
var batch_shape = input_shape;
var dtype = K.FloatX();
var x = InputLayer.CreateInput(batch_shape: batch_shape, dtype: dtype, name: this.name + "_input");
this.inputs = x.ToList();
foreach (var layer in this._layers)
{
x = layer.Invoke(x, null);
}
this.outputs = x.ToList();
this._build_input_shape = input_shape;
}
if (this.inputs != null && this.inputs.Count > 0)
{
this.InitGraphNetwork(this.inputs.ToArray(), this.outputs.ToArray(), name: this.name);
this.built = true;
}
}
public void Add(Layer layer)
{
this.built = false;
if (this._layers.Count == 0)
{
var set_inputs = false;
// First layer in model: check that it is an input layer.
if (!(layer is InputLayer))
{
// Create an input tensor and call `layer` on the input tensor.
// First, we need to infer the expected input shape and dtype.
var first_layer = layer;
if (first_layer.batch_input_shape != null)
{
var batch_shape = first_layer.batch_input_shape;
var dtype = first_layer.dtype;
// Instantiate the input layer.
var x = InputLayer.CreateInput(batch_shape: batch_shape, dtype: 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.Call(x);
set_inputs = true;
}
}
else
{
// Corner case where the user passes an InputLayer via `add`.
Debug.Assert(layer._inbound_nodes.Last().output_tensors.Length == 1);
set_inputs = true;
}
if (set_inputs)
{
if (layer._inbound_nodes.Count > 0 && layer._inbound_nodes.Last().output_tensors.Length != 1)
{
throw new Exception("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
}
this.outputs = new List <KerasSymbol> {
layer._inbound_nodes.Last().output_tensors[0]
};
this.inputs = MxNet.Keras.Utils.LayerUtils.GetSourceInputs(this.outputs[0]).ToList();
}
}
else if (this.outputs != null && this.outputs.Count > 0)
{
var output_tensor = layer.Call(this.outputs[0]);
if (output_tensor.Length > 1)
{
throw new Exception("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
}
this.outputs = output_tensor.ToList();
}
if (this.inputs.Count > 0)
{
this.Build();
}
else
{
this._layers.Add(layer);
}
}
public void Add(Model layer)
{
this.built = false;
if (this._layers.Count == 0)
{
var set_inputs = false;
// First layer in model: check that it is an input layer.
if (layer is Model || layer is Sequential)
{
// Create an input tensor and call `layer` on the input tensor.
// First, we need to infer the expected input shape and dtype.
if (layer.Layers == null || layer.Layers.Length == 0)
{
throw new Exception("Cannot add an empty model to a `Sequential` model.");
}
// In case of nested models: recover the first layer
// of the deepest model to infer input shape and dtype.
object first_layer = layer.Layers[0];
while (first_layer is Model || first_layer is Sequential)
{
first_layer = ((Model)first_layer).Layers[0];
}
if (((Layer)first_layer).batch_input_shape != null)
{
var batch_shape = ((Layer)first_layer).batch_input_shape;
var dtype = ((Layer)first_layer).dtype;
// Instantiate the input layer.
var x = InputLayer.CreateInput(batch_shape: batch_shape, dtype: 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.Call(x, null);
set_inputs = true;
}
}
else
{
// Corner case where the user passes an InputLayer via `add`.
Debug.Assert(layer._inbound_nodes.Last().output_tensors.Length == 1);
set_inputs = true;
}
if (set_inputs)
{
if (layer._inbound_nodes.Last().output_tensors.Length != 1)
{
throw new Exception("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
}
this.outputs = new List <KerasSymbol>
{
layer._inbound_nodes.Last().output_tensors[0]
};
this.inputs = MxNet.Keras.Utils.LayerUtils.GetSourceInputs(this.outputs[0]).ToList();
}
}
else if (this.outputs != null)
{
var output_tensor = layer.Invoke(new KerasSymbol[] { this.outputs[0] }, null);
if (output_tensor.Length > 1)
{
throw new Exception("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");
}
this.outputs = output_tensor.ToList();
}
if (this.inputs != null)
{
this.Build();
}
else
{
this._layers.Add(layer);
}
}
