/// <summary>
/// Instantiate a Keras tensor.
/// </summary>
/// <param name="shape"></param>
/// <param name="batch_size"></param>
/// <param name="dtype"></param>
/// <param name="name"></param>
/// <param name="sparse">
/// A boolean specifying whether the placeholder to be created is sparse.
/// </param>
/// <param name="ragged">
/// A boolean specifying whether the placeholder to be created is ragged.
/// </param>
/// <param name="tensor">
/// Optional existing tensor to wrap into the `Input` layer.
/// If set, the layer will not create a placeholder tensor.
/// </param>
/// <returns></returns>
public Tensor Input(TensorShape shape = null,
int batch_size = -1,
TensorShape batch_input_shape = null,
TF_DataType dtype = TF_DataType.DtInvalid,
string name = null,
bool sparse = false,
bool ragged = false,
Tensor tensor = null)
{
if (batch_input_shape != null)
{
shape = batch_input_shape.dims.Skip(1).ToArray();
}
var args = new InputLayerArgs
{
Name = name,
InputShape = shape,
BatchInputShape = batch_input_shape,
BatchSize = batch_size,
DType = dtype,
Sparse = sparse,
Ragged = ragged,
InputTensor = tensor
};
var layer = new InputLayer(args);
return(layer.InboundNodes[0].Outputs);
}
public InputLayer(InputLayerArgs args) :
base(args)
{
this.args = args;
built = true;
this.SupportsMasking = true;
if (BatchInputShape != null)
{
args.BatchSize = BatchInputShape.dims[0];
args.InputShape = BatchInputShape.dims[1..];
public InputLayer(InputLayerArgs args) :
base(args)
{
this.args = args;
built = true;
SupportsMasking = true;
if (BatchInputShape != null)
{
args.BatchSize = BatchInputShape.dims[0];
args.InputShape = BatchInputShape.dims.Skip(1).ToArray();
}
// moved to base class
if (string.IsNullOrEmpty(args.Name))
{
var prefix = "input";
name = prefix + '_' + keras.backend.get_uid(prefix);
args.Name = name;
}
if (args.DType == TF_DataType.DtInvalid)
{
args.DType = args.InputTensor == null ? tf.float32 : args.InputTensor.dtype;
}
if (args.InputTensor == null)
{
if (args.InputShape != null)
{
args.BatchInputShape = new int[] { args.BatchSize }
.Concat(args.InputShape.dims)
.ToArray();
}
else
{
args.BatchInputShape = null;
}
var graph = keras.backend.get_graph();
graph.as_default();
args.InputTensor = keras.backend.placeholder(
shape: BatchInputShape,
dtype: DType,
name: Name,
sparse: args.Sparse,
ragged: args.Ragged);
graph.Exit();
isPlaceholder = true;
}
// Create an input node to add to self.outbound_node
// and set output_tensors' _keras_history.
// input_tensor._keras_history = base_layer.KerasHistory(self, 0, 0)
// input_tensor._keras_mask = None
var node = new Node(new NodeArgs
{
Outputs = args.InputTensor
});
node.Connect(this);
typeSpec = new TensorSpec(args.InputTensor.TensorShape,
dtype: args.InputTensor.dtype,
name: Name);
}
