public Sequential(Layer[] layers = null, string name = "", Context context = null, string kvstore = "device")
: base(name, context, kvstore)
{
if (string.IsNullOrWhiteSpace(name))
{
var prefix = this.GetType().Name.ToLower();
this.name = prefix + "_" + K.GetUid(prefix).ToString();
}
this.batch_input_shape = null;
// Add to the model any layers passed to the constructor.
if (layers != null)
{
foreach (var layer in layers)
{
this.Add(layer);
}
}
}
public InputLayer(
Shape input_shape = null,
int?batch_size = null,
Shape batch_input_shape = null,
DType dtype = null,
KerasSymbol input_tensor = null,
bool sparse = false,
string name = null)
{
if (name != null)
{
var prefix = "input";
name = prefix + "_" + K.GetUid(prefix).ToString();
}
this.trainable = false;
this.built = true;
this.sparse = sparse;
this.supports_masking = true;
if (input_shape != null && batch_input_shape != null)
{
throw new Exception("Only provide the input_shape OR batch_input_shape argument to InputLayer, not both at the same time.");
}
if (input_tensor != null && batch_input_shape == null)
{
// If input_tensor is set, and batch_input_shape is not set:
// Attempt automatic input shape inference.
try
{
batch_input_shape = input_tensor.Shape;
}
catch (Exception)
{
if (input_shape == null && batch_input_shape == null)
{
throw new Exception("InputLayer was provided an input_tensor argument, but its input shape cannot be automatically inferred. You should pass an input_shape or batch_input_shape argument.");
}
}
}
if (batch_input_shape == null)
{
if (input_shape == null)
{
throw new Exception("An Input layer should be passed either a `batch_input_shape` or an `input_shape`.");
}
else
{
var batchShapeData = input_shape.Data.ToList();
batchShapeData.Insert(0, batch_size.Value);
batch_input_shape = new Shape(batchShapeData);
}
}
else
{
batch_input_shape = new Shape(batch_input_shape);
}
if (dtype == null)
{
if (input_tensor == null)
{
dtype = K.FloatX();
}
else
{
dtype = K.DataType(input_tensor);
}
}
this.batch_input_shape = batch_input_shape;
this.dtype = dtype;
if (input_tensor == null)
{
this.is_placeholder = true;
input_tensor = K.Placeholder(shape: batch_input_shape, dtype: dtype, sparse: this.sparse, name: this.name);
}
else
{
this.is_placeholder = false;
input_tensor._keras_shape = batch_input_shape;
}
// Create an input node to add to this.outbound_node
// and set output_tensors' _keras_history.
input_tensor._uses_learning_phase = false;
input_tensor._keras_history = (this, 0, 0);
var node = new Node(this,
inbound_layers: new Layer[0],
node_indices: new int[0],
tensor_indices: new int[0],
input_tensors: new KerasSymbol[] { input_tensor },
output_tensors: new KerasSymbol[] { input_tensor },
input_masks: new KerasSymbol[] { null },
output_masks: new KerasSymbol[] { null },
input_shapes: new Shape[] { batch_input_shape },
output_shapes: new Shape[] { batch_input_shape }
);
}