LostTech.TensorFlow : API Documentation

Type Lambda

Namespace tensorflow.keras.layers

Parent Layer

Interfaces ILambda

Wraps arbitrary expressions as a `Layer` object.

The `Lambda` layer exists so that arbitrary TensorFlow functions can be used when constructing `Sequential` and Functional API models. `Lambda` layers are best suited for simple operations or quick experimentation. For more advanced use cases, subclassing `keras.layers.Layer` is preferred. One reason for this is that when saving a Model, `Lambda` layers are saved by serializing the Python bytecode, whereas subclassed Layers are saved via overriding their `get_config` method and are thus more portable. Models that rely on subclassed Layers are also often easier to visualize and reason about.

Examples:

Variables can be created within a `Lambda` layer. Like with other layers, these variables will be created only once and reused if the `Lambda` layer is called on new inputs. If creating more than one variable in a given `Lambda` instance, be sure to use a different name for each variable. Note that calling sublayers from within a `Lambda` is not supported.

Example of variable creation: Note that creating two instances of `Lambda` using the same function will *not* share Variables between the two instances. Each instance of `Lambda` will create and manage its own weights.
Show Example 
# add a x -> x^2 layer
            model.add(Lambda(lambda x: x ** 2))

Methods

Properties

Public instance methods

Tensor call(IEnumerable<IGraphNodeBase> inputs, IGraphNodeBase mask, bool training)

Tensor call(IGraphNodeBase inputs, IGraphNodeBase mask, bool training)

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

IDictionary<string, object> arguments get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

Delegate function get; set;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

object input_spec_dyn get; set;

IList<object> losses get;

object losses_dyn get;

object mask get; set;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object PythonObject get;

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

bool trainable get; set;

object trainable_dyn get; set;

object trainable_variables get;

object trainable_variables_dyn get;

IList<object> trainable_weights get;

object trainable_weights_dyn get;

IList<object> updates get;

object updates_dyn get;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;