Define a custom Layer class. — Layer • keras3

A layer is a callable object that takes as input one or more tensors and that outputs one or more tensors. It involves computation, defined in the call() method, and a state (weight variables). State can be created:

in initialize(), for instance via self$add_weight();
in the optional build() method, which is invoked by the first call() to the layer, and supplies the shape(s) of the input(s), which may not have been known at initialization time.

Layers are recursively composable: If you assign a Layer instance as an attribute of another Layer, the outer layer will start tracking the weights created by the inner layer. Nested layers should be instantiated in the initialize() method or build() method.

Users will just instantiate a layer and then treat it as a callable.

Usage

Layer(
  classname,
  initialize = NULL,
  call = NULL,
  build = NULL,
  get_config = NULL,
  ...,
  public = list(),
  private = list(),
  inherit = NULL,
  parent_env = parent.frame()
)

Arguments

classname: String, the name of the custom class. (Conventionally, CamelCase).
initialize, call, build, get_config: Recommended methods to implement. See description and details sections.
..., public: Additional methods or public members of the custom class.
private: Named list of R objects (typically, functions) to include in instance private environments. private methods will have all the same symbols in scope as public methods (See section "Symbols in Scope"). Each instance will have it's own private environment. Any objects in private will be invisible from the Keras framework and the Python runtime.
inherit: What the custom class will subclass. By default, the base keras class.
parent_env: The R environment that all class methods will have as a grandparent.

Value

A composing layer constructor, with similar behavior to other layer functions like layer_dense(). The first argument of the returned function will be object, enabling initialize()ing and call() the layer in one step while composing the layer with the pipe, like

layer_foo <- Layer("Foo", ....)
output <- inputs |> layer_foo()

To only initialize() a layer instance and not call() it, pass a missing or NULL value to object, or pass all arguments to initialize() by name.

layer <- layer_dense(units = 2, activation = "relu")
layer <- layer_dense(NULL, 2, activation = "relu")
layer <- layer_dense(, 2, activation = "relu")

# then you can call() the layer in a separate step
outputs <- inputs |> layer()

Symbols in scope

All R function custom methods (public and private) will have the following symbols in scope:

self: The custom class instance.
super: The custom class superclass.
private: An R environment specific to the class instance. Any objects assigned here are invisible to the Keras framework.
__class__ and as.symbol(classname): the custom class type object.

Attributes

name: The name of the layer (string).
dtype: Dtype of the layer's weights. Alias of layer$variable_dtype.
variable_dtype: Dtype of the layer's weights.
compute_dtype: The dtype of the layer's computations. Layers automatically cast inputs to this dtype, which causes the computations and output to also be in this dtype. When mixed precision is used with a keras$mixed_precision$DTypePolicy, this will be different than variable_dtype.
trainable_weights: List of variables to be included in backprop.
non_trainable_weights: List of variables that should not be included in backprop.
weights: The concatenation of the lists trainable_weights and non_trainable_weights (in this order).
trainable: Whether the layer should be trained (boolean), i.e. whether its potentially-trainable weights should be returned as part of layer$trainable_weights.
input_spec: Optional (list of) InputSpec object(s) specifying the constraints on inputs that can be accepted by the layer.

We recommend that custom Layers implement the following methods:

initialize(): Defines custom layer attributes, and creates layer weights that do not depend on input shapes, using add_weight(), or other state.
build(input_shape): This method can be used to create weights that depend on the shape(s) of the input(s), using add_weight(), or other state. Calling call() will automatically build the layer (if it has not been built yet) by calling build().
call(...): Method called after making sure build() has been called. call() performs the logic of applying the layer to the input arguments. Two reserved arguments you can optionally use in call() are:
1. training (boolean, whether the call is in inference mode or training mode).
2. mask (boolean tensor encoding masked timesteps in the input, used e.g. in RNN layers).
A typical signature for this method is call(inputs), and user could optionally add training and mask if the layer need them.
get_config(): Returns a named list containing the configuration used to initialize this layer. If the list names differ from the arguments in initialize(), then override from_config() as well. This method is used when saving the layer or a model that contains this layer.

Examples

Here's a basic example: a layer with two variables, w and b, that returns y <- (w %*% x) + b. It shows how to implement build() and call(). Variables set as attributes of a layer are tracked as weights of the layers (in layer$weights).

layer_simple_dense <- Layer(
  "SimpleDense",
  initialize = function(units = 32) {
    super$initialize()
    self$units <- units
  },

  # Create the state of the layer (weights)
  build = function(input_shape) {
    self$kernel <- self$add_weight(
      shape = shape(tail(input_shape, 1), self$units),
      initializer = "glorot_uniform",
      trainable = TRUE,
      name = "kernel"
    )
    self$bias = self$add_weight(
      shape = shape(self$units),
      initializer = "zeros",
      trainable = TRUE,
      name = "bias"
    )
  },

  # Defines the computation
  call = function(self, inputs) {
    op_matmul(inputs, self$kernel) + self$bias
  }
)

# Instantiates the layer.
# Supply missing `object` arg to skip invoking `call()` and instead return
# the Layer instance
linear_layer <- layer_simple_dense(, 4)

# This will call `build(input_shape)` and create the weights,
# and then invoke `call()`.
y <- linear_layer(op_ones(c(2, 2)))
stopifnot(length(linear_layer$weights) == 2)

# These weights are trainable, so they're listed in `trainable_weights`:
stopifnot(length(linear_layer$trainable_weights) == 2)

Besides trainable weights, updated via backpropagation during training, layers can also have non-trainable weights. These weights are meant to be updated manually during call(). Here's a example layer that computes the running sum of its inputs:

layer_compute_sum <- Layer(
  classname = "ComputeSum",

  initialize = function(input_dim) {
    super$initialize()

    # Create a non-trainable weight.
    self$total <- self$add_weight(
      shape = shape(),
      initializer = "zeros",
      trainable = FALSE,
      name = "total"
    )
  },

  call = function(inputs) {
    self$total$assign(self$total + op_sum(inputs))
    self$total
  }
)

my_sum <- layer_compute_sum(, 2)
x <- op_ones(c(2, 2))
y <- my_sum(x)

stopifnot(exprs = {
  all.equal(my_sum$weights,               list(my_sum$total))
  all.equal(my_sum$non_trainable_weights, list(my_sum$total))
  all.equal(my_sum$trainable_weights,     list())
})

Methods available

```
initialize(...,
           activity_regularizer = NULL,
           trainable = TRUE,
           dtype = NULL,
           autocast = TRUE,
           name = NULL)
```
Initialize self. This method is typically called from a custom initialize() method. Example:
```
layer_my_layer <- Layer("MyLayer",
  initialize = function(units, ..., dtype = NULL, name = NULL) {
    super$initialize(..., dtype = dtype, name = name)
    # .... finish initializing `self` instance
  }
)
```
Args:
- trainable: Boolean, whether the layer's variables should be trainable.
- name: String name of the layer.
- dtype: The dtype of the layer's computations and weights. Can also be a keras$DTypePolicy, which allows the computation and weight dtype to differ. Defaults to NULL. NULL means to use config_dtype_policy(), which is a "float32" policy unless set to different value (via config_set_dtype_policy()).

add_loss(loss)

Can be called inside of the call() method to add a scalar loss.

Example:

Layer("MyLayer",
  ...
  call = function(x) {
    self$add_loss(op_sum(x))
    x
  }

```
add_metric()
```
```
add_variable(...)
```
Add a weight variable to the layer.

Alias of add_weight().
```
add_weight(shape = NULL,
           initializer = NULL,
           dtype = NULL,
           trainable = TRUE,
           autocast = TRUE,
           regularizer = NULL,
           constraint = NULL,
           aggregation = 'mean',
           name = NULL)
```
Add a weight variable to the layer.

Args:
- shape: shape for the variable (as defined by shape()) Must be fully-defined (no NA/NULL/-1 entries). Defaults to () (scalar) if unspecified.
- initializer: Initializer object to use to populate the initial variable value, or string name of a built-in initializer (e.g. "random_normal"). If unspecified, defaults to "glorot_uniform" for floating-point variables and to "zeros" for all other types (e.g. int, bool).
- dtype: Dtype of the variable to create, e.g. "float32". If unspecified, defaults to the layer's variable dtype (which itself defaults to "float32" if unspecified).
- trainable: Boolean, whether the variable should be trainable via backprop or whether its updates are managed manually. Defaults to TRUE.
- autocast: Boolean, whether to autocast layers variables when accessing them. Defaults to TRUE.
- regularizer: Regularizer object to call to apply penalty on the weight. These penalties are summed into the loss function during optimization. Defaults to NULL.
- constraint: Constraint object to call on the variable after any optimizer update, or string name of a built-in constraint. Defaults to NULL.
- aggregation: String, one of 'mean', 'sum', 'only_first_replica'. Annotates the variable with the type of multi-replica aggregation to be used for this variable when writing custom data parallel training loops.
- name: String name of the variable. Useful for debugging purposes.
Returns:

A backend tensor, wrapped in a KerasVariable class. The KerasVariable class has

Methods:
- assign(value)
- assign_add(value)
- assign_sub(value)
- numpy() (calling as.array(<variable>) is preferred)
Properties/Attributes:
- value
- dtype
- ndim
- shape (calling shape(<variable>) is preferred)
- trainable
```
build(input_shape)
```
```
build_from_config(config)
```
Builds the layer's states with the supplied config (named list of args).

By default, this method calls the do.call(build, config$input_shape) method, which creates weights based on the layer's input shape in the supplied config. If your config contains other information needed to load the layer's state, you should override this method.

Args:
- config: Named list containing the input shape associated with this layer.
```
call(...)
```
See description above
```
compute_mask(inputs, previous_mask)
```
```
compute_output_shape(...)
```
```
compute_output_spec(...)
```
```
count_params()
```
Count the total number of scalars composing the weights.

Returns: An integer count.
```
get_build_config()
```
Returns a named list with the layer's input shape.

This method returns a config (named list) that can be used by build_from_config(config) to create all states (e.g. Variables and Lookup tables) needed by the layer.

By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.

Returns: A named list containing the input shape associated with the layer.
```
get_config()
```
Returns the config of the object.

An object config is a named list (serializable) containing the information needed to re-instantiate it. The config is expected to be serializable to JSON, and is expected to consist of a (potentially complex, nested) structure of names lists consisting of simple objects like strings, ints.
```
get_weights()
```
Return the values of layer$weights as a list of R or NumPy arrays.

quantize(mode)

Currently, only the Dense and EinsumDense layers support in-place quantization via this quantize() method.