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Represents a backend-agnostic variable in Keras.

Source: R/variable.R
keras_variable.Rd

A Variable acts as a container for state. It holds a tensor value and can be updated. With the JAX backend, variables are used to implement "functionalization", the pattern of lifting stateful operations out of a piece of computation to turn it into a stateless function.

Usage

keras_variable(
  initializer,
  shape = NULL,
  dtype = NULL,
  trainable = TRUE,
  autocast = TRUE,
  aggregation = "none",
  name = NULL
)

Arguments

initializer

Initial value or callable for initialization. If a callable is used, it should take the arguments shape and dtype.

shape

Optional. Tuple for the variable's shape. Required if initializer is a callable.

dtype

Optional. Data type of the variable. Defaults to the global float dtype type ("float32" if never configured).

trainable

Optional. Boolean indicating if variable is trainable. Defaults to TRUE.

autocast

Optional. Boolean indicating whether the variable supports autocasting. If TRUE, the layer may first convert the variable to the compute data type when accessed. Defaults to TRUE.

aggregation

Optional string, one of NULL, "none", "mean", "sum" or "only_first_replica" specifying how a distributed variable will be aggregated. This serves as a semantic annotation, to be taken into account by downstream backends or users. Defaults to "none".

name

Optional. A unique name for the variable. Automatically generated if not set.

Attributes

  • shape: The shape of the variable (tuple of integers).

  • ndim: The number of dimensions of the variable (integer).

  • dtype: The data type of the variable (string).

  • trainable: Whether the variable is trainable (boolean).

  • autocast: Whether the variable supports autocasting (boolean).

  • aggregation: How a distributed variable will be aggregated (string).

  • value: The current value of the variable (NumPy array or tensor).

  • name: The name of the variable (string).

  • path: The path of the variable within the Keras model or layer (string).

Examples

Initializing a Variable with a NumPy array:

initial_array <- array(1, c(3, 3))
variable_from_array <- keras_variable(initializer = initial_array)

Using a Keras initializer to create a Variable:

variable_from_initializer <- keras_variable(
  initializer = initializer_ones(),
  shape = c(3, 3),
  dtype = "float32"
)

new_value <- array(0, c(3, 3))
variable_from_array$assign(new_value)

## tf.Tensor(
## [[0. 0. 0.]
##  [0. 0. 0.]
##  [0. 0. 0.]], shape=(3, 3), dtype=float64)


# To modify a subset of values
value <- variable_from_array$value
value@r[1,] <- 99
invisible(variable_from_array$assign(value))
variable_from_array

## <Variable path=variable, shape=(3, 3), dtype=float64, value=[[99. 99. 99.]
##  [ 0.  0.  0.]
##  [ 0.  0.  0.]]>

Marking a Variable as non-trainable:

non_trainable_variable <- keras_variable(
  initializer = array(1, c(3, 3)),
  dtype = "float32",
  trainable = FALSE
)