tf.keras.Variable

Represents a backend-agnostic variable in Keras.

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Compat aliases for migration

tf.compat.v1.keras.Variable

tf.keras.Variable( initializer, shape=None, dtype=None, trainable=True, autocast=True, aggregation='mean', name=None )

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.

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

Examples:

Initializing a Variable with a NumPy array:

import numpy as np import keras initial_array = np.ones((3, 3)) variable_from_array = keras.Variable(initializer=initial_array)

Using a Keras initializer to create a Variable:

from keras.src.initializers import Ones variable_from_initializer = keras.Variable( initializer=Ones(), shape=(3, 3), dtype="float32" )

Updating the value of a Variable:

new_value = np.zeros((3, 3), dtype="float32") variable_from_array.assign(new_value)

Marking a Variable as non-trainable:

non_trainable_variable = keras.Variable( initializer=np.ones((3, 3), dtype="float32"), trainable=False )

Attributes
`name`	The name of the variable (string).
`path`	The path of the variable within the Keras model or layer (string).
`dtype`	The data type of the variable (string).
`shape`	The shape of the variable (tuple of integers).
`ndim`	The number of dimensions of the variable (integer).
`trainable`	Whether the variable is trainable (boolean).
`value`	The current value of the variable (NumPy array or tensor).
`aggregation`
`constraint`
`handle`
`overwrite_with_gradient`	Whether this variable should be overwritten by the gradient. This property is designed for a special case where we want to overwrite the variable directly with its computed gradient. For example, in float8 training, new `scale` and `amax_history` are computed as gradients, and we want to overwrite them directly instead of following the typical procedure such as gradient descent with a learning rate, gradient clipping and weight decaying.
`regularizer`

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Args

Examples:

Attributes

Methods

assign

assign_add

assign_sub

numpy

__abs__

__add__

__and__

__array__

__bool__

__eq__

__floordiv__

__ge__

__getitem__

__gt__

__invert__

__le__

__lt__

__matmul__

__mod__

__mul__

__ne__

__neg__

__or__

__pos__

__pow__

__radd__

__rand__

__rfloordiv__

__rmatmul__

__rmod__

__rmul__

__ror__

__rpow__

__rsub__

__rtruediv__

__rxor__

__sub__

__truediv__

__xor__

tf.keras.Variable

`assign`

`assign_add`

`assign_sub`

`numpy`

`abs`

`add`

`and`

`array`

`bool`

`eq`

`floordiv`

`ge`

`getitem`

`gt`

`invert`

`le`

`lt`

`matmul`

`mod`

`mul`

`ne`

`neg`

`or`

`pos`

`pow`

`radd`

`rand`

`rfloordiv`

`rmatmul`

`rmod`

`rmul`

`ror`

`rpow`

`rsub`

`rtruediv`

`rxor`

`sub`

`truediv`

`xor`