o 2h%]@sddlZddlmZddlmZddlmZddlm Z ddlm Z ddl m Z ddl mZdd l mZdd lmZdd lmZGd d d ZddZddZeddgddZddZddZedddZedddZd d!ZGd"d#d#ZdS)$Nbackend) keras_export)config)dtypes) global_state) current_path)get_stateless_scope)in_stateless_scope) tensorflow) auto_namec@s~eZdZdZ       dddZdd Zd d Zd d ZddZe ddZ e ddZ e ddZ ddZ ddZddZe ddZe ddZe d d!Ze d"d#Zejd$d#Ze d%d&Ze d'd(Ze d)d*Zejd+d*Ze d,d-Zejd.d-Ze d/d0Zejd1d0Zd2d3Zd4d5Zd6d7Zdd8d9Zd:d;Zdd?Z dd@dAZ!dBdCZ"dDdEZ#dFdGZ$dHdIZ%dJdKZ&dLdMZ'dNdOZ(dPdQZ)dRdSZ*dTdUZ+dVdWZ,dXdYZ-dZd[Z.d\d]Z/d^d_Z0d`daZ1dbdcZ2dddeZ3dfdgZ4dhdiZ5djdkZ6dldmZ7dndoZ8dpdqZ9drdsZ:dtduZ;dvdwZd|d}Z?d~dZ@ddZAddZBdddZCdS)Variablea Represents a backend-agnostic variable in Keras. 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`. 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 `None`, `"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). kwargs: Additional backend-specific keyword arguments. Examples: **Initializing a `Variable` with a NumPy array:** ```python 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`:** ```python from keras.src.initializers import Ones variable_from_initializer = keras.Variable( initializer=Ones(), shape=(3, 3), dtype="float32" ) ``` **Updating the value of a `Variable`:** ```python new_value = np.zeros((3, 3), dtype="float32") variable_from_array.assign(new_value) ``` **Marking a `Variable` as non-trainable:** ```python non_trainable_variable = keras.Variable( initializer=np.ones((3, 3), dtype="float32"), trainable=False ) ``` NTnoneautoc Ks~ |pt|jj}t|trd|vrtd||dvr$td||dur*d}|dvr5td||dur;d}||_t} | rLtd||_n||_d|_ d|_ d|_ d|_ t ||_t ||_||_||_d|_t|tr~d d lm} | |}t|r|durtd |d |n|j||d }|dur|j}t||_trt|rd|_||_ |||_ t|n!tdt|r|||_ ||n | |||jj!|_ t"|j |_#dS)N/zRArgument `name` must be a string and cannot contain character `/`. 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