o ={c2@s@dZddlmmZddlmZddlmZddlm Z ddl m Z ddl m Z e dGd d d Ze d d Gd ddeZe ddGdddeZe ddGdddeZe ddGdddeZe ddGdddeZeZeZeZeZeZeZeZeZe dd d!Ze d"d(d#d$Ze d%d&d'ZdS))z@Constraints: functions that impose constraints on weight values.N)backend)deserialize_keras_objectserialize_keras_object) keras_export) doc_controlszkeras.constraints.Constraintc@s,eZdZdZddZddZeddZdS) ConstraintaoBase class for weight constraints. A `Constraint` instance works like a stateless function. Users who subclass this class should override the `__call__` method, which takes a single weight parameter and return a projected version of that parameter (e.g. normalized or clipped). Constraints can be used with various Keras layers via the `kernel_constraint` or `bias_constraint` arguments. Here's a simple example of a non-negative weight constraint: >>> class NonNegative(tf.keras.constraints.Constraint): ... ... def __call__(self, w): ... return w * tf.cast(tf.math.greater_equal(w, 0.), w.dtype) >>> weight = tf.constant((-1.0, 1.0)) >>> NonNegative()(weight) >>> tf.keras.layers.Dense(4, kernel_constraint=NonNegative()) cCs|S)agApplies the constraint to the input weight variable. By default, the inputs weight variable is not modified. Users should override this method to implement their own projection function. Args: w: Input weight variable. Returns: Projected variable (by default, returns unmodified inputs). selfwr r 1lib/python3.10/site-packages/keras/constraints.py__call__8s zConstraint.__call__cCsiS)aReturns a Python dict of the object config. A constraint config is a Python dictionary (JSON-serializable) that can be used to reinstantiate the same object. Returns: Python dict containing the configuration of the constraint object. r r r r r get_configGs zConstraint.get_configcCs|di|S)aInstantiates a weight constraint from a configuration dictionary. Example: ```python constraint = UnitNorm() config = constraint.get_config() constraint = UnitNorm.from_config(config) ``` Args: config: A Python dictionary, the output of `get_config`. Returns: A `tf.keras.constraints.Constraint` instance. Nr r )clsconfigr r r from_configRszConstraint.from_configN)__name__ __module__ __qualname____doc__rr classmethodrr r r r rs  rzkeras.constraints.MaxNormzkeras.constraints.max_normc@s6eZdZdZd ddZejddZejdd Zd S) MaxNormaQMaxNorm weight constraint. Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value. Also available via the shortcut function `tf.keras.constraints.max_norm`. Args: max_value: the maximum norm value for the incoming weights. axis: integer, axis along which to calculate weight norms. For instance, in a `Dense` layer the weight matrix has shape `(input_dim, output_dim)`, set `axis` to `0` to constrain each weight vector of length `(input_dim,)`. In a `Conv2D` layer with `data_format="channels_last"`, the weight tensor has shape `(rows, cols, input_depth, output_depth)`, set `axis` to `[0, 1, 2]` to constrain the weights of each filter tensor of size `(rows, cols, input_depth)`. rcCs||_||_dSN max_valueaxis)r rrr r r __init__s zMaxNorm.__init__cCsBttjt||jdd}t|d|j}||t|S)NTrZkeepdimsr) rsqrttf reduce_sumsquarercliprepsilonr r ZnormsZdesiredr r r rs zMaxNorm.__call__cCs|j|jdS)Nrrrr r r rszMaxNorm.get_configN)rr rrrrrrdo_not_generate_docsrrr r r r rgs  rzkeras.constraints.NonNegzkeras.constraints.non_negc@seZdZdZddZdS)NonNegz}Constrains the weights to be non-negative. Also available via the shortcut function `tf.keras.constraints.non_neg`. cCs|tt|dtS)N)r"castZ greater_equalrZfloatxr r r r rszNonNeg.__call__N)rrrrrr r r r r*s r*zkeras.constraints.UnitNormzkeras.constraints.unit_normc@s6eZdZdZd ddZejddZejddZd S) UnitNormaConstrains the weights incident to each hidden unit to have unit norm. Also available via the shortcut function `tf.keras.constraints.unit_norm`. Args: axis: integer, axis along which to calculate weight norms. For instance, in a `Dense` layer the weight matrix has shape `(input_dim, output_dim)`, set `axis` to `0` to constrain each weight vector of length `(input_dim,)`. In a `Conv2D` layer with `data_format="channels_last"`, the weight tensor has shape `(rows, cols, input_depth, output_depth)`, set `axis` to `[0, 1, 2]` to constrain the weights of each filter tensor of size `(rows, cols, input_depth)`. rcCs ||_dSrr)r rr r r rs zUnitNorm.__init__c Cs*|tttjt||jddS)NTr )rr&r!r"r#r$rr r r r rszUnitNorm.__call__cCs d|jiS)Nrr.rr r r rs zUnitNorm.get_configN)rr(r r r r r-s  r-zkeras.constraints.MinMaxNormzkeras.constraints.min_max_normc@s6eZdZdZd ddZejddZejd d Zd S) MinMaxNorma(MinMaxNorm weight constraint. Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound. Also available via the shortcut function `tf.keras.constraints.min_max_norm`. Args: min_value: the minimum norm for the incoming weights. max_value: the maximum norm for the incoming weights. rate: rate for enforcing the constraint: weights will be rescaled to yield `(1 - rate) * norm + rate * norm.clip(min_value, max_value)`. Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval. axis: integer, axis along which to calculate weight norms. For instance, in a `Dense` layer the weight matrix has shape `(input_dim, output_dim)`, set `axis` to `0` to constrain each weight vector of length `(input_dim,)`. In a `Conv2D` layer with `data_format="channels_last"`, the weight tensor has shape `(rows, cols, input_depth, output_depth)`, set `axis` to `[0, 1, 2]` to constrain the weights of each filter tensor of size `(rows, cols, input_depth)`. r+?rcCs||_||_||_||_dSr min_valuerrater)r r2rr3rr r r rs zMinMaxNorm.__init__cCsXttjt||jdd}|jt||j|j d|j|}||t |S)NTr ) rr!r"r#r$rr3r%r2rr&r'r r r rs zMinMaxNorm.__call__cCs|j|j|j|jdS)Nr1r1rr r r rs zMinMaxNorm.get_configN)r+r0r0rr(r r r r r/s   r/z"keras.constraints.RadialConstraintz#keras.constraints.radial_constraintc@s&eZdZdZejddZddZdS)RadialConstraintaConstrains `Conv2D` kernel weights to be the same for each radius. Also available via the shortcut function `tf.keras.constraints.radial_constraint`. For example, the desired output for the following 4-by-4 kernel: ``` kernel = [[v_00, v_01, v_02, v_03], [v_10, v_11, v_12, v_13], [v_20, v_21, v_22, v_23], [v_30, v_31, v_32, v_33]] ``` is this:: ``` kernel = [[v_11, v_11, v_11, v_11], [v_11, v_33, v_33, v_11], [v_11, v_33, v_33, v_11], [v_11, v_11, v_11, v_11]] ``` This constraint can be applied to any `Conv2D` layer version, including `Conv2DTranspose` and `SeparableConv2D`, and with either `"channels_last"` or `"channels_first"` data format. The method assumes the weight tensor is of shape `(rows, cols, input_depth, output_depth)`. cCs|j}|jdus |jdkrtd||\}}}}t|||||f}t|jtjtj |dddd}ttjtj |dddd||||fS)NzGThe weight tensor must have rank 4. Received weight tensor with shape: r.r) shapeZrank ValueErrorrZreshapeZmap_fn_kernel_constraintstackr"Zunstack)r r Zw_shapeZheightwidthZchannelsZkernelsr r r rs"  zRadialConstraint.__call__c stjddgddggddtd}t|ddtttj|ddfddfd d}tttj|ddd dd d}fd d}fd d}tjj j ||||g| t ddggd\}}|S)zKRadially constraints a kernel with shape (height, width, channels).r4int32dtyperrboolcsddfS)Nr4r r kernelstartr r >sz5RadialConstraint._kernel_constraint..cs,ddftjdjdS)Nr4)rrr>)rZzerosr?r rAr r rD?scStjdddS)Nrr=r>rconstantr r r r rDDcSrE)Nr4r=r>rFr r r r rDErHcs t|Sr)rZless)indexargs)rCr r rDGs cs(|dtj|||fdfS)Nr4)Zconstant_values)r"Zpad)iZarrayrBZpaddingrCr r body_fnIs z4RadialConstraint._kernel_constraint..body_fnN)Zshape_invariants) rrGr8r,Zswitchr"ZmathZfloormodcompatZv1Z while_loopZ get_shapeZ TensorShape)r rBZ kernel_shapeZ kernel_newrIZwhile_conditionrM_r rLr r:4s,    z#RadialConstraint._kernel_constraintN)rrrrrr)rr:r r r r r5s   r5zkeras.constraints.serializecCst|Srr) constraintr r r serializeesrQzkeras.constraints.deserializecCst|t|ddS)NrP)Zmodule_objectscustom_objectsZprintable_module_name)rglobals)rrRr r r deserializejs rTzkeras.constraints.getcCsX|durdSt|trt|St|trt|id}t|St|r%|Std|)z&Retrieves a Keras constraint function.N) class_namerz4Could not interpret constraint function identifier: ) isinstancedictrTstrcallabler9)Z identifierrr r r getts  rZr)rZtensorflow.compat.v2rNZv2r"ZkerasrZ!keras.saving.legacy.serializationrrZ tensorflow.python.util.tf_exportrZtensorflow.tools.docsrrrr*r-r/r5Zmax_normZnon_negZ unit_normZ min_max_normZradial_constraintZmaxnormZnonnegZunitnormrQrTrZr r r r sF     H) $;Y