|h}6dZddlZddlmcmZddlmZddlm Z ddl m Z ddl m Z ddlmZddlmZed Gd d Zed d GddeZeddGddeZeddGddeZeddGddeZeddGddeZeZeZeZeZeZeZeZeZed d&d!Z ed"d'd#Z!ed$d%Z"y)(z@Constraints: functions that impose constraints on weight values.N)backend) serialization)deserialize_keras_object)serialize_keras_object) keras_export) doc_controlszkeras.constraints.Constraintc,eZdZdZdZdZedZy) 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()) c|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). selfws W/var/www/html/test/engine/venv/lib/python3.12/site-packages/tf_keras/src/constraints.py__call__zConstraint.__call__;s ciS)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 rs r get_configzConstraint.get_configJs  rc|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. r r )clsconfigs r from_configzConstraint.from_configUs$}V}rN)__name__ __module__ __qualname____doc__rr classmethodrr rrr r !s%0  rr zkeras.constraints.MaxNormzkeras.constraints.max_normc`eZdZdZddZej dZej dZy)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)`. c ||_||_yN max_valueaxis)rr$r%s r__init__zMaxNorm.__init__s" rctjtjtj||j d}tj |d|j}||tj|zz zS)NTr%keepdimsr) rsqrttf reduce_sumsquarer%clipr$epsilonrrnormsdesireds rrzMaxNorm.__call__s` MM"))A,TYY F ,,ua8Gw0589::rc4|j|jdS)Nr#r#rs rrzMaxNorm.get_configs!^^TYY??rN)r rrrrr&rdo_not_generate_docsrrr rrr r jsB.&&;';&&@'@rr zkeras.constraints.NonNegzkeras.constraints.non_negceZdZdZdZy)NonNegz}Constrains the weights to be non-negative. Also available via the shortcut function `tf.keras.constraints.non_neg`. c|tjtj|dtjzS)N)r+cast greater_equalrfloatxr s rrzNonNeg.__call__s,2772++As3W^^5EFFFrN)rrrrrr rrr8r8s  Grr8zkeras.constraints.UnitNormzkeras.constraints.unit_normc`eZdZdZddZej dZej dZy)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)`. c||_yr"r%)rr%s rr&zUnitNorm.__init__s  rc |tjtjtjtj ||j dzz S)NTr()rr/r*r+r,r-r%r s rrzUnitNorm.__call__sF OO ll biilTJ   rcd|jiS)Nr%rArs rrzUnitNorm.get_configs ""rN)rr5r rrr?r?s@$&& ' &&#'#rr?zkeras.constraints.MinMaxNormzkeras.constraints.min_max_normc`eZdZdZddZej dZej dZy) MinMaxNorma(MinMaxNorm weight constraint. Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound. Also available via the shortcut function `tf.keras.constraints.min_max_norm`. Args: min_value: the minimum norm for the incoming weights. max_value: the maximum norm for the incoming weights. rate: rate for enforcing the constraint: weights will be rescaled to yield `(1 - rate) * norm + rate * norm.clip(min_value, max_value)`. Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval. axis: integer, axis along which to calculate weight norms. For instance, in a `Dense` layer the weight matrix has shape `(input_dim, output_dim)`, set `axis` to `0` to constrain each weight vector of length `(input_dim,)`. In a `Conv2D` layer with `data_format="channels_last"`, the weight tensor has shape `(rows, cols, input_depth, output_depth)`, set `axis` to `[0, 1, 2]` to constrain the weights of each filter tensor of size `(rows, cols, input_depth)`. c<||_||_||_||_yr" min_valuer$rater%)rrHr$rIr%s rr&zMinMaxNorm.__init__s""  rcbtjtjtj||j d}|j tj||j|jzd|j z |zz}||tj|zz zS)NTr() rr*r+r,r-r%rIr.rHr$r/r0s rrzMinMaxNorm.__call__s MM"))A,TYY F  II UDNNDNNK K499}% & Gw0589::rc`|j|j|j|jdS)NrGrGrs rrzMinMaxNorm.get_configs*IIII   rN)r:?rMrr5r rrrErEs@> &&;';&& ' rrEz"keras.constraints.RadialConstraintz#keras.constraints.radial_constraintc:eZdZdZej dZdZy)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)`. c |j}|j|jdk7rtd||\}}}}tj|||||zf}tj |j tjtj|dd}tjtjtj|dd||||fS)NzGThe weight tensor must have rank 4. Received weight tensor with shape: rAr) shaperank ValueErrorrreshapemap_fn_kernel_constraintstackr+unstack)rrw_shapeheightwidthchannelskernelss rrzRadialConstraint.__call__!s'' << 7<<1#466=Y@  ,3(x OOAx'/AB C NN  # # MM"**QR0q 9  MM"**QQ/b 9 UHg .  rc  tjddgddggdtjd}tj|dz d tjtjt j j|dd fd fd}tjtjt j j|ddd d } fd } fd }t jjj||||g|jt jd d gg\}}|S)zKRadially constraints a kernel with shape (height, width, channels).rKint32dtyperr4boolc&dz dz fS)NrKr kernelstartsrz5RadialConstraint._kernel_constraint..As#F519u,eai%.??@rcjdz dz ftjdjzS)NrK)r4r4rb)rzerosrcrfsrriz5RadialConstraint._kernel_constraint..Bs:F519u,eai%.??@mmF&,,78rc0tjddS)Nrrarbrconstantr rrriz5RadialConstraint._kernel_constraint..GG$$Qg6rc0tjddS)NrKrarbrmr rrriz5RadialConstraint._kernel_constraint..Hrorc0tj|Sr")rless)indexargsrhs rriz5RadialConstraint._kernel_constraint..Jsw||E5/Irc T|dztj||z|zffS)NrK)constant_values)r+pad)iarrayrgpaddingrhs rbody_fnz4RadialConstraint._kernel_constraint..body_fnLs8q5"&&wuqy%!)7K0L rN)shape_invariants) rrnrSr;switchr+mathfloormodcompatv1 while_loop get_shape TensorShape) rrg kernel_shape kernel_newrswhile_conditionr{_rzrhs ` @@rrXz#RadialConstraint._kernel_constraint7s""QFQF#37C}}V,Q/  \A-w7^^ LL)),:F C @ 8  LL)),:F C 6 6  J   //   J #oo/t 1MN 0 : rN)rrrrrr6rrXr rrrOrOs&:&& ' * rrOzkeras.constraints.serializec|yt|ts"tjdt |d|rt j |St |S)NzThe `keras.constraints.serialize()` API should only be used for objects of type `keras.constraints.Constraint`. Found an instance of type z+, which may lead to improper serialization.) isinstancer warningswarntypelegacy_serializationr) constraintuse_legacy_formats r serializerhs\ j* -  J'()  #:::FF !* --rzkeras.constraints.deserializecv|r!tj|t|dSt|t|dS)Nr)module_objectscustom_objectsprintable_module_name)rrglobals)rrrs r deserializerxsC#<< "9)".   $y%*  rzkeras.constraints.getc|yt|trd|v}t||St|trt|id}t |St |r|St d|)z)Retrieves a TF-Keras constraint function.Nmodule)r) class_namerz4Could not interpret constraint function identifier: )rdictrstrgetcallablerU) identifierrrs rrrsw*d#$J6:9JKK J $ #J2>6{ * B:, O  r)F)NF)#rrtensorflow.compat.v2rv2r+ tf_keras.srcrtf_keras.src.saving.legacyrr%tf_keras.src.saving.serialization_librr tensorflow.python.util.tf_exportrtensorflow.tools.docsrr r r8r?rErOmax_normnon_neg unit_norm min_max_normradial_constraintmaxnormnonnegunitnormrrrr rrrs"G!! LJH:.,-EE.EP)+GH&@j&@I&@R(*EFGZGGG*,IJ!#z!#K!#H,.NO8 8 P8 v(*OTzTTr     $   +, .- .-. /  %& ' r