o i en6@sVdZddlZddlmmZddlmZddlm Z ddl m Z ddl m Z ddlmZddlmZed Gd d d Zed d GdddeZeddGdddeZeddGdddeZeddGdddeZeddGdddeZeZeZeZeZeZeZeZeZed d*d"d#Z ed$d+d%d&Z!ed'd(d)Z"dS),z@Constraints: functions that impose constraints on weight values.N)backend) serialization)deserialize_keras_object)serialize_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 D/var/www/myenv/lib/python3.10/site-packages/keras/src/constraints.py__call__;s 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_configJs 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_configUszConstraint.from_configN)__name__ __module__ __qualname____doc__rr classmethodrr r r r r!s  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)NTrkeepdimsr) rsqrttf reduce_sumsquarercliprepsilonr r normsdesiredr r r rs zMaxNorm.__call__cCs|j|jdS)Nrrrr r r rszMaxNorm.get_configN)rr rrrrrrdo_not_generate_docsrrr r r r rjs  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#cast greater_equalrfloatxr 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)Nrr3rr r r rs zUnitNorm.get_configN)rr+r r r r r2s  r2zkeras.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 r7rr8rr r r rs zMinMaxNorm.__init__cCsXttjt||jdd}|jt||j|j d|j|}||t |S)NTr ) rr"r#r$r%rr8r&r7rr'r(r r r rs zMinMaxNorm.__call__cCs|j|j|j|jdS)Nr6r6rr r r rs zMinMaxNorm.get_configN)r.r5r5rr+r r r r r4s   r4z"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: r3r) shaperank ValueErrorrreshapemap_fn_kernel_constraintstackr#unstack)r r w_shapeheightwidthchannelskernelsr r r r!s"  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).r9int32dtyperrboolcsddfS)Nr9r r kernelstartr r Asz5RadialConstraint._kernel_constraint..cs,ddftjdjdS)Nr9)rrrK)rzerosrLr rNr r rQBscStjdddS)NrrJrKrconstantr r r r rQGcSrS)Nr9rJrKrTr r r r rQHrVcs t|Sr)rless)indexargs)rPr r rQJs cs(|dtj|||fdfS)Nr9)constant_values)r#pad)iarrayrOpaddingrPr r body_fnLs z4RadialConstraint._kernel_constraint..body_fnN)shape_invariants) rrUr=r/switchr#mathfloormodcompatv1 while_loop get_shape TensorShape)r rO kernel_shape kernel_newrXwhile_conditionr`_r r^r rB7s,    z#RadialConstraint._kernel_constraintN)rrrrrr,rrBr r r r r:s   r:zkeras.constraints.serializeFcCsB|durdSt|tstdt|d|rt|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.) isinstancerwarningswarntypelegacy_serializationr) constraintuse_legacy_formatr r r serializehs  ruzkeras.constraints.deserializecCs*|r tj|t|ddSt|t|ddS)Nrs)module_objectscustom_objectsprintable_module_name)rrrglobals)rrwrtr r r deserializexsrzzkeras.constraints.getcCsd|durdSt|trd|v}t||dSt|tr%t|id}t|St|r+|Std|)z&Retrieves a Keras constraint function.Nmodule)rt) class_namerz4Could not interpret constraint function identifier: )rndictrzstrgetcallabler?) identifierrtrr r r rs   r)F)NF)#rrotensorflow.compat.v2rev2r# keras.srcrkeras.src.saving.legacyrrr"keras.src.saving.serialization_librr tensorflow.python.util.tf_exportrtensorflow.tools.docsrrrr-r2r4r:max_normnon_neg unit_norm min_max_normradial_constraintmaxnormnonnegunitnormrurzrr r r r sJ      H) $;Y