BVh,*dZddlmZddlmZddlmZddlmZddlm Z ddlm Z ddlm Z dd lm Z dd l mZGd d ZGd deZGddeZGddeZGddeZGddeZeZeZeZeZeZeZeZeZdZddZdZy)z@Constraints: functions that impose constraints on weight values.) tensor_shape)backend)deserialize_keras_objectserialize_keras_object) array_ops)array_ops_stack)math_ops) while_loop) doc_controlsceZdZdZdZdZy) ConstraintaKBase 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 S/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/keras/constraints.py__call__zConstraint.__call__6s  HciS)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. rrs r get_configzConstraint.get_configEs  IrN)__name__ __module__ __qualname____doc__rrrrrrrs.   rrc`eZdZdZddZej dZej dZy)MaxNorma/MaxNorm 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$s r__init__zMaxNorm.__init__isDNDIrctjtjtj||j d}tj |d|j}||tj|zz zS)NTr$keepdimsr) rsqrtr reduce_sumsquarer$clipr#epsilonrrnormsdesireds rrzMaxNorm.__call__msb LLHOOA.TYYN PEll5!T^^4G 7??,u45 66rc4|j|jdS)Nr"r"rs rrzMaxNorm.get_configts ;;rN)r rrrrr%r do_not_generate_docsrrrrrrrQs@.$$7%7 $$<%