2VhO*RddlmZddlmZddlmZedGddZy))backend)utils) keras_exportzkeras.callbacks.CallbackceZdZdZdZdZdZedZe jddZ e jddZ e jdd Z e jdd Ze jdd Ze jdd Ze jdd Ze jddZe jddZe jddZe jddZe jddZe jddZe jddZe jddZe jddZy)Callbacka/Base class used to build new callbacks. Callbacks can be passed to keras methods such as `fit()`, `evaluate()`, and `predict()` in order to hook into the various stages of the model training, evaluation, and inference lifecycle. To create a custom callback, subclass `keras.callbacks.Callback` and override the method associated with the stage of interest. Example: >>> training_finished = False >>> class MyCallback(Callback): ... def on_train_end(self, logs=None): ... global training_finished ... training_finished = True >>> model = Sequential([ ... layers.Dense(1, input_shape=(1,))]) >>> model.compile(loss='mean_squared_error') >>> model.fit(np.array([[1.0]]), np.array([[1.0]]), ... callbacks=[MyCallback()]) >>> assert training_finished == True If you want to use `Callback` objects in a custom training loop: 1. You should pack all your callbacks into a single `callbacks.CallbackList` so they can all be called together. 2. You will need to manually call all the `on_*` methods at the appropriate locations in your loop. Like this: Example: ```python callbacks = keras.callbacks.CallbackList([...]) callbacks.append(...) callbacks.on_train_begin(...) for epoch in range(EPOCHS): callbacks.on_epoch_begin(epoch) for i, data in dataset.enumerate(): callbacks.on_train_batch_begin(i) batch_logs = model.train_step(data) callbacks.on_train_batch_end(i, batch_logs) epoch_logs = ... callbacks.on_epoch_end(epoch, epoch_logs) final_logs=... callbacks.on_train_end(final_logs) ``` Attributes: params: Dict. Training parameters (eg. verbosity, batch size, number of epochs...). model: Instance of `Model`. Reference of the model being trained. The `logs` dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch (see method-specific docstrings). c d|_d|_yN)params_model)selfs L/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/callbacks/callback.py__init__zCallback.__init__Cs  c||_yr )r )r r s r set_paramszCallback.set_paramsGs  rc||_yr )r )r models r set_modelzCallback.set_modelJs  rc:tjdk(r2ddlm}t|j|r|jj Stjdk(r0t |jdr|jj|jS)Ntorchr)DistributedDataParalleljaxjax_state_sync)rtorch.nn.parallelr isinstancer modulehasattrr)r rs r rzCallback.modelMsq ??  ' A$++'>?{{))) ??  %' KK)+  KK & & ({{rNcy)z;A backwards compatibility alias for `on_train_batch_begin`.Nr batchlogss r on_batch_beginzCallback.on_batch_beginfrcy)z9A backwards compatibility alias for `on_train_batch_end`.Nrr s r on_batch_endzCallback.on_batch_endjr$rcy)aeCalled at the start of an epoch. Subclasses should override for any actions to run. This function should only be called during TRAIN mode. Args: epoch: Integer, index of epoch. logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. Nrr epochr"s r on_epoch_beginzCallback.on_epoch_beginnr$rcy)a0Called at the end of an epoch. Subclasses should override for any actions to run. This function should only be called during TRAIN mode. Args: epoch: Integer, index of epoch. logs: Dict, metric results for this training epoch, and for the validation epoch if validation is performed. Validation result keys are prefixed with `val_`. For training epoch, the values of the `Model`'s metrics are returned. Example: `{'loss': 0.2, 'accuracy': 0.7}`. Nrr(s r on_epoch_endzCallback.on_epoch_end{r$rc*|j||y)aCalled at the beginning of a training batch in `fit` methods. Subclasses should override for any actions to run. Note that if the `steps_per_execution` argument to `compile` in `Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. r"N)r#r s r on_train_batch_beginzCallback.on_train_batch_begins E-rc*|j||y)aCalled at the end of a training batch in `fit` methods. Subclasses should override for any actions to run. Note that if the `steps_per_execution` argument to `compile` in `Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. r.N)r&r s r on_train_batch_endzCallback.on_train_batch_ends %d+rcy)auCalled at the beginning of a batch in `evaluate` methods. Also called at the beginning of a validation batch in the `fit` methods, if validation data is provided. Subclasses should override for any actions to run. Note that if the `steps_per_execution` argument to `compile` in `Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. Nrr s r on_test_batch_beginzCallback.on_test_batch_beginr$rcy)a*Called at the end of a batch in `evaluate` methods. Also called at the end of a validation batch in the `fit` methods, if validation data is provided. Subclasses should override for any actions to run. Note that if the `steps_per_execution` argument to `compile` in `Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. Nrr s r on_test_batch_endzCallback.on_test_batch_endr$rcy)aCalled at the beginning of a batch in `predict` methods. Subclasses should override for any actions to run. Note that if the `steps_per_execution` argument to `compile` in `Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. Nrr s r on_predict_batch_beginzCallback.on_predict_batch_beginr$rcy)aCalled at the end of a batch in `predict` methods. Subclasses should override for any actions to run. Note that if the `steps_per_execution` argument to `compile` in `Model` is set to `N`, this method will only be called every `N` batches. Args: batch: Integer, index of batch within the current epoch. logs: Dict. Aggregated metric results up until this batch. Nrr s r on_predict_batch_endzCallback.on_predict_batch_endr$rcy)zCalled at the beginning of training. Subclasses should override for any actions to run. Args: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. Nrr r"s r on_train_beginzCallback.on_train_beginr$rcy)a.Called at the end of training. Subclasses should override for any actions to run. Args: logs: Dict. Currently the output of the last call to `on_epoch_end()` is passed to this argument for this method but that may change in the future. Nrr;s r on_train_endzCallback.on_train_endr$rcy)aCalled at the beginning of evaluation or validation. Subclasses should override for any actions to run. Args: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. Nrr;s r on_test_beginzCallback.on_test_begin r$rcy)aCCalled at the end of evaluation or validation. Subclasses should override for any actions to run. Args: logs: Dict. Currently the output of the last call to `on_test_batch_end()` is passed to this argument for this method but that may change in the future. Nrr;s r on_test_endzCallback.on_test_endr$rcy)aCalled at the beginning of prediction. Subclasses should override for any actions to run. Args: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. Nrr;s r on_predict_beginzCallback.on_predict_begin r$rcy)zCalled at the end of prediction. Subclasses should override for any actions to run. Args: logs: Dict. Currently no data is passed to this argument for this method but that may change in the future. Nrr;s r on_predict_endzCallback.on_predict_end+r$rr )__name__ __module__ __qualname____doc__rrrpropertyrrdefaultr#r&r*r,r/r1r3r5r7r9r<r>r@rBrDrFrrr rrs9v0 ]]JJ ]]HH ]]     ]]     ]].." ]],,  ]]  $ ]]  " ]]     ]]     ]]   ]]     ]]   ]]     ]]   ]]  rrN) keras.srcrrkeras.src.api_exportrrrrr rOs0-()m m *m r