o 2hO*@s>ddlmZddlmZddlmZedGdddZdS))backend)utils) keras_exportzkeras.callbacks.Callbackc@s4eZdZdZddZddZddZedd Ze j d+d d Z e j d+d dZ e j d+ddZ e j d+ddZe j d+ddZe j d+ddZe j d+ddZe j d+ddZe j d+ddZe j d+ddZe j d+dd Ze j d+d!d"Ze j d+d#d$Ze j d+d%d&Ze j d+d'd(Ze j d+d)d*Zd S),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). cCsd|_d|_dSN)params_model)selfr W/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/callbacks/callback.py__init__Cs zCallback.__init__cC ||_dSr)r)r rr r r set_paramsG zCallback.set_paramscCr r)r)r modelr r r set_modelJrzCallback.set_modelcCsTtdkrddlm}t|j|r|jjStdkr't|jdr'|j|jS)Ntorchr)DistributedDataParalleljaxjax_state_sync)rtorch.nn.parallelr isinstancermodulehasattrr)r rr r r rMs    zCallback.modelNcCdS)z;A backwards compatibility alias for `on_train_batch_begin`.Nr r batchlogsr r r on_batch_beginfzCallback.on_batch_begincCr)z9A backwards compatibility alias for `on_train_batch_end`.Nr rr r r on_batch_endjrzCallback.on_batch_endcCr)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. Nr r epochrr r r on_epoch_beginnrzCallback.on_epoch_begincCr)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}`. Nr r!r r r on_epoch_end{rzCallback.on_epoch_endcC|j||ddS)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. rN)rrr r r on_train_batch_beginszCallback.on_train_batch_begincCr%)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 rr r r on_train_batch_endszCallback.on_train_batch_endcCr)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. Nr rr r r on_test_batch_beginrzCallback.on_test_batch_begincCr)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. Nr rr r r on_test_batch_endrzCallback.on_test_batch_endcCr)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. Nr rr r r on_predict_batch_beginrzCallback.on_predict_batch_begincCr)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. Nr rr r r on_predict_batch_endrzCallback.on_predict_batch_endcCr)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. Nr r rr r r on_train_beginrzCallback.on_train_begincCr)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. Nr r-r r r on_train_endrzCallback.on_train_endcCr)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. Nr r-r r r on_test_begin rzCallback.on_test_begincCr)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. Nr r-r r r on_test_endrzCallback.on_test_endcCr)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. Nr r-r r r on_predict_begin rzCallback.on_predict_begincCr)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. Nr r-r r r on_predict_end+rzCallback.on_predict_endr)__name__ __module__ __qualname____doc__r rrpropertyrrdefaultrr r#r$r'r(r)r*r+r,r.r/r0r1r2r3r r r r rsN;                 rN) keras.srcrrkeras.src.api_exportrrr r r r s