§ é&Vff&ãóZ—ddlmZddlmZed¦«Gd„d¦«¦«ZdS)é)Úbackend)Ú keras_exportzkeras.callbacks.Callbackcóº—eZdZdZd„Zd„Zd„Zed„¦«Zdd„Z dd„Z dd „Z dd „Z dd „Z dd „Zdd „Zdd„Zdd„Zdd„Zdd„Zdd„Zdd„Zdd„Zdd„Zdd„ZdS)ÚCallbacka/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). có"—d|_d|_dS©N)Úvalidation_dataÚ_model©Úselfs úY/var/www/html/software/conda/lib/python3.11/site-packages/keras/src/callbacks/callback.pyÚ__init__zCallback.__init__Bs€Ø#ˆÔ؈Œ ˆ ˆ ócó—||_dSr)Úparams)r rs r Ú set_paramszCallback.set_paramsFs €ØˆŒ ˆ ˆ rcó—||_dSr)r )r Úmodels r Ú set_modelzCallback.set_modelIs €ØˆŒ ˆ ˆ rcóš—tj¦«dkr.t|jd¦«r|j ¦«|jS)NÚjaxÚjax_state_sync)rÚhasattrr rr s r rzCallback.modelLsJ€å Œ?Ñ Ô  Ò %Ð %­'Ø ŒKÐ)ñ+ ô+ Ð %ð ŒK× &Ò &Ñ (Ô (Ð (ØŒ{ÐrNcó—dS)z;A backwards compatibility alias for `on_train_batch_begin`.N©©r ÚbatchÚlogss r Úon_batch_beginzCallback.on_batch_beginXó€€€rcó—dS)z9A backwards compatibility alias for `on_train_batch_end`.Nrrs r Ú on_batch_endzCallback.on_batch_end[r rcó—dS)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 Úepochrs r Úon_epoch_beginzCallback.on_epoch_begin^r rcó—dS)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 Ú on_epoch_endzCallback.on_epoch_endjr rcó4—| ||¬¦«dS)aÿCalled 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)rrs r Úon_train_batch_beginzCallback.on_train_batch_beginys#€ð ×Ò˜E¨ÐÑ-Ô-Ð-Ð-Ð-rcó4—| ||¬¦«dS)aºCalled 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"rs r Úon_train_batch_endzCallback.on_train_batch_endŠs#€ð ×Ò˜% dÐÑ+Ô+Ð+Ð+Ð+rcó—dS)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. Nrrs r Úon_test_batch_beginzCallback.on_test_batch_beginšr rcó—dS)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. Nrrs r Úon_test_batch_endzCallback.on_test_batch_end¬r rcó—dS)aúCalled 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. Nrrs r Úon_predict_batch_beginzCallback.on_predict_batch_begin½r rcó—dS)aµCalled 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. Nrrs r Úon_predict_batch_endzCallback.on_predict_batch_endÌr rcó—dS)zþCalled 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 rs r Úon_train_beginzCallback.on_train_beginÚr rcó—dS)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. Nrr7s r Ú on_train_endzCallback.on_train_endär rcó—dS)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. Nrr7s r Ú on_test_beginzCallback.on_test_beginïr rcó—dS)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. Nrr7s r Ú on_test_endzCallback.on_test_endùr rcó—dS)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. Nrr7s r Úon_predict_beginzCallback.on_predict_beginr rcó—dS)zúCalled 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. Nrr7s r Úon_predict_endzCallback.on_predict_endr rr)Ú__name__Ú __module__Ú __qualname__Ú__doc__rrrÚpropertyrrr"r&r(r+r-r/r1r3r5r8r:r<r>r@rBrrr rrs¯€€€€€ð9ð9ðvðððððððððð ð ñ„Xð ðJðJðJðJðHðHðHðHð  ð  ð  ð  ð  ð  ð  ð  ð.ð.ð.ð.ð",ð,ð,ð,ð  ð ð ð ð$ ð ð ð ð"  ð  ð  ð  ð  ð  ð  ð  ð ð ð ð ð  ð  ð  ð  ð ð ð ð ð  ð  ð  ð  ð ð ð ð ð ð ð ð ð ð rrN)Ú keras.srcrÚkeras.src.api_exportrrrrr úrJs}ðØÐÐÐÐÐØ-Ð-Ð-Ð-Ð-Ð-ð€Ð(Ñ)Ô)ðP ðP ðP ðP ðP ñP ôP ñ*Ô)ðP ðP ðP r