2Vhq HddlmZddlmZedGddeZy)) keras_export)Callbackzkeras.callbacks.LambdaCallbackc0eZdZdZ dfd ZxZS)LambdaCallbackac Callback for creating simple, custom callbacks on-the-fly. This callback is constructed with anonymous functions that will be called at the appropriate time (during `Model.{fit | evaluate | predict}`). Note that the callbacks expects positional arguments, as: - `on_epoch_begin` and `on_epoch_end` expect two positional arguments: `epoch`, `logs` - `on_train_begin` and `on_train_end` expect one positional argument: `logs` - `on_train_batch_begin` and `on_train_batch_end` expect two positional arguments: `batch`, `logs` - See `Callback` class definition for the full list of functions and their expected arguments. Args: on_epoch_begin: called at the beginning of every epoch. on_epoch_end: called at the end of every epoch. on_train_begin: called at the beginning of model training. on_train_end: called at the end of model training. on_train_batch_begin: called at the beginning of every train batch. on_train_batch_end: called at the end of every train batch. kwargs: Any function in `Callback` that you want to override by passing `function_name=function`. For example, `LambdaCallback(.., on_train_end=train_end_fn)`. The custom function needs to have same arguments as the ones defined in `Callback`. Example: ```python # Print the batch number at the beginning of every batch. batch_print_callback = LambdaCallback( on_train_batch_begin=lambda batch,logs: print(batch)) # Stream the epoch loss to a file in JSON format. The file content # is not well-formed JSON but rather has a JSON object per line. import json json_log = open('loss_log.json', mode='wt', buffering=1) json_logging_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: json_log.write( json.dumps({'epoch': epoch, 'loss': logs['loss']}) + ' '), on_train_end=lambda logs: json_log.close() ) # Terminate some processes after having finished model training. processes = ... cleanup_callback = LambdaCallback( on_train_end=lambda logs: [ p.terminate() for p in processes if p.is_alive()]) model.fit(..., callbacks=[batch_print_callback, json_logging_callback, cleanup_callback]) ``` c t||jj||||_|||_|||_|||_|||_|||_ yy)N) super__init____dict__updateon_epoch_begin on_epoch_endon_train_begin on_train_endon_train_batch_beginon_train_batch_end) selfr r rrrrkwargs __class__s S/home/dcms/DCMS/lib/python3.12/site-packages/keras/src/callbacks/lambda_callback.pyr zLambdaCallback.__init__@s{  V$  %"0D   # ,D   %"0D   # ,D   +(rs/-1./Q9XQ90Q9r