o ·2úhƒ ã@s4ddlmZddlmZedƒGdd„deƒƒZdS)é)Ú keras_export)ÚCallbackzkeras.callbacks.LambdaCallbackcs.eZdZdZ      d‡fdd„ Z‡ZS)ÚLambdaCallbackau 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 a positional argument `batch` and a keyword argument `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]) ``` Nc srtƒ ¡|j |¡|dur||_|dur||_|dur ||_|dur'||_|dur.||_|dur7||_ dSdS)N) ÚsuperÚ__init__Ú__dict__ÚupdateÚon_epoch_beginÚ on_epoch_endÚon_train_beginÚ on_train_endÚon_train_batch_beginÚon_train_batch_end)Úselfr r r r r rÚkwargs©Ú __class__©ú^/var/www/html/chatgem/venv/lib/python3.10/site-packages/keras/src/callbacks/lambda_callback.pyr@s  ÿzLambdaCallback.__init__)NNNNNN)Ú__name__Ú __module__Ú __qualname__Ú__doc__rÚ __classcell__rrrrrs;ùrN)Úkeras.src.api_exportrÚkeras.src.callbacks.callbackrrrrrrÚs