3672ba47ca
* Add LambdaCallback * Add Lambda Callback * Add Lambda Callback * Rename lambda_callback_test.py
88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
from keras_core.api_export import keras_core_export
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from keras_core.callbacks.callback import Callback
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@keras_core_export("keras_core.callbacks.LambdaCallback")
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class LambdaCallback(Callback):
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"""Callback for creating simple, custom callbacks on-the-fly.
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This callback is constructed with anonymous functions that will be called
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at the appropriate time (during `Model.{fit | evaluate | predict}`).
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Note that the callbacks expects positional arguments, as:
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- `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
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`epoch`, `logs`
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- `on_train_begin` and `on_train_end` expect one positional argument:
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`logs`
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- `on_train_batch_begin` and `on_train_batch_end` expect two positional
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arguments: `batch`, `logs`
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- See `Callback` class definition for the full list of functions and their
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expected arguments.
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Args:
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on_epoch_begin: called at the beginning of every epoch.
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on_epoch_end: called at the end of every epoch.
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on_train_begin: called at the beginning of model training.
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on_train_end: called at the end of model training.
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on_train_batch_begin: called at the beginning of every train batch.
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on_train_batch_end: called at the end of every train batch.
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kwargs: Any function in `Callback` that you want to override by
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passing `function_name=function`. For example,
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`LambdaCallback(.., on_train_end=train_end_fn)`. The custom function
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needs to have same arguments as the ones defined in `Callback`.
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Example:
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```python
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# Print the batch number at the beginning of every batch.
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batch_print_callback = LambdaCallback(
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on_train_batch_begin=lambda batch,logs: print(batch))
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# Stream the epoch loss to a file in JSON format. The file content
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# is not well-formed JSON but rather has a JSON object per line.
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import json
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json_log = open('loss_log.json', mode='wt', buffering=1)
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json_logging_callback = LambdaCallback(
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on_epoch_end=lambda epoch, logs: json_log.write(
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json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
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on_train_end=lambda logs: json_log.close()
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)
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# Terminate some processes after having finished model training.
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processes = ...
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cleanup_callback = LambdaCallback(
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on_train_end=lambda logs: [
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p.terminate() for p in processes if p.is_alive()])
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model.fit(...,
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callbacks=[batch_print_callback,
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json_logging_callback,
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cleanup_callback])
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```
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"""
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def __init__(
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self,
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on_epoch_begin=None,
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on_epoch_end=None,
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on_train_begin=None,
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on_train_end=None,
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on_train_batch_begin=None,
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on_train_batch_end=None,
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**kwargs,
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):
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super().__init__()
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self.__dict__.update(kwargs)
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if on_epoch_begin is not None:
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self.on_epoch_begin = on_epoch_begin
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if on_epoch_end is not None:
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self.on_epoch_end = on_epoch_end
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if on_train_begin is not None:
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self.on_train_begin = on_train_begin
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if on_train_end is not None:
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self.on_train_end = on_train_end
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if on_train_batch_begin is not None:
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self.on_train_batch_begin = on_train_batch_begin
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if on_train_batch_end is not None:
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self.on_train_batch_end = on_train_batch_end
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