tf.keras.callbacks.LambdaCallback

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Callback for creating simple, custom callbacks on-the-fly.

Inherits From: Callback

tf.keras.callbacks.LambdaCallback( on_epoch_begin=None, on_epoch_end=None, on_train_begin=None, on_train_end=None, on_train_batch_begin=None, on_train_batch_end=None, **kwargs ) 

Used in the notebooks

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]) ``` 

Methods

on_batch_begin

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on_batch_begin( batch, logs=None ) 

A backwards compatibility alias for on_train_batch_begin.

on_batch_end

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on_batch_end( batch, logs=None ) 

A backwards compatibility alias for on_train_batch_end.

on_epoch_begin

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on_epoch_begin( epoch, logs=None ) 

Called at the start of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

on_epoch_end

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on_epoch_end( epoch, logs=None ) 

Called at the end of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

on_predict_batch_begin

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on_predict_batch_begin( batch, logs=None ) 

Called 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.

on_predict_batch_end

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on_predict_batch_end( batch, logs=None ) 

Called 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.

on_predict_begin

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on_predict_begin( logs=None ) 

Called at the beginning of prediction.

Subclasses should override for any actions to run.

on_predict_end

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on_predict_end( logs=None ) 

Called at the end of prediction.

Subclasses should override for any actions to run.

on_test_batch_begin

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on_test_batch_begin( batch, logs=None ) 

Called 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.

on_test_batch_end

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on_test_batch_end( batch, logs=None ) 

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.

on_test_begin

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on_test_begin( logs=None ) 

Called at the beginning of evaluation or validation.

Subclasses should override for any actions to run.

on_test_end

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on_test_end( logs=None ) 

Called at the end of evaluation or validation.

Subclasses should override for any actions to run.

on_train_batch_begin

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on_train_batch_begin( batch, logs=None ) 

Called 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.

on_train_batch_end

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on_train_batch_end( batch, logs=None ) 

Called 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.

on_train_begin

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on_train_begin( logs=None ) 

Called at the beginning of training.

Subclasses should override for any actions to run.

on_train_end

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on_train_end( logs=None ) 

Called at the end of training.

Subclasses should override for any actions to run.

set_model

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set_model( model ) 

set_params

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set_params( params )