Python tf.keras.callbacks.LambdaCallback用法及代码示例

即时创建简单的自定义回调的回调。

继承自：Callback

用法

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

此回调由匿名函数构造，这些函数将在适当的时间(在 Model.{fit | evaluate | predict} 期间)调用。请注意，回调需要位置参数，如：

• on_epoch_begin 和 on_epoch_end 需要两个位置参数：epoch , logs
• on_batch_begin 和 on_batch_end 需要两个位置参数：batch , logs
• on_train_begin 和 on_train_end 需要一个位置参数：logs

例子：

# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
    on_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']}) + '\n'),
    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])