tf.keras.experimental.SidecarEvaluator

Deprecated. Please use tf.keras.utils.SidecarEvaluator instead.

Inherits From: SidecarEvaluator

tf.keras.experimental.SidecarEvaluator(
    *args, **kwargs
)

model Model to use for evaluation. The model object used here should be a tf.keras.Model, and should be the same as the one that is used in training, where tf.keras.Models are checkpointed. The model should have one or more metrics compiled before using SidecarEvaluator.
data The input data for evaluation. SidecarEvaluator supports all data types that Keras model.evaluate supports as the input data x, such as a tf.data.Dataset.
checkpoint_dir Directory where checkpoint files are saved.
steps Number of steps to perform evaluation for, when evaluating a single checkpoint file. If None, evaluation continues until the dataset is exhausted. For repeated evaluation dataset, user must specify steps to avoid infinite evaluation loop.
max_evaluations Maximum number of the checkpoint file to be evaluated, for SidecarEvaluator to know when to stop. The evaluator will stop after it evaluates a checkpoint filepath ending with '-'. If using tf.train.CheckpointManager.save for saving checkpoints, the kth saved checkpoint has the filepath suffix '-' (k=1 for the first saved), and if checkpoints are saved every epoch after training, the filepath saved at the kth epoch would end with '-. Thus, if training runs for n epochs, and the evaluator should end after the training finishes, use n for this parameter. Note that this is not necessarily equal to the number of total evaluations, since some checkpoints may be skipped if evaluation is slower than checkpoint creation. If None, SidecarEvaluator will evaluate indefinitely, and the user must terminate evaluator program themselves.
callbacks List of keras.callbacks.Callback instances to apply during evaluation. See callbacks.

Methods

start

View source

start()

Starts the evaluation loop.