Returns default EvalSharedModel.

Used in the notebooks

Used in the tutorials

eval_saved_model_path Path to EvalSavedModel.
add_metrics_callbacks Optional list of callbacks for adding additional metrics to the graph (see EvalSharedModel for more information on how to configure additional metrics). Metrics for example count and example weights will be added automatically.
include_default_metrics True to include the default metrics that are part of the saved model graph during evaluation. Note that eval_config.options.include_default_metrics must also be true.
example_weight_key Example weight key (single-output model) or dict of example weight keys (multi-output model) keyed by output name.
additional_fetches Prefixes of additional tensors stored in signature_def.inputs that should be fetched at prediction time. The "features" and "labels" tensors are handled automatically and should not be included.
blacklist_feature_fetches List of tensor names in the features dictionary which should be excluded from the fetches request. This is useful in scenarios where features are large (e.g. images) and can lead to excessive memory use if stored.
tags Model tags (e.g. 'serve' for serving or 'eval' for EvalSavedModel).
model_name Optional name of the model being created (should match The name should only be provided if multiple models are being evaluated.
eval_config Eval config. Only used for setting default tags.
custom_model_loader Optional custom model loader for non-TF models.