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Builds a tff.Computation for evaluating a reconstruction Model.

Used in the notebooks

Used in the tutorials

The returned computation proceeds in two stages: (1) reconstruction and (2) evaluation. During the reconstruction stage, local variables are reconstructed by freezing global variables and training using reconstruction_optimizer_fn. During the evaluation stage, the reconstructed local variables and global variables are evaluated using the provided loss_fn and metrics_fn.

Usage of returned computation: eval_comp = build_federated_evaluation(...) metrics = eval_comp(tff.learning.reconstruction.get_global_variables(model), federated_data)

model_fn A no-arg function that returns a tff.learning.reconstruction.Model. This method must not capture Tensorflow tensors or variables and use them. Must be constructed entirely from scratch on each invocation, returning the same pre-constructed model each call will result in an error.
loss_fn A no-arg function returning a tf.keras.losses.Loss to use to reconstruct and evaluate the model. The loss will be applied to the model's outputs during the evaluation stage. The final loss metric is the example-weighted mean loss across batches (and across clients).
metrics_fn A no-arg function returning a list of tf.keras.metrics.Metrics to evaluate the model. The metrics will be applied to the model's outputs during the evaluation stage. Final metric values are the example-weighted mean of metric values across batches (and across clients). If None, no metrics are applied.
reconstruction_optimizer_fn A no-arg function that returns a tf.keras.optimizers.Optimizer used to reconstruct the local variables with the global ones frozen.
dataset_split_fn A tff.learning.reconstruction.DatasetSplitFn taking in a single TF dataset and producing two TF datasets. The first is iterated over during reconstruction, and the second is iterated over during evaluation. This can be used to preprocess datasets to e.g. iterate over them for multiple epochs or use disjoint data for reconstruction and evaluation. If None, split client data in half for each user, using one half for reconstruction and the other for evaluation. See tff.learning.reconstruction.build_dataset_split_fn for options.
broadcast_process A tff.templates.MeasuredProcess that broadcasts the model weights on the server to the clients. It must support the signature (input_values@SERVER -> output_values@CLIENT) and have empty state. If set to default None, the server model is broadcast to the clients using the default tff.federated_broadcast.

TypeError if broadcast_process does not have the expected signature or has non-empty state.

A tff.Computation that accepts global model parameters and federated data and returns example-weighted evaluation loss and metrics.