tff.learning.models.FunctionalModel

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A model that parameterizes forward pass by model weights.

tff.learning.models.FunctionalModel(
    *,
    initial_weights: ModelWeights,
    forward_pass_fn: Callable[[ModelWeights, Any, bool], variable.BatchOutput],
    predict_on_batch_fn: Callable[[ModelWeights, Any, bool], Any],
    loss_fn: Callable[[Any, Any, Any], Any],
    metrics_fns: tuple[InitializeMetricsStateFn, UpdateMetricsStateFn, FinalizeMetricsFn] = (empty_metrics_state, noop_update_metrics, noop_finalize_metrics),
    input_spec: Any
)

initial_weights A 2-tuple (trainable, non_trainable) where the two elements are sequences of weights. Weights must be values convertable to tf.Tensor (e.g. numpy.ndarray, Python sequences, etc), but not tf.Tensor values.
forward_pass_fn A tf.function decorated callable that takes three arguments, model_weights the same structure as initial_weights, batch_input a nested structure of tensors matching input_spec, and training a boolean determinig whether the call is during a training pass (e.g. for Dropout, BatchNormalization, etc).
predict_on_batch_fn A tf.function decorated callable that takes three arguments, model_weights the same structure as initial_weights, x the first element of batch_input (or input_spec), and training a boolean determinig whether the call is during a training pass (e.g. for Dropout, BatchNormalization, etc).
loss_fn A callable that takes three arguments, output tensor(s) as output of predict_on_batch that is interpretable by the loss function, label the second element of batch_input, and optional sample_weight that weights the output.
metrics_fns A 3-tuple of callables that initialize the metrics state, update the metrics state, and finalize the metrics values respectively. This can be the result of tff.learning.metrics.create_functional_metric_fnsor custom user written callables.
input_spec A 2-tuple of (x, y) where each element is a nested structure of tf.TensorSpec that defines the shape and dtypes of batch_input to forward_pass_fn. x corresponds to batched model inputs and y corresponds to batched labels for those inputs.

initial_weights

input_spec

Methods

finalize_metrics

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@tf.function
finalize_metrics(
    state: types.MetricsState
) -> collections.OrderedDict[str, Any]

forward_pass

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@tf.function
forward_pass(
    model_weights: ModelWeights, batch_input: Any, training: bool = True
) -> tff.learning.BatchOutput

Runs the forward pass and returns results.

initialize_metrics_state

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@tf.function
initialize_metrics_state() -> types.MetricsState

loss

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loss(
    output: Any, label: Any, sample_weight: Optional[Any] = None
) -> float

Returns the loss value based on the model output and the label.

predict_on_batch

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@tf.function
predict_on_batch(
    model_weights: ModelWeights, x: Any, training: bool = True
)

Returns tensor(s) interpretable by the loss function.

update_metrics_state

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@tf.function
update_metrics_state(
    state: GenericMetricsState,
    labels: Any,
    batch_output: tff.learning.BatchOutput,
    sample_weight: Optional[Any] = None
) -> GenericMetricsState