tff.learning.Model

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Represents a model for use in TensorFlow Federated.

Used in the notebooks

Used in the tutorials
Federated Learning for Image Classification

Each Model will work on a set of tf.Variables, and each method should be a computation that can be implemented as a tf.function; this implies the class should essentially be stateless from a Python perspective, as each method will generally only be traced once (per set of arguments) to create the corresponding TensorFlow graph functions. Thus, Model instances should behave as expected in both eager and graph (TF 1.0) usage.

In general, tf.Variables may be either:

• Weights, the variables needed to make predictions with the model.
• Local variables, e.g. to accumulate aggregated metrics across calls to forward_pass.

The weights can be broken down into trainable variables (variables that can and should be trained using gradient-based methods), and non-trainable variables (which could include fixed pre-trained layers, or static model data). These variables are provided via the trainable_variables, non_trainable_variables, and local_variables properties, and must be initialized by the user of the Model.

In federated learning, model weights will generally be provided by the server, and updates to trainable model variables will be sent back to the server. Local variables are not transmitted, and are instead initialized locally on the device, and then used to produce aggregated_outputs which are sent to the server.

All tf.Variables should be introduced in __init__; this could move to a build method more inline with Keras (see https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) in the future.

Attributes
input_spec
local_variables	An iterable of tf.Variable objects, see class comment for details.
non_trainable_variables	An iterable of tf.Variable objects, see class comment for details.
trainable_variables	An iterable of tf.Variable objects, see class comment for details.

Methods

forward_pass

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@abc.abstractmethod
forward_pass(
    batch_input, training=True
) -> tff.learning.BatchOutput

Runs the forward pass and returns results.

This method must be serializable in a tff.tf_computation or other backend decorator. Any pure-Python or unserializable logic will not be runnable in the federated system.

This method should not modify any variables that are part of the model parameters, that is, variables that influence the predictions (exceptions being updated, rather than learned, parameters such as BatchNorm means and variances). Rather, this is done by the training loop. However, this method may update aggregated metrics computed across calls to forward_pass; the final values of such metrics can be accessed via aggregated_outputs.

Uses in TFF
To implement model evaluation. To implement federated gradient descent and other non-Federated-Averaging algorithms, where we want the model to run the forward pass and update metrics, but there is no optimizer (we might only compute gradients on the returned loss). To implement Federated Averaging.

Args
batch_input	A nested structure that matches the structure of Model.input_spec and each tensor in batch_input satisfies tf.TensorSpec.is_compatible_with() for the corresponding tf.TensorSpec in Model.input_spec.
training	If True, run the training forward pass, otherwise, run in evaluation mode. The semantics are generally the same as the training argument to keras.Model.call; this might e.g. influence how dropout or batch normalization is handled.

Returns
A BatchOutput object. The object must include the loss tensor if the model will be trained via a gradient-based algorithm.

metric_finalizers

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@abc.abstractmethod
metric_finalizers() -> tff.learning.MetricFinalizersType

Creates an collections.OrderedDict of metric names to finalizers.

This method and the report_local_unfinalized_metrics() method should have the same keys (i.e., metric names). A finalizer returned by this method is a function (typically a tf.function decorated callable or a tff.tf_computation decoreated TFF Computation) that takes in a metric's unfinalized values (returned by report_local_unfinalized_metrics()), and returns the finalized metric values.

This method and the report_local_unfinalized_metrics() method will be used together to build a cross-client metrics aggregator. See the documentation of report_local_unfinalized_metrics() for more information.

Returns

An collections.OrderedDict of metric names to finalizers. The metric names must be the same as those from the report_local_unfinalized_metrics() method. A finalizer is a tf.function (or tff.tf_computation) decorated callable that takes in a metric's unfinalized values, and returns the finalized values. This method and the report_local_unfinalized_metrics() method will be used together to build a cross-client metrics aggregator in federated training processes or evaluation computations.

predict_on_batch

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@abc.abstractmethod
predict_on_batch(
    batch_input, training=True
)

report_local_unfinalized_metrics

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@abc.abstractmethod
report_local_unfinalized_metrics() -> collections.OrderedDict[str, Any]

Creates an collections.OrderedDict of metric names to unfinalized values.

For a metric, its unfinalized values are given as a structure (typically a list) of tensors representing values from aggregating over all previous forward_pass calls, unless the reset_metrics is called. Each time the reset_metrics is called, the local metric variables will be reset, and report_local_unfinalized_metrics only reports metrics aggregated from the forward_pass calls since the last reset_metrics call. For a Keras metric, its unfinalized values are typically the tensor values of its state variables. In general, the tensors can be an arbitrary function of all the tf.Variables of this model.

The metric names returned by this method should be the same as those expected by the metric_finalizers(); one should be able to use the unfinalized values as input to the finalizers to get the finalized values. Taking tf.keras.metrics.CategoricalAccuracy as an example, its unfinalized values can be a list of two tensors (from its state variables): total and count, and the finalizer function performs a tf.math.divide_no_nan.

In federated learning, this method returns the local results from clients, which will typically be further aggregated across clients and made available on the server. This method and the metric_finalizers() method will be used together to build a cross-client metrics aggregator. For example, a simple "sum_then_finalize" aggregator will first sum the unfinalized metric values from clients, and then call the finalizer functions at the server.

Because both of this method and the metric_finalizers() method are defined in a per-metric manner, users have the flexiblity to call finalizer at the clients or at the server for different metrics. Users also have the freedom to defined a cross-client metrics aggregator that aggregates a single metric in multiple ways.

Returns

An collections.OrderedDict of metric names to unfinalized values. The metric names must be the same as those expected by the metric_finalizers() method. One should be able to use the unfinalized metric values (returned by this method) as the input to the finalizers (returned by metric_finalizers()) to get the finalized metrics. This method and the metric_finalizers() method will be used together to build a cross-client metrics aggregator when defining the federated training processes or evaluation computations.

reset_metrics

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@abc.abstractmethod
reset_metrics() -> None

Resets metrics variables to initial value.

This method is a tf.function. It is used to reset the metrics variables between different stages in client's local computation. Each time the reset_metrics is called, the local metric variables will be reset, and report_local_unfinalized_metrics only reports metrics aggregated from the forward_pass calls since the last reset_metrics call. If the reset_metrics is never called, report_local_unfinalized_metrics will report metrics aggregated over all previous forward_pass calls.