def configure_training(task_spec: training_specs.TaskSpec,
model: str = 'cnn') -> training_specs.RunnerSpec:
"""Configures training for the EMNIST character recognition task.
This method will load and pre-process datasets and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
model: A string specifying the model used for character recognition. Can be
one of `cnn` and `2nn`, corresponding to a CNN model and a densely
connected 2-layer model (respectively).
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
emnist_task = 'digit_recognition'
emnist_train, _ = tff.simulation.datasets.emnist.load_data(only_digits=False)
_, emnist_test = emnist_dataset.get_centralized_datasets(
only_digits=False, emnist_task=emnist_task)
train_preprocess_fn = emnist_dataset.create_preprocess_fn(
num_epochs=task_spec.client_epochs_per_round,
batch_size=task_spec.client_batch_size,
emnist_task=emnist_task)
input_spec = train_preprocess_fn.type_signature.result.element
if model == 'cnn':
model_builder = functools.partial(
emnist_models.create_conv_dropout_model, only_digits=False)
elif model == '2nn':
model_builder = functools.partial(
emnist_models.create_two_hidden_layer_model, only_digits=False)
else:
raise ValueError(
'Cannot handle model flag [{!s}], must be one of {!s}.'.format(
model, EMNIST_MODELS))
loss_builder = tf.keras.losses.SparseCategoricalCrossentropy
metrics_builder = lambda: [tf.keras.metrics.SparseCategoricalAccuracy()]
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(
keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
@tff.tf_computation(tf.string)
def build_train_dataset_from_client_id(client_id):
client_dataset = emnist_train.dataset_computation(client_id)
return train_preprocess_fn(client_dataset)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
build_train_dataset_from_client_id, iterative_process)
client_ids_fn = tff.simulation.build_uniform_sampling_fn(
emnist_train.client_ids,
size=task_spec.clients_per_round,
replace=False,
random_seed=task_spec.client_datasets_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
training_process.get_model_weights = iterative_process.get_model_weights
evaluate_fn = tff.learning.build_federated_evaluation(tff_model_fn)
def test_fn(state):
return evaluate_fn(
iterative_process.get_model_weights(state), [emnist_test])
def validation_fn(state, round_num):
del round_num
return evaluate_fn(
iterative_process.get_model_weights(state), [emnist_test])
return training_specs.RunnerSpec(
iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
def configure_training(
task_spec: training_specs.TaskSpec) -> training_specs.RunnerSpec:
"""Configures training for the EMNIST autoencoder task.
This method will load and pre-process datasets and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
emnist_task = 'autoencoder'
emnist_train, _ = tff.simulation.datasets.emnist.load_data(only_digits=False)
_, emnist_test = emnist_dataset.get_centralized_datasets(
only_digits=False, emnist_task=emnist_task)
train_preprocess_fn = emnist_dataset.create_preprocess_fn(
num_epochs=task_spec.client_epochs_per_round,
batch_size=task_spec.client_batch_size,
emnist_task=emnist_task)
input_spec = train_preprocess_fn.type_signature.result.element
model_builder = emnist_ae_models.create_autoencoder_model
loss_builder = functools.partial(
tf.keras.losses.MeanSquaredError, reduction=tf.keras.losses.Reduction.SUM)
metrics_builder = lambda: [tf.keras.metrics.MeanSquaredError()]
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(
keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
if hasattr(emnist_train, 'dataset_computation'):
@tff.tf_computation(tf.string)
def build_train_dataset_from_client_id(client_id):
client_dataset = emnist_train.dataset_computation(client_id)
return train_preprocess_fn(client_dataset)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
build_train_dataset_from_client_id, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
emnist_train.client_ids,
size=task_spec.clients_per_round,
replace=False,
random_seed=task_spec.sampling_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
else:
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_preprocess_fn, iterative_process)
client_sampling_fn = training_utils.build_client_datasets_fn(
dataset=emnist_train,
clients_per_round=task_spec.clients_per_round,
random_seed=task_spec.sampling_random_seed)
training_process.get_model_weights = iterative_process.get_model_weights
test_fn = training_utils.build_centralized_evaluate_fn(
eval_dataset=emnist_test,
model_builder=model_builder,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
validation_fn = lambda model_weights, round_num: test_fn(model_weights)
return training_specs.RunnerSpec(
iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
def configure_training(
task_spec: training_specs.TaskSpec,
crop_size: int = 24,
distort_train_images: bool = True) -> training_specs.RunnerSpec:
"""Configures training for the CIFAR-100 classification task.
This method will load and pre-process datasets and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
crop_size: An optional integer representing the resulting size of input
images after preprocessing.
distort_train_images: A boolean indicating whether to distort training
images during preprocessing via random crops, as opposed to simply
resizing the image.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
crop_shape = (crop_size, crop_size, 3)
cifar_train, _ = tff.simulation.datasets.cifar100.load_data()
_, cifar_test = cifar100_dataset.get_centralized_datasets(
train_batch_size=task_spec.client_batch_size, crop_shape=crop_shape)
train_preprocess_fn = cifar100_dataset.create_preprocess_fn(
num_epochs=task_spec.client_epochs_per_round,
batch_size=task_spec.client_batch_size,
crop_shape=crop_shape,
distort_image=distort_train_images)
input_spec = train_preprocess_fn.type_signature.result.element
model_builder = functools.partial(resnet_models.create_resnet18,
input_shape=crop_shape,
num_classes=NUM_CLASSES)
loss_builder = tf.keras.losses.SparseCategoricalCrossentropy
metrics_builder = lambda: [tf.keras.metrics.SparseCategoricalAccuracy()]
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
@tff.tf_computation(tf.string)
def build_train_dataset_from_client_id(client_id):
client_dataset = cifar_train.dataset_computation(client_id)
return train_preprocess_fn(client_dataset)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
build_train_dataset_from_client_id, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
cifar_train.client_ids,
size=task_spec.clients_per_round,
replace=False,
random_seed=task_spec.client_datasets_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
training_process.get_model_weights = iterative_process.get_model_weights
centralized_eval_fn = training_utils.build_centralized_evaluate_fn(
eval_dataset=cifar_test,
model_builder=model_builder,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
def test_fn(state):
return centralized_eval_fn(iterative_process.get_model_weights(state))
def validation_fn(state, round_num):
del round_num
return test_fn(state)
return training_specs.RunnerSpec(iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
def configure_training(task_spec: training_specs.TaskSpec,
sequence_length: int = 80) -> training_specs.RunnerSpec:
"""Configures training for the Shakespeare next-character prediction task.
This method will load and pre-process datasets and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
sequence_length: An int specifying the length of the character sequences
used for prediction.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
shakespeare_train, _ = tff.simulation.datasets.shakespeare.load_data()
_, shakespeare_test = shakespeare_dataset.get_centralized_datasets(
sequence_length=sequence_length)
train_preprocess_fn = shakespeare_dataset.create_preprocess_fn(
num_epochs=task_spec.client_epochs_per_round,
batch_size=task_spec.client_batch_size,
sequence_length=sequence_length)
input_spec = train_preprocess_fn.type_signature.result.element
model_builder = functools.partial(create_shakespeare_model,
sequence_length=sequence_length)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
if hasattr(shakespeare_train, 'dataset_computation'):
@tff.tf_computation(tf.string)
def build_train_dataset_from_client_id(client_id):
client_dataset = shakespeare_train.dataset_computation(client_id)
return train_preprocess_fn(client_dataset)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
build_train_dataset_from_client_id, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
shakespeare_train.client_ids,
size=task_spec.clients_per_round,
replace=False,
random_seed=task_spec.client_datasets_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
else:
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_preprocess_fn, iterative_process)
client_sampling_fn = training_utils.build_client_datasets_fn(
dataset=shakespeare_train,
clients_per_round=task_spec.clients_per_round,
random_seed=task_spec.client_datasets_random_seed)
training_process.get_model_weights = iterative_process.get_model_weights
test_fn = training_utils.build_centralized_evaluate_fn(
eval_dataset=shakespeare_test,
model_builder=model_builder,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
validation_fn = lambda model_weights, round_num: test_fn(model_weights)
return training_specs.RunnerSpec(iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
def configure_training(
task_spec: training_specs.TaskSpec,
vocab_size: int = 10000,
num_oov_buckets: int = 1,
sequence_length: int = 20,
max_elements_per_user: int = 1000,
num_validation_examples: int = 10000,
embedding_size: int = 96,
latent_size: int = 670,
num_layers: int = 1,
shared_embedding: bool = False) -> training_specs.RunnerSpec:
"""Configures training for Stack Overflow next-word prediction.
This method will load and pre-process datasets and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
vocab_size: Integer dictating the number of most frequent words to use in
the vocabulary.
num_oov_buckets: The number of out-of-vocabulary buckets to use.
sequence_length: The maximum number of words to take for each sequence.
max_elements_per_user: The maximum number of elements processed for each
client's dataset.
num_validation_examples: The number of test examples to use for validation.
embedding_size: The dimension of the word embedding layer.
latent_size: The dimension of the latent units in the recurrent layers.
num_layers: The number of stacked recurrent layers to use.
shared_embedding: Boolean indicating whether to tie input and output
embeddings.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
model_builder = functools.partial(
stackoverflow_models.create_recurrent_model,
vocab_size=vocab_size,
num_oov_buckets=num_oov_buckets,
embedding_size=embedding_size,
latent_size=latent_size,
num_layers=num_layers,
shared_embedding=shared_embedding)
loss_builder = functools.partial(
tf.keras.losses.SparseCategoricalCrossentropy, from_logits=True)
special_tokens = stackoverflow_word_prediction.get_special_tokens(
vocab_size, num_oov_buckets)
pad_token = special_tokens.pad
oov_tokens = special_tokens.oov
eos_token = special_tokens.eos
def metrics_builder():
return [
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_with_oov',
masked_tokens=[pad_token]),
keras_metrics.MaskedCategoricalAccuracy(name='accuracy_no_oov',
masked_tokens=[pad_token] +
oov_tokens),
# Notice BOS never appears in ground truth.
keras_metrics.MaskedCategoricalAccuracy(
name='accuracy_no_oov_or_eos',
masked_tokens=[pad_token, eos_token] + oov_tokens),
keras_metrics.NumBatchesCounter(),
keras_metrics.NumTokensCounter(masked_tokens=[pad_token])
]
train_clientdata, _, _ = tff.simulation.datasets.stackoverflow.load_data()
# TODO(b/161914546): consider moving evaluation to use
# `tff.learning.build_federated_evaluation` to get metrics over client
# distributions, as well as the example weight means from this centralized
# evaluation.
_, validation_dataset, test_dataset = stackoverflow_word_prediction.get_centralized_datasets(
vocab_size=vocab_size,
max_sequence_length=sequence_length,
num_validation_examples=num_validation_examples,
num_oov_buckets=num_oov_buckets)
train_dataset_preprocess_comp = stackoverflow_word_prediction.create_preprocess_fn(
vocab=stackoverflow_word_prediction.create_vocab(vocab_size),
num_oov_buckets=num_oov_buckets,
client_batch_size=task_spec.client_batch_size,
client_epochs_per_round=task_spec.client_epochs_per_round,
max_sequence_length=sequence_length,
max_elements_per_client=max_elements_per_user)
input_spec = train_dataset_preprocess_comp.type_signature.result.element
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
@tff.tf_computation(tf.string)
def train_dataset_computation(client_id):
client_train_data = train_clientdata.dataset_computation(client_id)
return train_dataset_preprocess_comp(client_train_data)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_dataset_computation, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
train_clientdata.client_ids,
size=task_spec.clients_per_round,
replace=False,
random_seed=task_spec.client_datasets_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
training_process.get_model_weights = iterative_process.get_model_weights
centralized_validation_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
eval_dataset=validation_dataset,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
def validation_fn(server_state, round_num):
del round_num
return centralized_validation_fn(
iterative_process.get_model_weights(server_state))
centralized_test_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
# Use both val and test for symmetry with other experiments, which
# evaluate on the entire test set.
eval_dataset=validation_dataset.concatenate(test_dataset),
loss_builder=loss_builder,
metrics_builder=metrics_builder)
def test_fn(server_state):
return centralized_test_fn(
iterative_process.get_model_weights(server_state))
return training_specs.RunnerSpec(iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
def configure_training(
task_spec: training_specs.TaskSpec,
vocab_tokens_size: int = 10000,
vocab_tags_size: int = 500,
max_elements_per_user: int = 1000,
num_validation_examples: int = 10000) -> training_specs.RunnerSpec:
"""Configures training for the Stack Overflow tag prediction task.
This tag prediction is performed via multi-class one-versus-rest logistic
regression. This method will load and pre-process datasets and construct a
model used for the task. It then uses `iterative_process_builder` to create an
iterative process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
vocab_tokens_size: Integer dictating the number of most frequent words to
use in the vocabulary.
vocab_tags_size: Integer dictating the number of most frequent tags to use
in the label creation.
max_elements_per_user: The maximum number of elements processed for each
client's dataset.
num_validation_examples: The number of test examples to use for validation.
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
stackoverflow_train, _, _ = tff.simulation.datasets.stackoverflow.load_data(
)
_, stackoverflow_validation, stackoverflow_test = stackoverflow_tag_prediction.get_centralized_datasets(
train_batch_size=task_spec.client_batch_size,
word_vocab_size=vocab_tokens_size,
tag_vocab_size=vocab_tags_size,
num_validation_examples=num_validation_examples)
word_vocab = stackoverflow_tag_prediction.create_word_vocab(
vocab_tokens_size)
tag_vocab = stackoverflow_tag_prediction.create_tag_vocab(vocab_tags_size)
train_preprocess_fn = stackoverflow_tag_prediction.create_preprocess_fn(
word_vocab=word_vocab,
tag_vocab=tag_vocab,
client_batch_size=task_spec.client_batch_size,
client_epochs_per_round=task_spec.client_epochs_per_round,
max_elements_per_client=max_elements_per_user)
input_spec = train_preprocess_fn.type_signature.result.element
model_builder = functools.partial(
stackoverflow_lr_models.create_logistic_model,
vocab_tokens_size=vocab_tokens_size,
vocab_tags_size=vocab_tags_size)
loss_builder = functools.partial(tf.keras.losses.BinaryCrossentropy,
from_logits=False,
reduction=tf.keras.losses.Reduction.SUM)
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
@tff.tf_computation(tf.string)
def build_train_dataset_from_client_id(client_id):
client_dataset = stackoverflow_train.dataset_computation(client_id)
return train_preprocess_fn(client_dataset)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
build_train_dataset_from_client_id, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
stackoverflow_train.client_ids,
size=task_spec.clients_per_round,
replace=False,
random_seed=task_spec.client_datasets_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
training_process.get_model_weights = iterative_process.get_model_weights
centralized_validation_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
eval_dataset=stackoverflow_validation,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
def validation_fn(server_state, round_num):
del round_num
return centralized_validation_fn(
iterative_process.get_model_weights(server_state))
centralized_test_fn = training_utils.build_centralized_evaluate_fn(
model_builder=model_builder,
# Use both val and test for symmetry with other experiments, which
# evaluate on the entire test set.
eval_dataset=stackoverflow_validation.concatenate(stackoverflow_test),
loss_builder=loss_builder,
metrics_builder=metrics_builder)
def test_fn(server_state):
return centralized_test_fn(
iterative_process.get_model_weights(server_state))
return training_specs.RunnerSpec(iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
def configure_training(task_spec: training_specs.TaskSpec,
eval_spec: Optional[training_specs.EvalSpec] = None,
model: str = 'cnn') -> training_specs.RunnerSpec:
"""Configures training for the EMNIST character recognition task.
This method will load and pre-process datasets and construct a model used for
the task. It then uses `iterative_process_builder` to create an iterative
process compatible with `federated_research.utils.training_loop`.
Args:
task_spec: A `TaskSpec` class for creating federated training tasks.
eval_spec: An `EvalSpec` class for configuring federated evaluation. If set
to None, centralized evaluation is used for validation and testing
instead.
model: A string specifying the model used for character recognition. Can be
one of `cnn` and `2nn`, corresponding to a CNN model and a densely
connected 2-layer model (respectively).
Returns:
A `RunnerSpec` containing attributes used for running the newly created
federated task.
"""
emnist_task = 'digit_recognition'
emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data(
only_digits=False)
train_preprocess_fn = emnist_dataset.create_preprocess_fn(
num_epochs=task_spec.client_epochs_per_round,
batch_size=task_spec.client_batch_size,
emnist_task=emnist_task)
input_spec = train_preprocess_fn.type_signature.result.element
if model == 'cnn':
model_builder = functools.partial(
emnist_models.create_conv_dropout_model, only_digits=False)
elif model == '2nn':
model_builder = functools.partial(
emnist_models.create_two_hidden_layer_model, only_digits=False)
else:
raise ValueError(
'Cannot handle model flag [{!s}], must be one of {!s}.'.format(
model, EMNIST_MODELS))
loss_builder = tf.keras.losses.SparseCategoricalCrossentropy
metrics_builder = lambda: [tf.keras.metrics.SparseCategoricalAccuracy()]
def tff_model_fn() -> tff.learning.Model:
return tff.learning.from_keras_model(
keras_model=model_builder(),
input_spec=input_spec,
loss=loss_builder(),
metrics=metrics_builder())
iterative_process = task_spec.iterative_process_builder(tff_model_fn)
clients_per_train_round = min(task_spec.clients_per_round,
TOTAL_NUM_TRAIN_CLIENTS)
if hasattr(emnist_train, 'dataset_computation'):
@tff.tf_computation(tf.string)
def build_train_dataset_from_client_id(client_id):
client_dataset = emnist_train.dataset_computation(client_id)
return train_preprocess_fn(client_dataset)
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
build_train_dataset_from_client_id, iterative_process)
client_ids_fn = training_utils.build_sample_fn(
emnist_train.client_ids,
size=clients_per_train_round,
replace=False,
random_seed=task_spec.sampling_random_seed)
# We convert the output to a list (instead of an np.ndarray) so that it can
# be used as input to the iterative process.
client_sampling_fn = lambda x: list(client_ids_fn(x))
else:
training_process = tff.simulation.compose_dataset_computation_with_iterative_process(
train_preprocess_fn, iterative_process)
client_sampling_fn = training_utils.build_client_datasets_fn(
dataset=emnist_train,
clients_per_round=clients_per_train_round,
random_seed=task_spec.sampling_random_seed)
training_process.get_model_weights = iterative_process.get_model_weights
if eval_spec:
if eval_spec.clients_per_validation_round is None:
clients_per_validation_round = TOTAL_NUM_TEST_CLIENTS
else:
clients_per_validation_round = min(eval_spec.clients_per_validation_round,
TOTAL_NUM_TEST_CLIENTS)
if eval_spec.clients_per_test_round is None:
clients_per_test_round = TOTAL_NUM_TEST_CLIENTS
else:
clients_per_test_round = min(eval_spec.clients_per_test_round,
TOTAL_NUM_TEST_CLIENTS)
test_preprocess_fn = emnist_dataset.create_preprocess_fn(
num_epochs=1,
batch_size=eval_spec.client_batch_size,
shuffle_buffer_size=1,
emnist_task=emnist_task)
emnist_test = emnist_test.preprocess(test_preprocess_fn)
def eval_metrics_builder():
return [
tf.keras.metrics.SparseCategoricalCrossentropy(),
tf.keras.metrics.SparseCategoricalAccuracy()
]
federated_eval_fn = training_utils.build_federated_evaluate_fn(
model_builder=model_builder, metrics_builder=eval_metrics_builder)
validation_client_sampling_fn = training_utils.build_client_datasets_fn(
emnist_test,
clients_per_validation_round,
random_seed=eval_spec.sampling_random_seed)
test_client_sampling_fn = training_utils.build_client_datasets_fn(
emnist_test,
clients_per_test_round,
random_seed=eval_spec.sampling_random_seed)
def validation_fn(model_weights, round_num):
validation_clients = validation_client_sampling_fn(round_num)
return federated_eval_fn(model_weights, validation_clients)
def test_fn(model_weights):
# We fix the round number to get deterministic behavior
test_round_num = 0
test_clients = test_client_sampling_fn(test_round_num)
return federated_eval_fn(model_weights, test_clients)
else:
_, central_emnist_test = emnist_dataset.get_centralized_datasets(
only_digits=False, emnist_task=emnist_task)
test_fn = training_utils.build_centralized_evaluate_fn(
eval_dataset=central_emnist_test,
model_builder=model_builder,
loss_builder=loss_builder,
metrics_builder=metrics_builder)
validation_fn = lambda model_weights, round_num: test_fn(model_weights)
return training_specs.RunnerSpec(
iterative_process=training_process,
client_datasets_fn=client_sampling_fn,
validation_fn=validation_fn,
test_fn=test_fn)
