def construct_example_training_comp():
"""Constructs a `computation_utils.IterativeProcess` via the FL API."""
np.random.seed(0)
sample_batch = collections.OrderedDict([
('x', np.array([[1., 1.]], dtype=np.float32)),
('y', np.array([[0]], dtype=np.int32))
])
def model_fn():
"""Constructs keras model."""
keras_model = tf.keras.models.Sequential([
tf.keras.layers.Dense(1,
activation=tf.nn.softmax,
kernel_initializer='zeros',
input_shape=(2, ))
])
def loss_fn(y_true, y_pred):
return tf.reduce_mean(
tf.keras.losses.sparse_categorical_crossentropy(
y_true, y_pred))
keras_model.compile(
loss=loss_fn,
optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
return learning.from_compiled_keras_model(keras_model, sample_batch)
return learning.build_federated_averaging_process(model_fn)
def construct_example_training_comp():
"""Constructs a `tff.utils.IterativeProcess` via the FL API."""
np.random.seed(0)
sample_batch = collections.OrderedDict(x=np.array([[1., 1.]],
dtype=np.float32),
y=np.array([[0]], dtype=np.int32))
def model_fn():
"""Constructs keras model."""
keras_model = tf.keras.models.Sequential([
tf.keras.layers.Dense(1,
activation=tf.nn.softmax,
kernel_initializer='zeros',
input_shape=(2, ))
])
return learning.from_keras_model(
keras_model,
dummy_batch=sample_batch,
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
return learning.build_federated_averaging_process(
model_fn,
client_optimizer_fn=lambda: tf.keras.optimizers.SGD(learning_rate=0.01
))