Extra functionality for TensorFlow, maintained by SIG-addons.
import tensorflow as tf
import tensorflow_addons as tfa
train,test = tf.keras.datasets.mnist.load_data()
x_train, y_train = train
x_train = x_train[..., tf.newaxis] / 255.0
# TFA layers and activations
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(filters=10, kernel_size=(3,3),
activation=tfa.activations.gelu),
tfa.layers.GroupNormalization(groups=5, axis=3),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10, activation='softmax')
])
# TFA optimizers, losses and metrics
model.compile(
optimizer=tfa.optimizers.RectifiedAdam(0.001),
loss=tfa.losses.TripletSemiHardLoss(),
metrics=[tfa.metrics.MultiLabelConfusionMatrix(num_classes=10)])
history = model.fit(x_train, y_train, epochs=10)
TensorFlow SIG Addons is a repository of community contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow.
TensorFlow natively supports a large number of operators, layers, metrics, losses, optimizers, and more. However, in a fast moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).