In open source, import tf_keras instead of setting TF_USE_LEGACY_KERA…

…S=1.

With this change, other imported libraries are free to use Keras 3 while TFP uses Keras 2.

PiperOrigin-RevId: 578174856

STYLE_GUIDE.md

@@ -187,7 +187,7 @@ they supersede all previous conventions.
 1.  Submodule names should be singular, except where they overlap to TF.
 
     Justification: Having plural looks strange in user code, ie,
-    tf.optimizer.Foo reads nicer than tf.keras.optimizers.Foo since submodules
+    tf.optimizer.Foo reads nicer than tf_keras.optimizers.Foo since submodules
     are only used to access a single, specific thing (at a time).
 
 1.  Use `tf.newaxis` rather than `None` to `tf.expand_dims`.

SUBSTRATES.md

@@ -75,7 +75,7 @@ vmap, etc.), we will special-case using an `if JAX_MODE:` block.
     tests, TFP impl, etc), with `tfp.math.value_and_gradient` or similar. Then,
     we can special-case `JAX_MODE` inside the body of `value_and_gradient`.
 
-*   __`tf.Variable`, `tf.keras.optimizers.Optimizer`__
+*   __`tf.Variable`, `tf_keras.optimizers.Optimizer`__
 
     TF provides a `Variable` abstraction so that graph functions may modify
     state, including using the Keras `Optimizer` subclasses like `Adam`. JAX,

tensorflow_probability/examples/BUILD

@@ -84,6 +84,7 @@ py_library(
         # six dep,
         # tensorflow dep,
         "//tensorflow_probability",
+        "//tensorflow_probability/python/internal:tf_keras",
     ],
 )
 

tensorflow_probability/examples/bayesian_neural_network.py

@@ -37,6 +37,7 @@
 import numpy as np
 import tensorflow.compat.v2 as tf
 import tensorflow_probability as tfp
+from tensorflow_probability.python.internal import tf_keras
 
 tf.enable_v2_behavior()
 
@@ -174,26 +175,26 @@ def create_model():
   # and two fully connected dense layers. We use the Flipout
   # Monte Carlo estimator for these layers, which enables lower variance
   # stochastic gradients than naive reparameterization.
-  model = tf.keras.models.Sequential([
+  model = tf_keras.models.Sequential([
       tfp.layers.Convolution2DFlipout(
           6, kernel_size=5, padding='SAME',
           kernel_divergence_fn=kl_divergence_function,
           activation=tf.nn.relu),
-      tf.keras.layers.MaxPooling2D(
+      tf_keras.layers.MaxPooling2D(
           pool_size=[2, 2], strides=[2, 2],
           padding='SAME'),
       tfp.layers.Convolution2DFlipout(
           16, kernel_size=5, padding='SAME',
           kernel_divergence_fn=kl_divergence_function,
           activation=tf.nn.relu),
-      tf.keras.layers.MaxPooling2D(
+      tf_keras.layers.MaxPooling2D(
           pool_size=[2, 2], strides=[2, 2],
           padding='SAME'),
       tfp.layers.Convolution2DFlipout(
           120, kernel_size=5, padding='SAME',
           kernel_divergence_fn=kl_divergence_function,
           activation=tf.nn.relu),
-      tf.keras.layers.Flatten(),
+      tf_keras.layers.Flatten(),
       tfp.layers.DenseFlipout(
           84, kernel_divergence_fn=kl_divergence_function,
           activation=tf.nn.relu),
@@ -203,7 +204,7 @@ def create_model():
   ])
 
   # Model compilation.
-  optimizer = tf.keras.optimizers.Adam(lr=FLAGS.learning_rate)
+  optimizer = tf_keras.optimizers.Adam(lr=FLAGS.learning_rate)
   # We use the categorical_crossentropy loss since the MNIST dataset contains
   # ten labels. The Keras API will then automatically add the
   # Kullback-Leibler divergence (contained on the individual layers of
@@ -214,7 +215,7 @@ def create_model():
   return model
 
 
-class MNISTSequence(tf.keras.utils.Sequence):
+class MNISTSequence(tf_keras.utils.Sequence):
   """Produces a sequence of MNIST digits with labels."""
 
   def __init__(self, data=None, batch_size=128, fake_data_size=None):
@@ -272,7 +273,7 @@ def __preprocessing(images, labels):
     images = 2 * (images / 255.) - 1.
     images = images[..., tf.newaxis]
 
-    labels = tf.keras.utils.to_categorical(labels)
+    labels = tf_keras.utils.to_categorical(labels)
     return images, labels
 
   def __len__(self):
@@ -298,7 +299,7 @@ def main(argv):
     heldout_seq = MNISTSequence(batch_size=FLAGS.batch_size,
                                 fake_data_size=NUM_HELDOUT_EXAMPLES)
   else:
-    train_set, heldout_set = tf.keras.datasets.mnist.load_data()
+    train_set, heldout_set = tf_keras.datasets.mnist.load_data()
     train_seq = MNISTSequence(data=train_set, batch_size=FLAGS.batch_size)
     heldout_seq = MNISTSequence(data=heldout_set, batch_size=FLAGS.batch_size)
 

tensorflow_probability/examples/cifar10_bnn.py

@@ -47,6 +47,8 @@
 from tensorflow_probability.examples.models.bayesian_resnet import bayesian_resnet
 from tensorflow_probability.examples.models.bayesian_vgg import bayesian_vgg
 
+from tensorflow_probability.python.internal import tf_keras
+
 matplotlib.use("Agg")
 warnings.simplefilter(action="ignore")
 tfd = tfp.distributions
@@ -169,7 +171,7 @@ def main(argv):
   if FLAGS.fake_data:
     (x_train, y_train), (x_test, y_test) = build_fake_data()
   else:
-    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
+    (x_train, y_train), (x_test, y_test) = tf_keras.datasets.cifar10.load_data()
 
   (images, labels, handle,
    training_iterator,