Changes for API docstrings for TF.org:

(1) Hide documentation for superclass methods in DPModel.
(2) Make compute_dp_sgd_privacy visible.

PiperOrigin-RevId: 377553548

g3doc/build_docs.py

@@ -19,6 +19,9 @@ import os
 from absl import app
 from absl import flags
 
+import tensorflow as tf
+
+from tensorflow_docs.api_generator import doc_controls
 from tensorflow_docs.api_generator import generate_lib
 from tensorflow_docs.api_generator import public_api
 
@@ -44,10 +47,35 @@ PROJECT_SHORT_NAME = 'tf_privacy'
 PROJECT_FULL_NAME = 'TensorFlow Privacy'
 
 
+def _hide_layer_and_module_methods():
+  """Hide methods and properties defined in the base classes of keras layers."""
+  # __dict__ only sees attributes defined in *this* class, not on parent classes
+  # Needed to ignore redudant subclass documentation
+  model_contents = list(tf.keras.Model.__dict__.items())
+  layer_contents = list(tf.keras.layers.Layer.__dict__.items())
+  module_contents = list(tf.Module.__dict__.items())
+
+  for name, obj in model_contents + layer_contents + module_contents:
+    if name == '__init__':
+      continue
+
+    if isinstance(obj, property):
+      obj = obj.fget
+
+    if isinstance(obj, (staticmethod, classmethod)):
+      obj = obj.__func__
+
+    try:
+      doc_controls.do_not_doc_in_subclasses(obj)
+    except AttributeError:
+      pass
+
+
 def gen_api_docs():
   """Generates api docs for the tensorflow docs package."""
   output_dir = FLAGS.output_dir
 
+  _hide_layer_and_module_methods()
   doc_generator = generate_lib.DocGenerator(
       root_title=PROJECT_FULL_NAME,
       py_modules=[(PROJECT_SHORT_NAME, tf_privacy)],

tensorflow_privacy/__init__.py

@@ -27,6 +27,7 @@ if hasattr(sys, 'skip_tf_privacy_import'):  # Useful for standalone scripts.
   pass
 else:
   # Analysis
+  from tensorflow_privacy.privacy.analysis.compute_dp_sgd_privacy_lib import compute_dp_sgd_privacy
   from tensorflow_privacy.privacy.analysis.privacy_ledger import GaussianSumQueryEntry
   from tensorflow_privacy.privacy.analysis.privacy_ledger import PrivacyLedger
   from tensorflow_privacy.privacy.analysis.privacy_ledger import QueryWithLedger

tensorflow_privacy/privacy/analysis/compute_dp_sgd_privacy_lib.py

@@ -55,7 +55,18 @@ def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
 
 
 def compute_dp_sgd_privacy(n, batch_size, noise_multiplier, epochs, delta):
-  """Compute epsilon based on the given hyperparameters."""
+  """Compute epsilon based on the given hyperparameters.
+
+  Args:
+    n: Number of examples in the training data.
+    batch_size: Batch size used in training.
+    noise_multiplier: Noise multiplier used in training.
+    epochs: Number of epochs in training.
+    delta: Value of delta for which to compute epsilon.
+
+  Returns:
+    Value of epsilon corresponding to input hyperparameters.
+  """
   q = batch_size / n  # q - the sampling ratio.
   if q > 1:
     raise app.UsageError('n must be larger than the batch size.')