Write to Tensorboard in Keras under TF2.

PiperOrigin-RevId: 349446504

tensorflow_privacy/privacy/membership_inference_attack/keras_evaluation.py

@@ -19,7 +19,7 @@ import os
 from typing import Iterable
 from absl import logging
 
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 
 from tensorflow_privacy.privacy.membership_inference_attack import membership_inference_attack as mia
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackInputData
@@ -27,7 +27,7 @@ from tensorflow_privacy.privacy.membership_inference_attack.data_structures impo
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import get_flattened_attack_metrics
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingSpec
 from tensorflow_privacy.privacy.membership_inference_attack.utils import log_loss
-from tensorflow_privacy.privacy.membership_inference_attack.utils_tensorboard import write_results_to_tensorboard
+from tensorflow_privacy.privacy.membership_inference_attack.utils_tensorboard import write_results_to_tensorboard_tf2 as write_results_to_tensorboard
 
 
 def calculate_losses(model, data, labels):
@@ -76,13 +76,11 @@ class MembershipInferenceCallback(tf.keras.callbacks.Callback):
     if tensorboard_dir:
       if tensorboard_merge_classifiers:
         self._writers = {}
-        with tf.Graph().as_default():
         for attack_type in attack_types:
-            self._writers[attack_type.name] = tf.summary.FileWriter(
+          self._writers[attack_type.name] = tf.summary.create_file_writer(
               os.path.join(tensorboard_dir, 'MI', attack_type.name))
       else:
-        with tf.Graph().as_default():
+        self._writers = tf.summary.create_file_writer(
-          self._writers = tf.summary.FileWriter(
             os.path.join(tensorboard_dir, 'MI'))
       logging.info('Will write to tensorboard.')
     else:

tensorflow_privacy/privacy/membership_inference_attack/keras_evaluation_example.py

@@ -19,7 +19,7 @@ from absl import app
 from absl import flags
 
 import numpy as np
-import tensorflow.compat.v1 as tf
+import tensorflow as tf
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackType
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import get_flattened_attack_metrics
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import SlicingSpec

tensorflow_privacy/privacy/membership_inference_attack/tf_estimator_evaluation_example.py

@@ -102,7 +102,7 @@ def main(unused_argv):
   x_train, y_train, x_test, y_test = load_cifar10()
 
   # Instantiate the tf.Estimator.
-  mnist_classifier = tf.estimator.Estimator(
+  classifier = tf.estimator.Estimator(
       model_fn=small_cnn_fn, model_dir=FLAGS.model_dir)
 
   # A function to construct input_fn given (data, label), to be used by the
@@ -112,7 +112,7 @@ def main(unused_argv):
 
   # Get hook for membership inference attack.
   mia_hook = MembershipInferenceTrainingHook(
-      mnist_classifier,
+      classifier,
       (x_train, y_train),
       (x_test, y_test),
       input_fn_constructor,
@@ -133,20 +133,20 @@ def main(unused_argv):
       x={'x': x_test}, y=y_test, num_epochs=1, shuffle=False)
 
   # Training loop.
-  steps_per_epoch = 60000 // FLAGS.batch_size
+  steps_per_epoch = 50000 // FLAGS.batch_size
   for epoch in range(1, FLAGS.epochs + 1):
     # Train the model, with the membership inference hook.
-    mnist_classifier.train(
+    classifier.train(
         input_fn=train_input_fn, steps=steps_per_epoch, hooks=[mia_hook])
 
     # Evaluate the model and print results
-    eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
+    eval_results = classifier.evaluate(input_fn=eval_input_fn)
     test_accuracy = eval_results['accuracy']
     print('Test accuracy after %d epochs is: %.3f' % (epoch, test_accuracy))
 
   print('End of training attack')
   attack_results = run_attack_on_tf_estimator_model(
-      mnist_classifier, (x_train, y_train), (x_test, y_test),
+      classifier, (x_train, y_train), (x_test, y_test),
       input_fn_constructor,
       slicing_spec=SlicingSpec(entire_dataset=True, by_class=True),
       attack_types=[AttackType.THRESHOLD_ATTACK, AttackType.K_NEAREST_NEIGHBORS]

tensorflow_privacy/privacy/membership_inference_attack/utils_tensorboard.py

@@ -17,7 +17,8 @@
 from typing import List
 from typing import Union
 
-import tensorflow.compat.v1 as tf
+import tensorflow as tf2
+import tensorflow.compat.v1 as tf1
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import AttackResults
 from tensorflow_privacy.privacy.membership_inference_attack.data_structures import get_flattened_attack_metrics
 
@@ -41,7 +42,7 @@ def write_to_tensorboard(writers, tags, values, step):
   assert len(writers) == len(tags) == len(values)
 
   for writer, tag, val in zip(writers, tags, values):
-    summary = tf.Summary()
+    summary = tf1.Summary()
     summary.value.add(tag=tag, simple_value=val)
     writer.add_summary(summary, step)
 
@@ -49,9 +50,37 @@ def write_to_tensorboard(writers, tags, values, step):
     writer.flush()
 
 
+def write_to_tensorboard_tf2(writers, tags, values, step):
+  """Write metrics to tensorboard.
+
+  Args:
+    writers: a list of tensorboard writers or one writer to be used for metrics.
+    If it's a list, it should be of the same length as tags
+    tags: a list of tags of metrics
+    values: a list of values of metrics with the same length as tags
+    step: step for the tensorboard summary
+  """
+  if writers is None or not writers:
+    raise ValueError('write_to_tensorboard does not get any writer.')
+
+  if not isinstance(writers, list):
+    writers = [writers] * len(tags)
+
+  assert len(writers) == len(tags) == len(values)
+
+  for writer, tag, val in zip(writers, tags, values):
+    with writer.as_default():
+      tf2.summary.scalar(tag, val, step=step)
+      writer.flush()
+
+  for writer in set(writers):
+    with writer.as_default():
+      writer.flush()
+
+
 def write_results_to_tensorboard(
     attack_results: AttackResults,
-    writers: Union[tf.summary.FileWriter, List[tf.summary.FileWriter]],
+    writers: Union[tf1.summary.FileWriter, List[tf1.summary.FileWriter]],
     step: int,
     merge_classifiers: bool):
   """Write attack results to tensorboard.
@@ -69,11 +98,42 @@ def write_results_to_tensorboard(
   att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics(
       attack_results)
   if merge_classifiers:
-    att_tags = ['attack/' + '_'.join([s, m]) for s, m in
+    att_tags = ['attack/' + f'{s}_{m}' for s, m in
                 zip(att_slices, att_metrics)]
     write_to_tensorboard([writers[t] for t in att_types],
                          att_tags, att_values, step)
   else:
-    att_tags = ['attack/' + '_'.join([s, t, m]) for t, s, m in
+    att_tags = ['attack/' + f'{s}_{t}_{m}' for t, s, m in
                 zip(att_types, att_slices, att_metrics)]
     write_to_tensorboard(writers, att_tags, att_values, step)
+
+
+def write_results_to_tensorboard_tf2(
+    attack_results: AttackResults,
+    writers: Union[tf2.summary.SummaryWriter, List[tf2.summary.SummaryWriter]],
+    step: int,
+    merge_classifiers: bool):
+  """Write attack results to tensorboard.
+
+  Args:
+    attack_results: results from attack
+    writers: a list of tensorboard writers or one writer to be used for metrics
+    step: step for the tensorboard summary
+    merge_classifiers: if true, plot different classifiers with the same
+      slicing_spec and metric in the same figure
+  """
+  if writers is None or not writers:
+    raise ValueError('write_results_to_tensorboard does not get any writer.')
+
+  att_types, att_slices, att_metrics, att_values = get_flattened_attack_metrics(
+      attack_results)
+  if merge_classifiers:
+    att_tags = ['attack/' + f'{s}_{m}' for s, m in
+                zip(att_slices, att_metrics)]
+    write_to_tensorboard_tf2([writers[t] for t in att_types],
+                             att_tags, att_values, step)
+  else:
+    att_tags = ['attack/' + f'{s}_{t}_{m}' for t, s, m in
+                zip(att_types, att_slices, att_metrics)]
+    write_to_tensorboard_tf2(writers, att_tags, att_values, step)
+