A separate tensorboard function.

PiperOrigin-RevId: 322820408

tensorflow_privacy/privacy/membership_inference_attack/tf_estimator_evaluation.py

@@ -23,6 +23,7 @@ import tensorflow.compat.v1 as tf
 
 from tensorflow_privacy.privacy.membership_inference_attack import membership_inference_attack as mia
 from tensorflow_privacy.privacy.membership_inference_attack.utils import log_loss
+from tensorflow_privacy.privacy.membership_inference_attack.utils import write_to_tensorboard
 
 
 def calculate_losses(estimator, input_fn, labels):
@@ -36,7 +37,7 @@ def calculate_losses(estimator, input_fn, labels):
   Args:
     estimator: model to make prediction
     input_fn: input function to be used in estimator.predict
-    labels: true labels of samples
+    labels: true labels of samples (integer valued)
 
   Returns:
     preds: probability vector of each sample
@@ -92,13 +93,10 @@ class MembershipInferenceTrainingHook(tf.estimator.SessionRunHook):
     print('all_thresh_loss_advantage', results['all_thresh_loss_advantage'])
     logging.info(results)
 
-    if self._writer:
-      summary = tf.Summary()
-      summary.value.add(tag='attack advantage',
-                        simple_value=results['all_thresh_loss_advantage'])
-      global_step = self._estimator.get_variable_value('global_step')
-      self._writer.add_summary(summary, global_step)
-      self._writer.flush()
+    # Write to tensorboard if writer is specified
+    global_step = self._estimator.get_variable_value('global_step')
+    write_to_tensorboard(self._writer, ['attack advantage'],
+                         [results['all_thresh_loss_advantage']], global_step)
 
 
 def run_attack_on_tf_estimator_model(estimator, in_train, out_train,

tensorflow_privacy/privacy/membership_inference_attack/utils.py

@@ -19,6 +19,8 @@ from typing import Text, Dict, Union, List, Any, Tuple
 
 import numpy as np
 from sklearn import metrics
+import tensorflow.compat.v1 as tf
+
 
 ArrayDict = Dict[Text, np.ndarray]
 Dataset = Tuple[Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]
@@ -236,3 +238,26 @@ def log_loss(y, pred, small_value=1e-8):
     the cross-entropy loss of each sample
   """
   return -np.log(np.maximum(pred[range(y.size), y], small_value))
+
+
+# ------------------------------------------------------------------------------
+#  Tensorboard
+# ------------------------------------------------------------------------------
+
+
+def write_to_tensorboard(writer, tags, values, step):
+  """Write metrics to tensorboard.
+
+  Args:
+    writer: tensorboard writer
+    tags: a list of tags of metrics
+    values: a list of values of metrics
+    step: step for the summary
+  """
+  if writer is None:
+    return
+  summary = tf.Summary()
+  for tag, val in zip(tags, values):
+    summary.value.add(tag=tag, simple_value=val)
+  writer.add_summary(summary, step)
+  writer.flush()