Fix bug in v1 estimators that was preventing use of microbatches.
PiperOrigin-RevId: 560765153
This commit is contained in:
Steve Chien
A. Unique TensorFlower
parent
b4b47b1403
commit
372c934d14
3 changed files with 51 additions and 21 deletions
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@ -20,16 +20,20 @@ from tensorflow_privacy.privacy.estimators import test_utils
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from tensorflow_privacy.privacy.estimators.v1 import dnn
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from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
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# pylint: disable=g-deprecated-tf-checker
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class DPDNNClassifierTest(tf.test.TestCase, parameterized.TestCase):
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"""Tests for DP-enabled DNNClassifier."""
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@parameterized.named_parameters(
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('BinaryClassDNN', 2),
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('MultiClassDNN 3', 3),
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('MultiClassDNN 4', 4),
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('BinaryClassDNN', 2, 1),
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('BinaryClassDNN 4', 2, 4),
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('MultiClassDNN 3', 3, 1),
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('MultiClassDNN 4', 4, 1),
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('MultiClassDNN 4 4', 4, 4),
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)
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def testDNN(self, n_classes):
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def testDNN(self, n_classes, num_microbatches):
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train_features, train_labels = test_utils.make_input_data(256, n_classes)
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feature_columns = []
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for key in train_features:
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@ -40,7 +44,8 @@ class DPDNNClassifierTest(tf.test.TestCase, parameterized.TestCase):
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learning_rate=0.5,
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l2_norm_clip=1.0,
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noise_multiplier=0.0,
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num_microbatches=1)
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num_microbatches=num_microbatches,
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)
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classifier = dnn.DNNClassifier(
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hidden_units=[10],
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@ -16,6 +16,7 @@
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import tensorflow as tf
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from tensorflow.python.ops import lookup_ops # pylint: disable=g-direct-tensorflow-import
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# pylint: disable=g-deprecated-tf-checker
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from tensorflow_estimator.python.estimator import model_fn
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from tensorflow_estimator.python.estimator.canned import head as head_lib
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from tensorflow_estimator.python.estimator.canned import metric_keys
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@ -23,6 +24,7 @@ from tensorflow_estimator.python.estimator.canned import prediction_keys
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from tensorflow_estimator.python.estimator.export import export_output
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from tensorflow_estimator.python.estimator.mode_keys import ModeKeys
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# Collect together all protected access items needed from base head.
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# pylint: disable=protected-access
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_DEFAULT_SERVING_KEY = head_lib._DEFAULT_SERVING_KEY
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@ -39,8 +41,12 @@ _create_eval_metrics_tuple = head_lib._create_eval_metrics_tuple
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_summary_key = head_lib._summary_key
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_validate_loss_fn_args = head_lib._validate_loss_fn_args
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_BaseBinaryLogisticHeadWithSigmoidCrossEntropyLoss = head_lib._BinaryLogisticHeadWithSigmoidCrossEntropyLoss
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_BaseMultiClassHeadWithSoftmaxCrossEntropyLoss = head_lib._MultiClassHeadWithSoftmaxCrossEntropyLoss
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_BaseBinaryLogisticHeadWithSigmoidCrossEntropyLoss = (
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head_lib._BinaryLogisticHeadWithSigmoidCrossEntropyLoss
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)
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_BaseMultiClassHeadWithSoftmaxCrossEntropyLoss = (
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head_lib._MultiClassHeadWithSoftmaxCrossEntropyLoss
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)
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# pylint: enable=protected-access
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@ -146,25 +152,33 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(
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classifier_output = _classification_output(
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scores=probabilities,
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n_classes=self._n_classes,
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label_vocabulary=self._label_vocabulary)
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label_vocabulary=self._label_vocabulary,
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)
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return model_fn._TPUEstimatorSpec( # pylint: disable=protected-access
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mode=ModeKeys.PREDICT,
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predictions=predictions,
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export_outputs={
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_DEFAULT_SERVING_KEY: classifier_output,
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_CLASSIFY_SERVING_KEY: classifier_output,
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_PREDICT_SERVING_KEY: export_output.PredictOutput(predictions)
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})
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_PREDICT_SERVING_KEY: export_output.PredictOutput(predictions),
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},
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)
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training_loss, unreduced_loss, weights, label_ids = self.create_loss(
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features=features, mode=mode, logits=logits, labels=labels)
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features=features, mode=mode, logits=logits, labels=labels
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)
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if regularization_losses:
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regularization_loss = tf.math.add_n(regularization_losses)
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regularized_training_loss = tf.math.add_n(
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[training_loss, regularization_loss])
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[training_loss, regularization_loss]
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)
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unreduced_regularized_training_loss = tf.math.add(
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unreduced_loss, regularization_loss
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)
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else:
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regularization_loss = None
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regularized_training_loss = training_loss
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unreduced_regularized_training_loss = unreduced_loss
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if self._loss_reduction == tf.compat.v1.losses.Reduction.NONE:
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scalar_loss = tf.reduce_mean(regularized_training_loss)
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@ -191,8 +205,10 @@ class _MultiClassHeadWithSoftmaxCrossEntropyLoss(
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if train_op_fn is not None:
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raise ValueError('train_op_fn and optimizer cannot both be set.')
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train_op = optimizer.minimize(
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regularized_training_loss,
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global_step=tf.compat.v1.train.get_global_step())
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# regularized_training_loss,
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unreduced_regularized_training_loss,
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global_step=tf.compat.v1.train.get_global_step(),
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)
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elif train_op_fn is not None:
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train_op = train_op_fn(regularized_training_loss)
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else:
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@ -352,9 +368,13 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(
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regularization_loss = tf.math.add_n(regularization_losses)
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regularized_training_loss = tf.math.add_n(
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[training_loss, regularization_loss])
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unreduced_regularized_training_loss = tf.math.add(
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unreduced_loss, regularization_loss
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)
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else:
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regularization_loss = None
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regularized_training_loss = training_loss
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unreduced_regularized_training_loss = unreduced_loss
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if self._loss_reduction == tf.compat.v1.losses.Reduction.NONE:
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scalar_loss = tf.reduce_mean(regularized_training_loss)
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@ -382,8 +402,9 @@ class _BinaryLogisticHeadWithSigmoidCrossEntropyLoss(
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if train_op_fn is not None:
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raise ValueError('train_op_fn and optimizer cannot both be set.')
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train_op = optimizer.minimize(
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regularized_training_loss,
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global_step=tf.compat.v1.train.get_global_step())
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unreduced_regularized_training_loss,
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global_step=tf.compat.v1.train.get_global_step(),
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)
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elif train_op_fn is not None:
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train_op = train_op_fn(regularized_training_loss)
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else:
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@ -21,6 +21,8 @@ from tensorflow_privacy.privacy.estimators import test_utils
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from tensorflow_privacy.privacy.estimators.v1 import linear
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from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
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# pylint: disable=g-deprecated-tf-checker
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class DPLinearClassifierClassifierTest(
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tf.test.TestCase, parameterized.TestCase
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@ -28,11 +30,13 @@ class DPLinearClassifierClassifierTest(
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"""Tests for DP-enabled LinearClassifier."""
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@parameterized.named_parameters(
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('BinaryClassLinear', 2),
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('MultiClassLinear 3', 3),
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('MultiClassLinear 4', 4),
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('BinaryClassLinear 1', 2, 1),
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('BinaryClassLinear 4', 2, 4),
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('MultiClassLinear 3', 3, 1),
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('MultiClassLinear 4', 4, 1),
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('MultiClassLinear 4 1', 4, 2),
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)
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def testLinearClassifier(self, n_classes):
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def testRunsWithoutErrors(self, n_classes, num_microbatches):
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train_features, train_labels = test_utils.make_input_data(256, n_classes)
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feature_columns = []
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for key in train_features:
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@ -43,7 +47,7 @@ class DPLinearClassifierClassifierTest(
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learning_rate=0.5,
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l2_norm_clip=1.0,
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noise_multiplier=0.0,
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num_microbatches=1,
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num_microbatches=num_microbatches,
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)
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classifier = linear.LinearClassifier(
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