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Update Keras tutorial in light of recent changes to Keras Optimizer.

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PiperOrigin-RevId: 326346140
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Steve Chien 2020-08-12 17:12:41 -07:00 committed by A. Unique TensorFlower
parent f8515dfd71
commit 3240a71965
1 changed files with 7 additions and 9 deletions
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16
tutorials/mnist_dpsgd_tutorial_keras.py
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@ -22,19 +22,17 @@ from absl import flags
from absl import logging from absl import logging
import numpy as np import numpy as np
import tensorflow.compat.v1 as tf import tensorflow as tf
from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer from tensorflow_privacy.privacy.optimizers.dp_optimizer_keras import DPKerasSGDOptimizer
GradientDescentOptimizer = tf.train.GradientDescentOptimizer
flags.DEFINE_boolean( flags.DEFINE_boolean(
'dpsgd', True, 'If True, train with DP-SGD. If False, ' 'dpsgd', True, 'If True, train with DP-SGD. If False, '
'train with vanilla SGD.') 'train with vanilla SGD.')
flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training') flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
flags.DEFINE_float('noise_multiplier', 1.1, flags.DEFINE_float('noise_multiplier', 0.1,
'Ratio of the standard deviation to the clipping norm') 'Ratio of the standard deviation to the clipping norm')
flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm') flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
flags.DEFINE_integer('batch_size', 250, 'Batch size') flags.DEFINE_integer('batch_size', 250, 'Batch size')
@ -70,8 +68,8 @@ def load_mnist():
train_data = np.array(train_data, dtype=np.float32) / 255 train_data = np.array(train_data, dtype=np.float32) / 255
test_data = np.array(test_data, dtype=np.float32) / 255 test_data = np.array(test_data, dtype=np.float32) / 255
train_data = train_data.reshape(train_data.shape[0], 28, 28, 1) train_data = train_data.reshape((train_data.shape[0], 28, 28, 1))
test_data = test_data.reshape(test_data.shape[0], 28, 28, 1) test_data = test_data.reshape((test_data.shape[0], 28, 28, 1))
train_labels = np.array(train_labels, dtype=np.int32) train_labels = np.array(train_labels, dtype=np.int32)
test_labels = np.array(test_labels, dtype=np.int32) test_labels = np.array(test_labels, dtype=np.int32)
@ -114,7 +112,7 @@ def main(unused_argv):
]) ])
if FLAGS.dpsgd: if FLAGS.dpsgd:
optimizer = DPGradientDescentGaussianOptimizer( optimizer = DPKerasSGDOptimizer(
l2_norm_clip=FLAGS.l2_norm_clip, l2_norm_clip=FLAGS.l2_norm_clip,
noise_multiplier=FLAGS.noise_multiplier, noise_multiplier=FLAGS.noise_multiplier,
num_microbatches=FLAGS.microbatches, num_microbatches=FLAGS.microbatches,
@ -123,7 +121,7 @@ def main(unused_argv):
loss = tf.keras.losses.CategoricalCrossentropy( loss = tf.keras.losses.CategoricalCrossentropy(
from_logits=True, reduction=tf.losses.Reduction.NONE) from_logits=True, reduction=tf.losses.Reduction.NONE)
else: else:
optimizer = GradientDescentOptimizer(learning_rate=FLAGS.learning_rate) optimizer = tf.keras.optimizers.SGD(learning_rate=FLAGS.learning_rate)
loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True) loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
# Compile model with Keras # Compile model with Keras