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fix keras typo

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PiperOrigin-RevId: 258434656
This commit is contained in:
Nicolas Papernot 2019-07-16 13:58:56 -07:00 committed by A. Unique TensorFlower
parent 973a1759aa
commit bb7956ed7e
1 changed files with 4 additions and 5 deletions
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9
tutorials/mnist_dpsgd_tutorial_keras.py
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@ -41,10 +41,10 @@ flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
flags.DEFINE_float('noise_multiplier', 1.1,
'Ratio of the standard deviation to the clipping norm')
flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
flags.DEFINE_integer('batch_size', 256, 'Batch size')
flags.DEFINE_integer('batch_size', 250, 'Batch size')
flags.DEFINE_integer('epochs', 60, 'Number of epochs')
flags.DEFINE_integer(
'microbatches', 256, 'Number of microbatches '
'microbatches', 250, 'Number of microbatches '
'(must evenly divide batch_size)')
flags.DEFINE_string('model_dir', None, 'Model directory')
@ -121,9 +121,8 @@ def main(unused_argv):
optimizer = DPGradientDescentGaussianOptimizer(
l2_norm_clip=FLAGS.l2_norm_clip,
noise_multiplier=FLAGS.noise_multiplier,
num_microbatches=FLAGS.num_microbatches,
learning_rate=FLAGS.learning_rate,
unroll_microbatches=True)
num_microbatches=FLAGS.microbatches,
learning_rate=FLAGS.learning_rate)
# Compute vector of per-example loss rather than its mean over a minibatch.
loss = tf.keras.losses.CategoricalCrossentropy(
from_logits=True, reduction=tf.losses.Reduction.NONE)