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Modify loss passed to optimizer when dpsgd is False in MNIST tutorial

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PiperOrigin-RevId: 229233829
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Nicolas Papernot 2019-01-14 12:37:59 -08:00 committed by A. Unique TensorFlower
parent 89ca3f2a06
commit 4c1f3c07f4
1 changed files with 7 additions and 1 deletions
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tutorials/mnist_dpsgd_tutorial.py
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@ -79,11 +79,17 @@ def cnn_model_fn(features, labels, mode):
noise_multiplier=FLAGS.noise_multiplier,
l2_norm_clip=FLAGS.l2_norm_clip,
num_microbatches=FLAGS.microbatches)
opt_loss = vector_loss
else:
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=FLAGS.learning_rate)
opt_loss = scalar_loss
global_step = tf.train.get_global_step()
train_op = optimizer.minimize(loss=vector_loss, global_step=global_step)
train_op = optimizer.minimize(loss=opt_loss, global_step=global_step)
# In the following, we pass the mean of the loss (scalar_loss) rather than
# the vector_loss because tf.estimator requires a scalar loss. This is only
# used for evaluation and debugging by tf.estimator. The actual loss being
# minimized is opt_loss defined above and passed to optimizer.minimize().
return tf.estimator.EstimatorSpec(mode=mode,
loss=scalar_loss,
train_op=train_op)