research/GDP_2019/imdb_tutorial.py [62:96]:
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  vector_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
      labels=labels, logits=logits)
  # Define mean of loss across minibatch (for reporting through tf.Estimator).
  scalar_loss = tf.reduce_mean(vector_loss)

  # Configure the training op (for TRAIN mode).
  if mode == tf.estimator.ModeKeys.TRAIN:
    if FLAGS.dpsgd:
      # Use DP version of GradientDescentOptimizer. Other optimizers are
      # available in dp_optimizer. Most optimizers inheriting from
      # tf.train.Optimizer should be wrappable in differentially private
      # counterparts by calling dp_optimizer.optimizer_from_args().
      optimizer = dp_optimizer.DPAdamGaussianOptimizer(
          l2_norm_clip=FLAGS.l2_norm_clip,
          noise_multiplier=FLAGS.noise_multiplier,
          num_microbatches=microbatches,
          learning_rate=FLAGS.learning_rate)
      opt_loss = vector_loss
    else:
      optimizer = tf.compat.v1.train.AdamOptimizer(
          learning_rate=FLAGS.learning_rate)
      opt_loss = scalar_loss

    global_step = tf.compat.v1.train.get_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)

  # Add evaluation metrics (for EVAL mode).
  if mode == tf.estimator.ModeKeys.EVAL:
    eval_metric_ops = {
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tutorials/movielens_tutorial.py [85:119]:
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  vector_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
      labels=labels, logits=logits)
  # Define mean of loss across minibatch (for reporting through tf.Estimator).
  scalar_loss = tf.reduce_mean(vector_loss)

  # Configure the training op (for TRAIN mode).
  if mode == tf.estimator.ModeKeys.TRAIN:
    if FLAGS.dpsgd:
      # Use DP version of GradientDescentOptimizer. Other optimizers are
      # available in dp_optimizer. Most optimizers inheriting from
      # tf.train.Optimizer should be wrappable in differentially private
      # counterparts by calling dp_optimizer.optimizer_from_args().
      optimizer = dp_optimizer.DPAdamGaussianOptimizer(
          l2_norm_clip=FLAGS.l2_norm_clip,
          noise_multiplier=FLAGS.noise_multiplier,
          num_microbatches=microbatches,
          learning_rate=FLAGS.learning_rate)
      opt_loss = vector_loss
    else:
      optimizer = tf.compat.v1.train.AdamOptimizer(
          learning_rate=FLAGS.learning_rate)
      opt_loss = scalar_loss

    global_step = tf.compat.v1.train.get_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)

  # Add evaluation metrics (for EVAL mode).
  if mode == tf.estimator.ModeKeys.EVAL:
    eval_metric_ops = {
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