tutorials/mnist_dpsgd_tutorial_vectorized.py [94:117]:
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          learning_rate=FLAGS.learning_rate)
      opt_loss = vector_loss
    else:
      optimizer = GradientDescentOptimizer(learning_rate=FLAGS.learning_rate)
      opt_loss = scalar_loss
    global_step = tf.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).
  elif mode == tf.estimator.ModeKeys.EVAL:
    eval_metric_ops = {
        'accuracy':
            tf.metrics.accuracy(
                labels=labels, predictions=tf.argmax(input=logits, axis=1))
    }

    return tf.estimator.EstimatorSpec(
        mode=mode, loss=scalar_loss, eval_metric_ops=eval_metric_ops)
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tutorials/mnist_lr_tutorial.py [78:100]:
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          learning_rate=FLAGS.learning_rate)
      opt_loss = vector_loss
    else:
      optimizer = GradientDescentOptimizer(learning_rate=FLAGS.learning_rate)
      opt_loss = scalar_loss
    global_step = tf.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).
  elif mode == tf.estimator.ModeKeys.EVAL:
    eval_metric_ops = {
        'accuracy':
            tf.metrics.accuracy(
                labels=labels, predictions=tf.argmax(input=logits, axis=1))
    }
    return tf.estimator.EstimatorSpec(
        mode=mode, loss=scalar_loss, eval_metric_ops=eval_metric_ops)
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