(learning_rate, hparams)
return {
      "rmsprop":
          tf.RMSPropOptimizer(
              learning_rate,
              decay=0.95,
              momentum=hparams.momentum,
              epsilon=1e-4),
      "adam":
          tf.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8),
      "adagrad":
          tf.AdagradOptimizer(learning_rate, initial_accumulator_value=1.0),
      "mom":
          tf.MomentumOptimizer(learning_rate, momentum=hparams.momentum),
      "sgd":
          tf.GradientDescentOptimizer(learning_rate)
  }.get(hparams.optimizer)
Get the tf.train.Optimizer for this optimizer string.

  Args:
    learning_rate: The learning_rate tensor.
    hparams: tf.contrib.training.HParams object with the optimizer and
        momentum values.

  Returns:
    optimizer: The tf.train.Optimizer based on the optimizer string.
Get the tf.train.Optimizer for this optimizer string.
Get the tf . train . Optimizer for this optimizer string .
def get_optimizer(learning_rate, hparams):
  """Get the tf.train.Optimizer for this optimizer string.

  Args:
    learning_rate: The learning_rate tensor.
    hparams: tf.contrib.training.HParams object with the optimizer and
        momentum values.

  Returns:
    optimizer: The tf.train.Optimizer based on the optimizer string.
  """
  return {
      "rmsprop":
          tf.RMSPropOptimizer(
              learning_rate,
              decay=0.95,
              momentum=hparams.momentum,
              epsilon=1e-4),
      "adam":
          tf.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8),
      "adagrad":
          tf.AdagradOptimizer(learning_rate, initial_accumulator_value=1.0),
      "mom":
          tf.MomentumOptimizer(learning_rate, momentum=hparams.momentum),
      "sgd":
          tf.GradientDescentOptimizer(learning_rate)
  }.get(hparams.optimizer)
def get_optimizer ( learning_rate , hparams ) : return { "rmsprop" : tf . RMSPropOptimizer ( learning_rate , decay = 0.95 , momentum = hparams . momentum , epsilon = 1e-4 ) , "adam" : tf . AdamOptimizer ( learning_rate , beta1 = 0.9 , beta2 = 0.999 , epsilon = 1e-8 ) , "adagrad" : tf . AdagradOptimizer ( learning_rate , initial_accumulator_value = 1.0 ) , "mom" : tf . MomentumOptimizer ( learning_rate , momentum = hparams . momentum ) , "sgd" : tf . GradientDescentOptimizer ( learning_rate ) } . get ( hparams . optimizer )