Add powerSign optimizer to deepchem

    return tf.train.GradientDescentOptimizer(learning_rate=learning_rate)

class PowerSignOptimizer(Optimizer):

  """The new powersign optimization algorithm"""

  def __init__(self, learning_rate=0.1, beta=0.9):

    """construct a powerSign optimizer

    Parameters

    ----------

    learning_rate : float or LearningRateSchedule 

    the learning rate to use for optimization

    beta : A float Parameter for the PowerSign algorithm

    """

    self.learning_rate = learning_rate

    self.beta = beta

  def _create_optimizer(self, global_step):

    if isinstance(self.learning_rate, LearningRateSchedule):

      learning_rate = self.learning_rate._create_tensor(global_step)

    else:

      learning_rate = self.learning_rate

    return tf.contrib.opt.PowerSignOptimizer(

        learning_rate=learning_rate, beta=self.beta)

class ExponentialDecay(LearningRateSchedule):

  """A learning rate that decreases exponentially with the number of training steps."""