added hingeloss

    return result

class Hingeloss():

  def __init__(

      self,

      in_layers=None,

      weights_initializer=tf.contrib.layers.variance_scaling_initializer,

      batch_size,

      labels,

      reg,

      **kwargs):

class Hingeloss():

  def __init__(self,in_layers=None,weights_initializer=tf.contrib.layers.variance_scaling_initializer,batch_size,labels,reg,**kwargs):

    self.weights = weights

    self.weights_initializer=weights_initializer

    self.batch_size=batch_size

    self.labels=labels

    self.reg=reg

    super(Hingeloss,self).__init__(in_layers,**kwargs)

  def create_tensor(self,in_layers=None,set_tensors=True,**kwargs):

    inputs=self.get_input_tensors(in_layers)

    weights, batch_size, labels, reg = inputs[0], inputs[1], inputs[2], inputs[

        3]

    weights,batch_size,labels,reg=inputs[0],inputs[1],inputs[2],inputs[3]

    scores=tf.matmul(batch_size,weights)

    labels_i=scores[np.arange(scores.shape[0]),labels]

    loss_i=tf.maximum(0,scores-tf.transpose(labels_i)+1)