@tf_export('keras.optimizers.adamsss')
class Adamsss(Optimizer):

 def __init__(self,
  lr=0.002,
  beta_1=0.9,
  beta_2=0.999,
  epsilon=None,
  schedule_decay=0.004,
  **kwargs):
 super(Adamsss, self).__init__(**kwargs)
 with K.name_scope(self.__class__.__name__):
 self.iterations = K.variable(0, dtype='int64', name='iterations')
 self.m_schedule = K.variable(1., name='m_schedule')
 self.lr = K.variable(lr, name='lr')
 self.beta_1 = K.variable(beta_1, name='beta_1')
 self.beta_2 = K.variable(beta_2, name='beta_2')
 if epsilon is None:
 epsilon = K.epsilon()
 self.epsilon = epsilon
 self.schedule_decay = schedule_decay

 def get_updates(self, loss, params):
 grads = self.get_gradients(loss, params)
 self.updates = [state_ops.assign_add(self.iterations, 1)]

 t = math_ops.cast(self.iterations, K.floatx()) + 1

 # Due to the recommendations in [2], i.e. warming momentum schedule
 momentum_cache_t = self.beta_1 * (
 1. - 0.5 *
 (math_ops.pow(K.cast_to_floatx(0.96), t * self.schedule_decay)))
 momentum_cache_t_1 = self.beta_1 * (
 1. - 0.5 *
 (math_ops.pow(K.cast_to_floatx(0.96), (t + 1) * self.schedule_decay)))
 m_schedule_new = self.m_schedule * momentum_cache_t
 m_schedule_next = self.m_schedule * momentum_cache_t * momentum_cache_t_1
 self.updates.append((self.m_schedule, m_schedule_new))

 shapes = [K.int_shape(p) for p in params]
 ms = [K.zeros(shape) for shape in shapes]
 vs = [K.zeros(shape) for shape in shapes]

 self.weights = [self.iterations] + ms + vs

 for p, g, m, v in zip(params, grads, ms, vs):
 # the following equations given in [1]
 g_prime = g / (1. - m_schedule_new)
 m_t = self.beta_1 * m + (1. - self.beta_1) * g
 m_t_prime = m_t / (1. - m_schedule_next)
 v_t = self.beta_2 * v + (1. - self.beta_2) * math_ops.square(g)
 v_t_prime = v_t / (1. - math_ops.pow(self.beta_2, t))
 m_t_bar = (
  1. - momentum_cache_t) * g_prime + momentum_cache_t_1 * m_t_prime

 self.updates.append(state_ops.assign(m, m_t))
 self.updates.append(state_ops.assign(v, v_t))

 p_t = p - self.lr * m_t_bar / (K.sqrt(v_t_prime) + self.epsilon)
 new_p = p_t

 # Apply constraints.
 if getattr(p, 'constraint', None) is not None:
 new_p = p.constraint(new_p)

 self.updates.append(state_ops.assign(p, new_p))
 return self.updates

 def get_config(self):
 config = {
 'lr': float(K.get_value(self.lr)),
 'beta_1': float(K.get_value(self.beta_1)),
 'beta_2': float(K.get_value(self.beta_2)),
 'epsilon': self.epsilon,
 'schedule_decay': self.schedule_decay
 }
 base_config = super(Adamsss, self).get_config()
 return dict(list(base_config.items()) + list(config.items()))

# Aliases.

sgd = SGD
rmsprop = RMSprop
adagrad = Adagrad
adadelta = Adadelta
adam = Adam
adamsss = Adamsss
adamax = Adamax
nadam = Nadam

def deserialize(config, custom_objects=None):
 """Inverse of the `serialize` function.

 Arguments:
 config: Optimizer configuration dictionary.
 custom_objects: Optional dictionary mapping
  names (strings) to custom objects
  (classes and functions)
  to be considered during deserialization.

 Returns:
 A Keras Optimizer instance.
 """
 if tf2.enabled():
 all_classes = {
 'adadelta': adadelta_v2.Adadelta,
 'adagrad': adagrad_v2.Adagrad,
 'adam': adam_v2.Adam,
		'adamsss': adamsss_v2.Adamsss,
 'adamax': adamax_v2.Adamax,
 'nadam': nadam_v2.Nadam,
 'rmsprop': rmsprop_v2.RMSprop,
 'sgd': gradient_descent_v2.SGD
 }
 else:
 all_classes = {
 'adadelta': Adadelta,
 'adagrad': Adagrad,
 'adam': Adam,
 'adamax': Adamax,
 'nadam': Nadam,
		'adamsss': Adamsss,
 'rmsprop': RMSprop,
 'sgd': SGD,
 'tfoptimizer': TFOptimizer
 }

from keras import optimizers
 
model = Sequential()
model.add(Dense(64, kernel_initializer='uniform', input_shape=(10,)))
model.add(Activation('softmax'))
 
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)