opt4.py

class PowerSign(optimizer.Optimizer):
    """Implementation of PowerSign.
    See [Bello et. al., 2017](https://arxiv.org/abs/1709.07417)
    @@__init__
    """
    def __init__(self, learning_rate=0.001,alpha=0.01,beta=0.5, use_locking=False, name="PowerSign"):
        super(PowerSign, self).__init__(use_locking, name)
        self._lr = learning_rate
        self._alpha = alpha
        self._beta = beta
        
        # Tensor versions of the constructor arguments, created in _prepare().
        self._lr_t = None
        self._alpha_t = None
        self._beta_t = None

    def _prepare(self):
        self._lr_t = ops.convert_to_tensor(self._lr, name="learning_rate")
        self._alpha_t = ops.convert_to_tensor(self._beta, name="alpha_t")
        self._beta_t = ops.convert_to_tensor(self._beta, name="beta_t")

    def _create_slots(self, var_list):
        # Create slots for the first and second moments.
        for v in var_list:
            self._zeros_slot(v, "m", self._name)

    def _apply_dense(self, grad, var):
        lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
        alpha_t = math_ops.cast(self._alpha_t, var.dtype.base_dtype)
        beta_t = math_ops.cast(self._beta_t, var.dtype.base_dtype)

        eps = 1e-7 #cap for moving average
        
        m = self.get_slot(var, "m")
        m_t = m.assign(tf.maximum(beta_t * m + eps, tf.abs(grad)))

        var_update = state_ops.assign_sub(var, lr_t*grad*tf.exp( tf.log(alpha_t)*tf.sign(grad)*tf.sign(m_t))) #Update 'ref' by subtracting 'value
        #Create an op that groups multiple operations.
        #When this op finishes, all ops in input have finished
        return control_flow_ops.group(*[var_update, m_t])

     def _apply_sparse(self, grad, var):
        raise NotImplementedError("Sparse gradient updates are not supported.")

Hi,

Thank you for sharing the code! I have a question regarding to the variable update.

According to the update function shared in https://towardsdatascience.com/custom-optimizer-in-tensorflow-d5b41f75644a, for power sign function, there is an additional term f_n in the function which I could not find in the code itself.(referring to line 37) Could you please guide me to understand where is it calculated in the code?

P.S. Same term (f_n) is there in the update function of AddSign optimizer but I could not see it in the code.

Thank you for your time.

Best,

Aysegul Bumin