AdaBound + AMSBound implementations with Keras

Adabound.py

from keras.optimizers import Optimizer
from keras.legacy import interfaces
from keras import backend as K
import tensorflow as tf


class Adabound(Optimizer):
    def __init__(self, lr=0.001,
                 beta_1=0.9, beta_2=0.999,
                 gamma=0.001,
                 final_lr=0.1,
                 epsilon=None,
                 decay=0.,
                 amsbound=False,
                 **kwargs):
        super().__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.lr = K.variable(lr, name='lr')
            self.beta_1 = K.variable(beta_1, name='beta_1')
            self.beta_2 = K.variable(beta_1, name='beta_2')
            self.gamma = K.variable(gamma, name='gamma')
            self.final_lr = K.variable(final_lr, name='final_lr')
            self.decay = K.variable(decay, name='decay')
        if epsilon is None:
            epsilon = K.epsilon()
        self.epsilon = epsilon
        self.initial_decay = decay
        self.amsbound = amsbound

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr = lr * (1. / (1. + self.decay * K.cast(self.iterations,
                                                      K.dtype(self.decay))))

        t = K.cast(self.iterations, K.floatx()) + 1
        lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
                     (1. - K.pow(self.beta_1, t)))

        ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]

        if self.amsbound:
            vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        else:
            vhats = [K.zeros((1, 1)) for _ in params]

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

        for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
            lower_bound_t = self.final_lr * (1 - 1 / self.gamma * t + 1)
            upper_bound_t = self.final_lr * (1 + 1 / self.gamma * t)

            if self.amsbound:
                vhat_t = K.maximum(vhat, v_t)
                # p_t = p - K.clip(lr_t * (K.sqrt(vhat_t) + self.epsilon),
                #                  lower_bound_t,
                #                  upper_bound_t)
                p_t = p - tf.clip_by_value(
                    lr_t * (K.sqrt(vhat_t) + self.epsilon),
                    lower_bound_t,
                    upper_bound_t)
            else:
                # p_t = p - K.clip(lr_t * (K.sqrt(v_t) + self.epsilon),
                #                  lower_bound_t,
                #                  upper_bound_t)
                p_t = p - tf.clip_by_value(
                    lr_t * (K.sqrt(v_t) + self.epsilon),
                    lower_bound_t,
                    upper_bound_t)

            self.updates.append(K.update(m, m_t))
            self.updates.append(K.update(v, v_t))
            new_p = p_t

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

            self.updates.append(K.update(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)),
                  'gamma': float(K.get_value(self.gamma)),
                  'final_lr': float(K.get_value(self.final_lr)),
                  'decay': float(K.get_value(self.decay)),
                  'epsilon': self.epsilon,
                  'amsbound': self.amsbound}
        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))