"""Adopted from https://github.com/keras-team/keras/issues/3556#issuecomment-440638517""" # noqa
# TODO (TF2.X)
# from keras.legacy import interfaces
import keras.backend as K
from keras.optimizers import Optimizer
[docs]class AdamAccumulate(Optimizer):
def __init__(
self,
lr=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False,
accum_iters=1,
**kwargs
):
if accum_iters < 1:
raise ValueError("accum_iters must be >= 1")
super(AdamAccumulate, self).__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_2, name="beta_2")
self.decay = K.variable(decay, name="decay")
if epsilon is None:
epsilon = K.epsilon()
self.epsilon = epsilon
self.initial_decay = decay
self.amsgrad = amsgrad
self.accum_iters = K.variable(accum_iters, K.dtype(self.iterations))
self.accum_iters_float = K.cast(self.accum_iters, K.floatx())
# TODO (TF2.X)
# @interfaces.legacy_get_updates_support
[docs] def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
completed_updates = K.cast(K.tf.floordiv(self.iterations, self.accum_iters), K.floatx())
if self.initial_decay > 0:
lr = lr * (1.0 / (1.0 + self.decay * completed_updates))
t = completed_updates + 1
lr_t = lr * (K.sqrt(1.0 - K.pow(self.beta_2, t)) / (1.0 - K.pow(self.beta_1, t)))
# self.iterations incremented after processing a batch
# batch: 1 2 3 4 5 6 7 8 9
# self.iterations: 0 1 2 3 4 5 6 7 8
# update_switch = 1: x x (if accum_iters=4)
update_switch = K.equal((self.iterations + 1) % self.accum_iters, 0)
update_switch = K.cast(update_switch, K.floatx())
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]
gs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
if self.amsgrad:
vhats = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
else:
vhats = [K.zeros(1) for _ in params]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat, tg in zip(params, grads, ms, vs, vhats, gs):
sum_grad = tg + g
avg_grad = sum_grad / self.accum_iters_float
m_t = (self.beta_1 * m) + (1.0 - self.beta_1) * avg_grad
v_t = (self.beta_2 * v) + (1.0 - self.beta_2) * K.square(avg_grad)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(
K.update(vhat, (1 - update_switch) * vhat + update_switch * vhat_t)
)
else:
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, (1 - update_switch) * m + update_switch * m_t))
self.updates.append(K.update(v, (1 - update_switch) * v + update_switch * v_t))
self.updates.append(K.update(tg, (1 - update_switch) * sum_grad))
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, (1 - update_switch) * p + update_switch * new_p))
return self.updates
[docs] 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)),
"decay": float(K.get_value(self.decay)),
"epsilon": self.epsilon,
"amsgrad": self.amsgrad,
}
base_config = super(AdamAccumulate, self).get_config()
return dict(list(base_config.items()) + list(config.items()))