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from tensorflow.keras.optimizers import Optimizer
from tensorflow.keras import backend as K
class AccumOptimizer(Optimizer):
"""Optimizer
Inheriting Optimizer class, wrapping the original optimizer
to achieve a new corresponding optimizer of gradient accumulation.
# Arguments
optimizer: an instance of keras optimizer (supporting
all keras optimizers currently available);
steps_per_update: the steps of gradient accumulation
# Returns
a new keras optimizer.
"""
def __init__(self, optimizer, steps_per_update=1, **kwargs):
super(AccumOptimizer, self).__init__(**kwargs)
self.optimizer = optimizer
with K.name_scope(self.__class__.__name__):
self.steps_per_update = steps_per_update
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.cond = K.equal(self.iterations % self.steps_per_update, 0)
self.lr = self.optimizer.lr
self.optimizer.lr = K.switch(self.cond, self.optimizer.lr, 0.)
for attr in ['momentum', 'rho', 'beta_1', 'beta_2']:
if hasattr(self.optimizer, attr):
value = getattr(self.optimizer, attr)
setattr(self, attr, value)
setattr(self.optimizer, attr, K.switch(self.cond, value, 1 - 1e-7))
for attr in self.optimizer.get_config():
if not hasattr(self, attr):
value = getattr(self.optimizer, attr)
setattr(self, attr, value)
# Cover the original get_gradients method with accumulative gradients.
def get_gradients(loss, params):
return [ag / self.steps_per_update for ag in self.accum_grads]
self.optimizer.get_gradients = get_gradients
def get_updates(self, loss, params):
self.updates = [
K.update_add(self.iterations, 1),
K.update_add(self.optimizer.iterations, K.cast(self.cond, 'int64')),
]
# gradient accumulation
self.accum_grads = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
grads = self.get_gradients(loss, params)
for g, ag in zip(grads, self.accum_grads):
self.updates.append(K.update(ag, K.switch(self.cond, g, ag + g)))
# inheriting updates of original optimizer
self.updates.extend(self.optimizer.get_updates(loss, params)[1:])
self.weights.extend(self.optimizer.weights)
return self.updates
def get_config(self):
iterations = K.eval(self.iterations)
K.set_value(self.iterations, 0)
config = self.optimizer.get_config()
K.set_value(self.iterations, iterations)
return config
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