Created
September 4, 2018 12:00
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Padam Keras Optimizer
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from keras import backend as K | |
from keras.optimizers import Optimizer | |
class Padam(Optimizer): | |
def __init__(self, lr=1e-1, beta_1=0.9, beta_2=0.999, | |
epsilon=1e-8, decay=0., amsgrad=False, partial=1. / 4., **kwargs): | |
""" Partially adaptive momentum estimation optimizer. | |
# Arguments | |
lr: float >= 0. Learning rate. | |
beta_1: float, 0 < beta < 1. Generally close to 1. | |
beta_2: float, 0 < beta < 1. Generally close to 1. | |
epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`. | |
decay: float >= 0. Learning rate decay over each update. | |
amsgrad: boolean. Whether to apply the AMSGrad variant of this | |
algorithm from the paper "On the Convergence of Adam and | |
Beyond". | |
partial: float >=0. Parameter controlling partial momentum adaption. | |
# References | |
- [Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks](https://arxiv.org/pdf/1806.06763.pdf) | |
""" | |
super(Padam, 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.partial = partial | |
self.initial_decay = decay | |
self.amsgrad = amsgrad | |
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 *= (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.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 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) | |
if self.amsgrad: | |
vhat_t = K.maximum(vhat, v_t) | |
denom = (K.sqrt(vhat_t) + self.epsilon) | |
self.updates.append(K.update(vhat, vhat_t)) | |
else: | |
denom = (K.sqrt(v_t) + self.epsilon) | |
self.updates.append(K.update(m, m_t)) | |
self.updates.append(K.update(v, v_t)) | |
# Partial momentum adaption. | |
p_t = p - (lr_t * (m_t / (denom ** (self.partial * 2)))) | |
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)), | |
'decay': float(K.get_value(self.decay)), | |
'epsilon': self.epsilon, | |
'amsgrad': self.amsgrad} | |
base_config = super(Padam, self).get_config() | |
return dict(list(base_config.items()) + list(config.items())) |
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