mirror of
https://github.com/wassname/keras-contrib.git
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85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
from __future__ import absolute_import
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from keras.optimizers import Optimizer
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from .. import backend as K
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from keras.utils.generic_utils import get_custom_objects
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from keras.legacy import interfaces
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class FTML(Optimizer):
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"""FTML optimizer.
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# Arguments
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lr: float >= 0. Learning rate.
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beta_1: float, 0 < beta < 1. Generally close to 0.5.
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beta_2: float, 0 < beta < 1. Generally close to 1.
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epsilon: float >= 0. Fuzz factor.
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decay: float >= 0. Learning rate decay over each update.
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# References
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- [FTML - Follow the Moving Leader in Deep Learning](http://www.cse.ust.hk/~szhengac/papers/icml17.pdf)
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"""
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def __init__(self, lr=0.0025, beta_1=0.6, beta_2=0.999,
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epsilon=1e-8, decay=0., **kwargs):
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super(FTML, self).__init__(**kwargs)
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self.__dict__.update(locals())
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self.iterations = K.variable(0)
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self.lr = K.variable(lr)
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self.beta_1 = K.variable(beta_1)
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self.beta_2 = K.variable(beta_2)
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self.decay = K.variable(decay)
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self.epsilon = epsilon
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self.inital_decay = decay
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@interfaces.legacy_get_updates_support
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def get_updates(self, loss, params):
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grads = self.get_gradients(loss, params)
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self.updates = [K.update_add(self.iterations, 1)]
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lr = self.lr
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if self.inital_decay > 0:
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lr *= (1. / (1. + self.decay * self.iterations))
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t = self.iterations + 1
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lr_t = lr / (1. - K.pow(self.beta_1, t))
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shapes = [K.get_variable_shape(p) for p in params]
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zs = [K.zeros(shape) for shape in shapes]
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vs = [K.zeros(shape) for shape in shapes]
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ds = [K.zeros(shape) for shape in shapes]
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self.weights = [self.iterations] + zs + vs + ds
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for p, g, z, v, d in zip(params, grads, zs, vs, ds):
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v_t = self.beta_2 * v + (1. - self.beta_2) * K.square(g)
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d_t = (K.sqrt(v_t / (1. - K.pow(self.beta_2, t))) + self.epsilon) / lr_t
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sigma_t = d_t - self.beta_1 * d
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z_t = self.beta_1 * z + (1. - self.beta_1) * g - sigma_t * p
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p_t = - z_t / d_t
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self.updates.append(K.update(z, z_t))
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self.updates.append(K.update(v, v_t))
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self.updates.append(K.update(d, d_t))
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new_p = p_t
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# Apply constraints.
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if getattr(p, 'constraint', None) is not None:
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new_p = p.constraint(new_p)
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self.updates.append(K.update(p, new_p))
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return self.updates
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def get_config(self):
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config = {'lr': float(K.get_value(self.lr)),
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'beta_1': float(K.get_value(self.beta_1)),
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'beta_2': float(K.get_value(self.beta_2)),
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'decay': float(K.get_value(self.decay)),
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'epsilon': self.epsilon}
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base_config = super(FTML, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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get_custom_objects().update({'FTML': FTML})
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