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"""One-line documentation for rmsprop module.
rmsprop algorithm [tieleman2012rmsprop]
A detailed description of rmsprop.
- maintain a moving (discounted) average of the square of gradients
- divide gradient by the root of this average
mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(mean_square + epsilon)
delta = - mom
"""
from tensorflow.python.framework import ops
from tensorflow.python.ops import constant_op
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class RMSPropOptimizer(optimizer.Optimizer):
"""Optimizer that implements the RMSProp algorithm.
@@__init__
"""
def __init__(self, learning_rate, decay, momentum=0.0, epsilon=1e-10,
use_locking=False, name="RMSProp"):
"""Construct a new RMSProp optimizer.
Args:
learning_rate: A Tensor or a floating point value. The learning rate.
decay: discounting factor for the history/coming gradient
momentum: a scalar tensor.
epsilon: small value to avoid zero denominator.
use_locking: If True use locks for update operation.
name: Optional name prefic for the operations created when applying
gradients. Defaults to "RMSProp".
"""
super(RMSPropOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
self._decay = decay
self._momentum = momentum
self._epsilon = epsilon
# Tensors for learning rate and momentum. Created in _prepare.
self._learning_rate_tensor = None
self._decay_tensor = None
self._momentum_tensor = None
self._epsilon_tensor = None
def _create_slots(self, var_list):
for v in var_list:
self._get_or_make_slot(
v, constant_op.constant(1.0, dtype=v.dtype, shape=v.get_shape()),
"rms", self._name)
self._zeros_slot(v, "momentum", self._name)
def _prepare(self):
self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
name="learning_rate")
self._decay_tensor = ops.convert_to_tensor(self._decay, name="decay")
self._momentum_tensor = ops.convert_to_tensor(self._momentum,
name="momentum")
self._epsilon_tensor = ops.convert_to_tensor(self._epsilon,
name="epsilon")
def _apply_dense(self, grad, var):
rms = self.get_slot(var, "rms")
mom = self.get_slot(var, "momentum")
return training_ops.apply_rms_prop(
var, rms, mom,
self._learning_rate_tensor,
self._decay_tensor,
self._momentum_tensor,
self._epsilon_tensor,
grad, use_locking=self._use_locking).op
def _apply_sparse(self, grad, var):
raise NotImplementedError()
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