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"""Adagrad for TensorFlow."""
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 AdagradOptimizer(optimizer.Optimizer):
"""Optimizer that implements the Adagrad algorithm.
@@__init__
"""
def __init__(self, learning_rate, initial_accumulator_value=0.1,
use_locking=False, name="Adagrad"):
"""Construct a new Adagrad optimizer.
Args:
learning_rate: A `Tensor` or a floating point value. The learning rate.
initial_accumulator_value: A floating point value.
Starting value for the accumulators, must be positive.
use_locking: If `True` use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Adagrad".
Raises:
ValueError: If the initial_accumulator_value is invalid.
"""
if initial_accumulator_value <= 0.0:
raise ValueError("initial_accumulator_value must be positive: %s" %
initial_accumulator_value)
super(AdagradOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
self._initial_accumulator_value = initial_accumulator_value
# Created in Initialize.
self._learning_rate_tensor = None
def _create_slots(self, var_list):
for v in var_list:
val = constant_op.constant(self._initial_accumulator_value,
shape=v.get_shape())
self._get_or_make_slot(v, val, "accumulator", self._name)
def _prepare(self):
self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
name="learning_rate")
def _apply_dense(self, grad, var):
acc = self.get_slot(var, "accumulator")
return training_ops.apply_adagrad(
var, acc, self._learning_rate_tensor, grad,
use_locking=self._use_locking)
def _apply_sparse(self, grad, var):
acc = self.get_slot(var, "accumulator")
return training_ops.sparse_apply_adagrad(
var, acc, self._learning_rate_tensor, grad.values, grad.indices,
use_locking=self._use_locking)
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