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"""GradientDescent for TensorFlow."""
from tensorflow.python.framework import ops
from tensorflow.python.ops import constant_op
# pylint: disable=unused-import
from tensorflow.python.ops import math_ops
# pylint: enable=unused-import
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
class GradientDescentOptimizer(optimizer.Optimizer):
"""Optimizer that implements the gradient descent algorithm.
@@__init__
"""
def __init__(self, learning_rate, use_locking=False, name="GradientDescent"):
"""Construct a new gradient descent optimizer.
Args:
learning_rate: A Tensor or a floating point value. The learning
rate to use.
use_locking: If True use locks for update operation.s
name: Optional name prefix for the operations created when applying
gradients. Defaults to "GradientDescent".
"""
super(GradientDescentOptimizer, self).__init__(use_locking, name)
self._learning_rate = learning_rate
def _apply_dense(self, grad, var):
return training_ops.apply_gradient_descent(
var,
self._learning_rate_tensor,
grad,
use_locking=self._use_locking).op
def _apply_sparse(self, grad, var):
delta = ops.IndexedSlices(grad.values * self._learning_rate_tensor,
grad.indices, grad.dense_shape)
return var.scatter_sub(delta, use_locking=self._use_locking)
def _prepare(self):
self._learning_rate_tensor = ops.convert_to_tensor(self._learning_rate,
name="learning_rate")
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