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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Adagrad for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
from tensorflow.python.util.tf_export import tf_export
@tf_export("train.AdagradOptimizer")
class AdagradOptimizer(optimizer.Optimizer):
"""Optimizer that implements the Adagrad algorithm.
See this [paper](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
or this
[intro](https://ppasupat.github.io/a9online/uploads/proximal_notes.pdf).
"""
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.
@compatibility(eager)
When eager execution is enabled, `learning_rate` can be a callable that
takes no arguments and returns the actual value to use. This can be useful
for changing these values across different invocations of optimizer
functions.
@end_compatibility
"""
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:
dtype = v.dtype.base_dtype
if v.get_shape().is_fully_defined():
init = init_ops.constant_initializer(self._initial_accumulator_value,
dtype=dtype)
else:
init = self._init_constant_op(v, dtype)
self._get_or_make_slot_with_initializer(v, init, v.get_shape(), dtype,
"accumulator", self._name)
def _init_constant_op(self, v, dtype):
def init():
# Use a Tensor instead of initializer if variable does not have
# static shape.
init_constant = gen_array_ops.fill(array_ops.shape(v),
self._initial_accumulator_value)
return math_ops.cast(init_constant, dtype)
return init
def _prepare(self):
learning_rate = self._call_if_callable(self._learning_rate)
self._learning_rate_tensor = ops.convert_to_tensor(
learning_rate, name="learning_rate")
def _apply_dense(self, grad, var):
acc = self.get_slot(var, "accumulator")
return training_ops.apply_adagrad(
var,
acc,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
grad,
use_locking=self._use_locking)
def _resource_apply_dense(self, grad, var):
acc = self.get_slot(var, "accumulator")
return training_ops.resource_apply_adagrad(
var.handle,
acc.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
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,
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
grad.values,
grad.indices,
use_locking=self._use_locking)
def _resource_apply_sparse(self, grad, var, indices):
acc = self.get_slot(var, "accumulator")
return training_ops.resource_sparse_apply_adagrad(
var.handle,
acc.handle,
math_ops.cast(self._learning_rate_tensor, grad.dtype),
grad,
indices,
use_locking=self._use_locking)
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