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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.
# ==============================================================================
"""Momentum for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.keras.optimizer_v2 import sgd
from tensorflow.python.util import deprecation
class MomentumOptimizer(sgd.SGD):
"""Optimizer that implements the Momentum algorithm.
Computes (if `use_nesterov = False`):
```
accumulation = momentum * accumulation + gradient
variable -= learning_rate * accumulation
```
Note that in the dense version of this algorithm, `accumulation` is updated
and applied regardless of a gradient's value, whereas the sparse version (when
the gradient is an `IndexedSlices`, typically because of `tf.gather` or an
embedding) only updates variable slices and corresponding `accumulation` terms
when that part of the variable was used in the forward pass.
"""
@deprecation.deprecated_args(
"2018-10-01",
"`use_locking = True` is no longer supported and will be ignored.",
("use_locking", [False]))
def __init__(self, learning_rate, momentum,
use_locking=False, name="Momentum", use_nesterov=False):
"""Construct a new Momentum optimizer.
Some of the args below are hyperparameters, where a hyperparameter is
defined as a scalar Tensor, a regular Python value or a callable (which
will be evaluated when `apply_gradients` is called) returning a scalar
Tensor or a Python value.
Args:
learning_rate: A float hyperparameter. The learning rate.
momentum: A float hyperparameter. The momentum.
use_locking: If `True` use locks for update operations.
name: Optional name prefix for the operations created when applying
gradients. Defaults to "Momentum".
use_nesterov: If `True` use Nesterov Momentum.
See [Sutskever et al., 2013](
http://jmlr.org/proceedings/papers/v28/sutskever13.pdf).
This implementation always computes gradients at the value of the
variable(s) passed to the optimizer. Using Nesterov Momentum makes the
variable(s) track the values called `theta_t + mu*v_t` in the paper.
@compatibility(eager)
When eager execution is enabled, learning_rate and momentum can each 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
"""
super(MomentumOptimizer, self).__init__(
learning_rate=learning_rate,
momentum=momentum,
name=name,
nesterov=use_nesterov)
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