125 lines
4.8 KiB
Python
125 lines
4.8 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Momentum for TensorFlow."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.optimizer_v2 import optimizer_v2
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from tensorflow.python.training import training_ops
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class MomentumOptimizer(optimizer_v2.OptimizerV2):
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"""Optimizer that implements the Momentum algorithm.
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Computes (if `use_nesterov = False`):
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```
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accumulation = momentum * accumulation + gradient
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variable -= learning_rate * accumulation
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```
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Note that in the dense version of this algorithm, `accumulation` is updated
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and applied regardless of a gradient's value, whereas the sparse version (when
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the gradient is an `IndexedSlices`, typically because of `tf.gather` or an
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embedding) only updates variable slices and corresponding `accumulation` terms
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when that part of the variable was used in the forward pass.
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"""
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def __init__(self, learning_rate, momentum,
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use_locking=False, name="Momentum", use_nesterov=False):
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"""Construct a new Momentum optimizer.
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Some of the args below are hyperparameters, where a hyperparameter is
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defined as a scalar Tensor, a regular Python value or a callable (which
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will be evaluated when `apply_gradients` is called) returning a scalar
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Tensor or a Python value.
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Args:
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learning_rate: A float hyperparameter. The learning rate.
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momentum: A float hyperparameter. The momentum.
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use_locking: If `True` use locks for update operations.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to "Momentum".
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use_nesterov: If `True` use Nesterov Momentum.
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See [Sutskever et al., 2013](
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http://jmlr.org/proceedings/papers/v28/sutskever13.pdf).
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This implementation always computes gradients at the value of the
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variable(s) passed to the optimizer. Using Nesterov Momentum makes the
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variable(s) track the values called `theta_t + mu*v_t` in the paper.
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@compatibility(eager)
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When eager execution is enabled, learning_rate and momentum can each be a
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callable that takes no arguments and returns the actual value to use. This
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can be useful for changing these values across different invocations of
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optimizer functions.
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@end_compatibility
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"""
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super(MomentumOptimizer, self).__init__(use_locking, name)
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self._set_hyper("learning_rate", learning_rate)
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self._set_hyper("momentum", momentum)
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self._use_nesterov = use_nesterov
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def _create_vars(self, var_list, state):
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for v in var_list:
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state.zeros_slot(v, "momentum")
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def _apply_dense(self, grad, var, state):
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mom = state.get_slot(var, "momentum")
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return training_ops.apply_momentum(
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var,
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mom,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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grad,
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state.get_hyper("momentum", var.dtype.base_dtype),
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use_locking=self._use_locking,
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use_nesterov=self._use_nesterov).op
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def _resource_apply_dense(self, grad, var, state):
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mom = state.get_slot(var, "momentum")
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return training_ops.resource_apply_momentum(
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var.handle,
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mom.handle,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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grad,
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state.get_hyper("momentum", var.dtype.base_dtype),
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use_locking=self._use_locking,
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use_nesterov=self._use_nesterov)
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def _apply_sparse(self, grad, var, state):
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mom = state.get_slot(var, "momentum")
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return training_ops.sparse_apply_momentum(
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var,
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mom,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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grad.values,
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grad.indices,
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state.get_hyper("momentum", var.dtype.base_dtype),
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use_locking=self._use_locking,
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use_nesterov=self._use_nesterov).op
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def _resource_apply_sparse(self, grad, var, indices, state):
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mom = state.get_slot(var, "momentum")
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return training_ops.resource_sparse_apply_momentum(
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var.handle,
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mom.handle,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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grad,
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indices,
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state.get_hyper("momentum", var.dtype.base_dtype),
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use_locking=self._use_locking,
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use_nesterov=self._use_nesterov)
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