139 lines
5.7 KiB
Python
139 lines
5.7 KiB
Python
# 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.python.framework import ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.training import optimizer
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from tensorflow.python.training import training_ops
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from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=["train.MomentumOptimizer"])
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class MomentumOptimizer(optimizer.Optimizer):
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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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Args:
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learning_rate: A `Tensor` or a floating point value. The learning rate.
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momentum: A `Tensor` or a floating point value. 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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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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This implementation is an approximation of the original formula, valid
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for high values of momentum. It will compute the "adjusted gradient"
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in NAG by assuming that the new gradient will be estimated by the
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current average gradient plus the product of momentum and the change
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in the average gradient.
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References:
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On the importance of initialization and momentum in deep learning:
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[Sutskever et al., 2013]
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(http://proceedings.mlr.press/v28/sutskever13.html)
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([pdf](http://proceedings.mlr.press/v28/sutskever13.pdf))
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@compatibility(eager)
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When eager execution is enabled, `learning_rate` and `momentum` can each be
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a 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._learning_rate = learning_rate
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self._momentum = momentum
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self._use_nesterov = use_nesterov
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def _create_slots(self, var_list):
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for v in var_list:
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self._zeros_slot(v, "momentum", self._name)
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def _prepare(self):
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learning_rate = self._learning_rate
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if callable(learning_rate):
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learning_rate = learning_rate()
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self._learning_rate_tensor = ops.convert_to_tensor(learning_rate,
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name="learning_rate")
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momentum = self._momentum
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if callable(momentum):
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momentum = momentum()
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self._momentum_tensor = ops.convert_to_tensor(momentum, name="momentum")
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def _apply_dense(self, grad, var):
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mom = self.get_slot(var, "momentum")
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return training_ops.apply_momentum(
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var, mom,
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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grad,
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math_ops.cast(self._momentum_tensor, 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):
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mom = self.get_slot(var, "momentum")
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return training_ops.resource_apply_momentum(
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var.handle, mom.handle,
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math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
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grad,
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math_ops.cast(self._momentum_tensor, grad.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):
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mom = self.get_slot(var, "momentum")
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return training_ops.sparse_apply_momentum(
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var, mom,
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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grad.values, grad.indices,
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math_ops.cast(self._momentum_tensor, 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):
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mom = self.get_slot(var, "momentum")
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return training_ops.resource_sparse_apply_momentum(
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var.handle, mom.handle,
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math_ops.cast(self._learning_rate_tensor, grad.dtype),
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grad, indices,
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math_ops.cast(self._momentum_tensor, grad.dtype),
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use_locking=self._use_locking,
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use_nesterov=self._use_nesterov)
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