133 lines
4.9 KiB
Python
133 lines
4.9 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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"""Adadelta 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("train.AdadeltaOptimizer")
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class AdadeltaOptimizer(optimizer.Optimizer):
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"""Optimizer that implements the Adadelta algorithm.
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See [M. D. Zeiler](http://arxiv.org/abs/1212.5701)
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([pdf](http://arxiv.org/pdf/1212.5701v1.pdf))
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"""
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def __init__(self, learning_rate=0.001, rho=0.95, epsilon=1e-8,
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use_locking=False, name="Adadelta"):
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"""Construct a new Adadelta 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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To match the exact form in the original paper use 1.0.
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rho: A `Tensor` or a floating point value. The decay rate.
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epsilon: A `Tensor` or a floating point value. A constant epsilon used
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to better conditioning the grad update.
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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 "Adadelta".
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@compatibility(eager)
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When eager execution is enabled, `learning_rate`, `rho`, and `epsilon` can
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each be a callable that takes no arguments and returns the actual value to
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use. This can be useful for changing these values across different
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invocations of optimizer functions.
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@end_compatibility
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"""
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super(AdadeltaOptimizer, self).__init__(use_locking, name)
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self._lr = learning_rate
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self._rho = rho
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self._epsilon = epsilon
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# Tensor versions of the constructor arguments, created in _prepare().
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self._lr_t = None
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self._rho_t = None
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self._epsilon_t = None
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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, "accum", self._name)
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self._zeros_slot(v, "accum_update", self._name)
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def _prepare(self):
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lr = self._call_if_callable(self._lr)
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rho = self._call_if_callable(self._rho)
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epsilon = self._call_if_callable(self._epsilon)
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self._lr_t = ops.convert_to_tensor(lr, name="lr")
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self._rho_t = ops.convert_to_tensor(rho, name="rho")
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self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon")
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def _apply_dense(self, grad, var):
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accum = self.get_slot(var, "accum")
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accum_update = self.get_slot(var, "accum_update")
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return training_ops.apply_adadelta(
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var,
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accum,
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accum_update,
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math_ops.cast(self._lr_t, var.dtype.base_dtype),
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math_ops.cast(self._rho_t, var.dtype.base_dtype),
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math_ops.cast(self._epsilon_t, var.dtype.base_dtype),
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grad,
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use_locking=self._use_locking)
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def _resource_apply_dense(self, grad, var):
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accum = self.get_slot(var, "accum")
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accum_update = self.get_slot(var, "accum_update")
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return training_ops.resource_apply_adadelta(
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var.handle,
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accum.handle,
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accum_update.handle,
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math_ops.cast(self._lr_t, grad.dtype.base_dtype),
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math_ops.cast(self._rho_t, grad.dtype.base_dtype),
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math_ops.cast(self._epsilon_t, grad.dtype.base_dtype),
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grad,
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use_locking=self._use_locking)
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def _apply_sparse(self, grad, var):
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accum = self.get_slot(var, "accum")
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accum_update = self.get_slot(var, "accum_update")
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return training_ops.sparse_apply_adadelta(
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var,
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accum,
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accum_update,
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math_ops.cast(self._lr_t, var.dtype.base_dtype),
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math_ops.cast(self._rho_t, var.dtype.base_dtype),
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math_ops.cast(self._epsilon_t, var.dtype.base_dtype),
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grad.values,
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grad.indices,
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use_locking=self._use_locking)
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def _resource_apply_sparse(self, grad, var, indices):
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accum = self.get_slot(var, "accum")
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accum_update = self.get_slot(var, "accum_update")
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return training_ops.resource_sparse_apply_adadelta(
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var.handle,
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accum.handle,
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accum_update.handle,
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math_ops.cast(self._lr_t, grad.dtype),
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math_ops.cast(self._rho_t, grad.dtype),
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math_ops.cast(self._epsilon_t, grad.dtype),
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grad,
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indices,
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use_locking=self._use_locking)
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