114 lines
4.3 KiB
Python
114 lines
4.3 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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"""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.contrib.optimizer_v2 import optimizer_v2
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from tensorflow.python.training import training_ops
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class AdadeltaOptimizer(optimizer_v2.OptimizerV2):
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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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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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To match the exact form in the original paper use 1.0.
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rho: A float hyperparameter. The decay rate.
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epsilon: A float hyperparameter. A constant epsilon used to better
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condition 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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"""
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super(AdadeltaOptimizer, self).__init__(use_locking, name)
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self._set_hyper("learning_rate", learning_rate)
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self._set_hyper("rho", rho)
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self._set_hyper("epsilon", epsilon)
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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, "accum")
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state.zeros_slot(v, "accum_update")
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def _apply_dense(self, grad, var, state):
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accum = state.get_slot(var, "accum")
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accum_update = state.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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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("rho", var.dtype.base_dtype),
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state.get_hyper("epsilon", 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, state):
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accum = state.get_slot(var, "accum")
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accum_update = state.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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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("rho", var.dtype.base_dtype),
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state.get_hyper("epsilon", var.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, state):
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accum = state.get_slot(var, "accum")
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accum_update = state.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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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("rho", var.dtype.base_dtype),
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state.get_hyper("epsilon", 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, state):
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accum = state.get_slot(var, "accum")
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accum_update = state.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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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("rho", var.dtype.base_dtype),
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state.get_hyper("epsilon", var.dtype.base_dtype),
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grad,
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indices,
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use_locking=self._use_locking)
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