172 lines
7.4 KiB
Python
172 lines
7.4 KiB
Python
# Copyright 2016 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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"""Adagrad Dual Averaging for TensorFlow."""
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gen_training_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.util.tf_export import tf_export
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@tf_export(v1=["train.AdagradDAOptimizer"])
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class AdagradDAOptimizer(optimizer.Optimizer):
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"""Adagrad Dual Averaging algorithm for sparse linear models.
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This optimizer takes care of regularization of unseen features in a mini batch
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by updating them when they are seen with a closed form update rule that is
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equivalent to having updated them on every mini-batch.
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AdagradDA is typically used when there is a need for large sparsity in the
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trained model. This optimizer only guarantees sparsity for linear models. Be
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careful when using AdagradDA for deep networks as it will require careful
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initialization of the gradient accumulators for it to train.
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References:
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
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:[Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
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([pdf](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf))
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"""
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def __init__(self,
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learning_rate,
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global_step,
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initial_gradient_squared_accumulator_value=0.1,
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l1_regularization_strength=0.0,
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l2_regularization_strength=0.0,
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use_locking=False,
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name="AdagradDA"):
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"""Construct a new AdagradDA 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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global_step: A `Tensor` containing the current training step number.
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initial_gradient_squared_accumulator_value: A floating point value.
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Starting value for the accumulators, must be positive.
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l1_regularization_strength: A float value, must be greater than or
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equal to zero.
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l2_regularization_strength: A float value, must be greater than or
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equal to zero.
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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 "AdagradDA".
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Raises:
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ValueError: If the `initial_gradient_squared_accumulator_value` is
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invalid.
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"""
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if initial_gradient_squared_accumulator_value <= 0.0:
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raise ValueError("initial_gradient_squared_accumulator_value must be "
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"positive: %s" %
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initial_gradient_squared_accumulator_value)
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super(AdagradDAOptimizer, self).__init__(use_locking, name)
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self._learning_rate = learning_rate
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self._initial_gradient_squared_accumulator_value = (
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initial_gradient_squared_accumulator_value)
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# Created in Initialize.
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self._learning_rate_tensor = None
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self._l1_regularization_strength = l1_regularization_strength
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self._l2_regularization_strength = l2_regularization_strength
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self._global_step = global_step
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self._global_step_on_worker = None
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def _create_slots(self, var_list):
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for v in var_list:
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with ops.colocate_with(v):
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g_val = constant_op.constant(
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0.0, shape=v.get_shape(), dtype=v.dtype.base_dtype)
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gg_val = constant_op.constant(
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self._initial_gradient_squared_accumulator_value,
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shape=v.get_shape(),
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dtype=v.dtype.base_dtype)
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self._get_or_make_slot(v, g_val, "gradient_accumulator", self._name)
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self._get_or_make_slot(v, gg_val, "gradient_squared_accumulator",
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self._name)
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def _prepare(self):
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self._learning_rate_tensor = ops.convert_to_tensor(
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self._learning_rate, name="learning_rate")
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# Performance optimization so that worker creates a copy of the global step
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# to avoid overloading the parameter server holding the global step.
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with ops.colocate_with(self._learning_rate_tensor):
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self._global_step_on_worker = array_ops.identity(self._global_step) + 1
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def _apply_dense(self, grad, var):
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g_acc = self.get_slot(var, "gradient_accumulator")
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gg_acc = self.get_slot(var, "gradient_squared_accumulator")
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with ops.device(var.device):
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global_step = array_ops.identity(self._global_step_on_worker)
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return gen_training_ops.apply_adagrad_da(
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var,
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g_acc,
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gg_acc,
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grad,
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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math_ops.cast(self._l1_regularization_strength, var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength, var.dtype.base_dtype),
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global_step,
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use_locking=self._use_locking)
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def _resource_apply_dense(self, grad, var):
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g_acc = self.get_slot(var, "gradient_accumulator")
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gg_acc = self.get_slot(var, "gradient_squared_accumulator")
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with ops.device(var.device):
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global_step = array_ops.identity(self._global_step_on_worker)
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return gen_training_ops.resource_apply_adagrad_da(
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var.handle,
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g_acc.handle,
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gg_acc.handle,
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grad,
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math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
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math_ops.cast(self._l1_regularization_strength, grad.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength, grad.dtype.base_dtype),
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global_step,
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use_locking=self._use_locking)
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def _apply_sparse(self, grad, var):
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g_acc = self.get_slot(var, "gradient_accumulator")
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gg_acc = self.get_slot(var, "gradient_squared_accumulator")
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with ops.device(var.device):
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global_step = array_ops.identity(self._global_step_on_worker)
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return gen_training_ops.sparse_apply_adagrad_da(
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var,
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g_acc,
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gg_acc,
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grad.values,
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grad.indices,
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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math_ops.cast(self._l1_regularization_strength, var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength, var.dtype.base_dtype),
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global_step,
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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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g_acc = self.get_slot(var, "gradient_accumulator")
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gg_acc = self.get_slot(var, "gradient_squared_accumulator")
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with ops.device(var.device):
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global_step = array_ops.identity(self._global_step_on_worker)
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return gen_training_ops.resource_sparse_apply_adagrad_da(
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var.handle,
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g_acc.handle,
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gg_acc.handle,
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grad,
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indices,
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math_ops.cast(self._learning_rate_tensor, grad.dtype),
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math_ops.cast(self._l1_regularization_strength, grad.dtype),
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math_ops.cast(self._l2_regularization_strength, grad.dtype),
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global_step,
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use_locking=self._use_locking)
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