119 lines
4.6 KiB
Python
119 lines
4.6 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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"""Adagrad optimizer 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.ops import array_ops
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from tensorflow.python.ops import gen_array_ops
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from tensorflow.python.ops import init_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.training import training_ops
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class AdagradOptimizer(optimizer_v2.OptimizerV2):
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"""Optimizer that implements the Adagrad algorithm.
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See this [paper](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
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or this
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[intro](http://cs.stanford.edu/~ppasupat/a9online/uploads/proximal_notes.pdf).
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"""
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def __init__(self, learning_rate, initial_accumulator_value=0.1,
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use_locking=False, name="Adagrad"):
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"""Construct a new Adagrad optimizer.
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The learning_rate arg below is a hyperparameter, 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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initial_accumulator_value: A floating point value.
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Starting value for the accumulators, must be positive.
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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 "Adagrad".
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Raises:
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ValueError: If the `initial_accumulator_value` is invalid.
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"""
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if initial_accumulator_value <= 0.0:
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raise ValueError("initial_accumulator_value must be positive: %s" %
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initial_accumulator_value)
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super(AdagradOptimizer, self).__init__(use_locking, name)
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self._set_hyper("learning_rate", learning_rate)
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self._initial_accumulator_value = initial_accumulator_value
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def _create_vars(self, var_list, state):
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for v in var_list:
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# TODO(isaprykin): Delete colocate_with(v) from other optimizers and
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# confirm that colocation will happen anyway.
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dtype = v.dtype.base_dtype
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if v.get_shape().is_fully_defined():
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init = init_ops.constant_initializer(self._initial_accumulator_value,
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dtype=dtype)
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else:
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# Use a Tensor instead of initializer if variable does not have static
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# shape.
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init_constant = gen_array_ops.fill(
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array_ops.shape(v), self._initial_accumulator_value)
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init = math_ops.cast(init_constant, dtype)
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state.create_slot_with_initializer(v, init, v.get_shape(), dtype,
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"accumulator")
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def _apply_dense(self, grad, var, state):
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acc = state.get_slot(var, "accumulator")
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return training_ops.apply_adagrad(
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var,
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acc,
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state.get_hyper("learning_rate", 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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acc = state.get_slot(var, "accumulator")
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return training_ops.resource_apply_adagrad(
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var.handle,
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acc.handle,
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state.get_hyper("learning_rate", 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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acc = state.get_slot(var, "accumulator")
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return training_ops.sparse_apply_adagrad(
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var,
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acc,
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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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use_locking=self._use_locking)
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def _resource_apply_sparse(self, grad, var, indices, state):
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acc = state.get_slot(var, "accumulator")
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return training_ops.resource_sparse_apply_adagrad(
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var.handle,
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acc.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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use_locking=self._use_locking)
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