270 lines
12 KiB
Python
270 lines
12 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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"""Ftrl-proximal 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 constant_op
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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.FtrlOptimizer")
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class FtrlOptimizer(optimizer.Optimizer):
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"""Optimizer that implements the FTRL algorithm.
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See this [paper](
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https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
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This version has support for both online L2 (the L2 penalty given in the paper
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above) and shrinkage-type L2 (which is the addition of an L2 penalty to the
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loss function).
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"""
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def __init__(self,
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learning_rate,
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learning_rate_power=-0.5,
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initial_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="Ftrl",
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accum_name=None,
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linear_name=None,
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l2_shrinkage_regularization_strength=0.0):
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r"""Construct a new FTRL optimizer.
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Args:
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learning_rate: A float value or a constant float `Tensor`.
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learning_rate_power: A float value, must be less or equal to zero.
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initial_accumulator_value: The starting value for accumulators.
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Only zero or positive values are allowed.
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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 "Ftrl".
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accum_name: The suffix for the variable that keeps the gradient squared
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accumulator. If not present, defaults to name.
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linear_name: The suffix for the variable that keeps the linear gradient
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accumulator. If not present, defaults to name + "_1".
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l2_shrinkage_regularization_strength: A float value, must be greater than
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or equal to zero. This differs from L2 above in that the L2 above is a
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stabilization penalty, whereas this L2 shrinkage is a magnitude penalty.
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The FTRL formulation can be written as:
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w_{t+1} = argmin_w(\hat{g}_{1:t}w + L1*||w||_1 + L2*||w||_2^2), where
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\hat{g} = g + (2*L2_shrinkage*w), and g is the gradient of the loss
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function w.r.t. the weights w.
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Specifically, in the absence of L1 regularization, it is equivalent to
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the following update rule:
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w_{t+1} = w_t - lr_t / (1 + 2*L2*lr_t) * g_t -
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2*L2_shrinkage*lr_t / (1 + 2*L2*lr_t) * w_t
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where lr_t is the learning rate at t.
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When input is sparse shrinkage will only happen on the active weights.
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Raises:
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ValueError: If one of the arguments is invalid.
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"""
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super(FtrlOptimizer, self).__init__(use_locking, name)
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if initial_accumulator_value < 0.0:
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raise ValueError(
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"initial_accumulator_value %f needs to be be positive or zero" %
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initial_accumulator_value)
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if learning_rate_power > 0.0:
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raise ValueError("learning_rate_power %f needs to be negative or zero" %
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learning_rate_power)
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if l1_regularization_strength < 0.0:
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raise ValueError(
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"l1_regularization_strength %f needs to be positive or zero" %
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l1_regularization_strength)
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if l2_regularization_strength < 0.0:
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raise ValueError(
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"l2_regularization_strength %f needs to be positive or zero" %
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l2_regularization_strength)
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if l2_shrinkage_regularization_strength < 0.0:
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raise ValueError(
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"l2_shrinkage_regularization_strength %f needs to be positive"
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" or zero" % l2_shrinkage_regularization_strength)
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self._learning_rate = learning_rate
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self._learning_rate_power = learning_rate_power
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self._initial_accumulator_value = initial_accumulator_value
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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._l2_shrinkage_regularization_strength = (
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l2_shrinkage_regularization_strength)
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self._learning_rate_tensor = None
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self._learning_rate_power_tensor = None
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self._l1_regularization_strength_tensor = None
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self._l2_regularization_strength_tensor = None
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self._l2_shrinkage_regularization_strength_tensor = None
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self._accum_name = accum_name
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self._linear_name = linear_name
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def _create_slots(self, var_list):
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# Create the "accum" and "linear" slots.
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for v in var_list:
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with ops.colocate_with(v):
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val = constant_op.constant(
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self._initial_accumulator_value, dtype=v.dtype, shape=v.get_shape())
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self._get_or_make_slot(v, val, "accum", self._accum_name or self._name)
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self._zeros_slot(v, "linear", self._linear_name or 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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self._l1_regularization_strength_tensor = ops.convert_to_tensor(
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self._l1_regularization_strength, name="l1_regularization_strength")
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self._l2_regularization_strength_tensor = ops.convert_to_tensor(
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self._l2_regularization_strength, name="l2_regularization_strength")
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self._l2_shrinkage_regularization_strength_tensor = ops.convert_to_tensor(
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self._l2_shrinkage_regularization_strength,
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name="l2_shrinkage_regularization_strength")
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self._learning_rate_power_tensor = ops.convert_to_tensor(
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self._learning_rate_power, name="learning_rate_power")
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def _apply_dense(self, grad, var):
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accum = self.get_slot(var, "accum")
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linear = self.get_slot(var, "linear")
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if self._l2_shrinkage_regularization_strength <= 0.0:
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return training_ops.apply_ftrl(
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var,
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accum,
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linear,
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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_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
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use_locking=self._use_locking)
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else:
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return training_ops.apply_ftrl_v2(
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var,
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accum,
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linear,
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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_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
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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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linear = self.get_slot(var, "linear")
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if self._l2_shrinkage_regularization_strength <= 0.0:
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return training_ops.resource_apply_ftrl(
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var.handle,
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accum.handle,
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linear.handle,
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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_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
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use_locking=self._use_locking)
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else:
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return training_ops.resource_apply_ftrl_v2(
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var.handle,
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accum.handle,
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linear.handle,
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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_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
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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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linear = self.get_slot(var, "linear")
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if self._l2_shrinkage_regularization_strength <= 0.0:
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return training_ops.sparse_apply_ftrl(
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var,
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accum,
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linear,
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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_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
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use_locking=self._use_locking)
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else:
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return training_ops.sparse_apply_ftrl_v2(
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var,
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accum,
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linear,
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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_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_regularization_strength_tensor,
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var.dtype.base_dtype),
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math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
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grad.dtype.base_dtype),
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math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
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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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linear = self.get_slot(var, "linear")
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if self._l2_shrinkage_regularization_strength <= 0.0:
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return training_ops.resource_sparse_apply_ftrl(
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var.handle,
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accum.handle,
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linear.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_tensor, grad.dtype),
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math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
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math_ops.cast(self._learning_rate_power_tensor, grad.dtype),
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use_locking=self._use_locking)
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else:
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return training_ops.resource_sparse_apply_ftrl_v2(
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var.handle,
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accum.handle,
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linear.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_tensor, grad.dtype),
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math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
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math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
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grad.dtype),
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math_ops.cast(self._learning_rate_power_tensor, grad.dtype),
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use_locking=self._use_locking)
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