234 lines
9 KiB
Python
234 lines
9 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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"""RMSprop optimizer for Tensorflow.
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rmsprop algorithm [tieleman2012rmsprop]
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A detailed description of rmsprop.
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- maintain a moving (discounted) average of the square of gradients
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- divide gradient by the root of this average
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mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
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mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(mean_square + epsilon)
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delta = - mom
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This implementation of RMSProp uses plain momentum, not Nesterov momentum.
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The centered version additionally maintains a moving (discounted) average of the
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gradients, and uses that average to estimate the variance:
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mean_grad = decay * mean_square{t-1} + (1-decay) * gradient
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mean_square = decay * mean_square{t-1} + (1-decay) * gradient ** 2
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mom = momentum * mom{t-1} + learning_rate * g_t /
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sqrt(mean_square - mean_grad**2 + epsilon)
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delta = - mom
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"""
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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 init_ops
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from tensorflow.python.training import training_ops
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class RMSPropOptimizer(optimizer_v2.OptimizerV2):
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"""Optimizer that implements the RMSProp algorithm.
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See the
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[paper](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
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"""
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def __init__(self,
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learning_rate,
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decay=0.9,
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momentum=0.0,
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epsilon=1e-10,
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use_locking=False,
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centered=False,
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name="RMSProp"):
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"""Construct a new RMSProp optimizer.
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Note that in the dense implementation of this algorithm, variables and their
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corresponding accumulators (momentum, gradient moving average, square
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gradient moving average) will be updated even if the gradient is zero
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(i.e. accumulators will decay, momentum will be applied). The sparse
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implementation (used when the gradient is an `IndexedSlices` object,
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typically because of `tf.gather` or an embedding lookup in the forward pass)
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will not update variable slices or their accumulators unless those slices
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were used in the forward pass (nor is there an "eventual" correction to
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account for these omitted updates). This leads to more efficient updates for
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large embedding lookup tables (where most of the slices are not accessed in
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a particular graph execution), but differs from the published algorithm.
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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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decay: A float hyperparameter. Discounting factor for the history/coming
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gradient.
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momentum: A float hyperparameter.
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epsilon: A float hyperparameter. Small value to avoid zero denominator.
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use_locking: If True use locks for update operation.
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centered: If True, gradients are normalized by the estimated variance of
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the gradient; if False, by the uncentered second moment. Setting this to
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True may help with training, but is slightly more expensive in terms of
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computation and memory. Defaults to False.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to "RMSProp".
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"""
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super(RMSPropOptimizer, self).__init__(use_locking, name)
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self._set_hyper("learning_rate", learning_rate)
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self._set_hyper("decay", decay)
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self._set_hyper("momentum", momentum)
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self._set_hyper("epsilon", epsilon)
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self._centered = centered
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def _create_vars(self, var_list, state):
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for v in var_list:
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if v.get_shape().is_fully_defined():
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init_rms = init_ops.ones_initializer(dtype=v.dtype.base_dtype)
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else:
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init_rms = array_ops.ones_like(v)
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state.create_slot_with_initializer(v, init_rms, v.get_shape(),
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v.dtype.base_dtype, "rms")
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if self._centered:
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state.zeros_slot(v, "mg")
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state.zeros_slot(v, "momentum")
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def _apply_dense(self, grad, var, state):
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rms = state.get_slot(var, "rms")
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mom = state.get_slot(var, "momentum")
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if self._centered:
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mg = state.get_slot(var, "mg")
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return training_ops.apply_centered_rms_prop(
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var,
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mg,
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rms,
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mom,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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).op
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else:
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return training_ops.apply_rms_prop(
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var,
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rms,
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mom,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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).op
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def _resource_apply_dense(self, grad, var, state):
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rms = state.get_slot(var, "rms")
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mom = state.get_slot(var, "momentum")
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if self._centered:
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mg = state.get_slot(var, "mg")
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return training_ops.resource_apply_centered_rms_prop(
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var.handle,
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mg.handle,
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rms.handle,
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mom.handle,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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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else:
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return training_ops.resource_apply_rms_prop(
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var.handle,
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rms.handle,
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mom.handle,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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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rms = state.get_slot(var, "rms")
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mom = state.get_slot(var, "momentum")
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if self._centered:
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mg = state.get_slot(var, "mg")
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return training_ops.sparse_apply_centered_rms_prop(
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var,
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mg,
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rms,
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mom,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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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else:
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return training_ops.sparse_apply_rms_prop(
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var,
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rms,
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mom,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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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rms = state.get_slot(var, "rms")
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mom = state.get_slot(var, "momentum")
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if self._centered:
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mg = self.get_slot(var, "mg")
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return training_ops.resource_sparse_apply_centered_rms_prop(
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var.handle,
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mg.handle,
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rms.handle,
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mom.handle,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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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else:
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return training_ops.resource_sparse_apply_rms_prop(
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var.handle,
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rms.handle,
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mom.handle,
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("decay", var.dtype.base_dtype),
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state.get_hyper("momentum", 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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