199 lines
8.6 KiB
Python
199 lines
8.6 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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"""Adam 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.framework import ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.training import training_ops
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class AdamOptimizer(optimizer_v2.OptimizerV2):
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"""Optimizer that implements the Adam algorithm.
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See [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
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([pdf](http://arxiv.org/pdf/1412.6980.pdf)).
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"""
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def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8,
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use_locking=False, name="Adam"):
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"""Construct a new Adam optimizer.
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Initialization:
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$$m_0 := 0 (Initialize initial 1st moment vector)$$
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$$v_0 := 0 (Initialize initial 2nd moment vector)$$
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$$t := 0 (Initialize timestep)$$
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The update rule for `variable` with gradient `g` uses an optimization
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described at the end of section2 of the paper:
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$$t := t + 1$$
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$$lr_t := \text{learning_rate} * \sqrt{(1 - beta_2^t) / (1 - beta_1^t)}$$
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$$m_t := beta_1 * m_{t-1} + (1 - beta_1) * g$$
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$$v_t := beta_2 * v_{t-1} + (1 - beta_2) * g * g$$
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$$variable := variable - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$
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The default value of 1e-8 for epsilon might not be a good default in
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general. For example, when training an Inception network on ImageNet a
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current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the
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formulation just before Section 2.1 of the Kingma and Ba paper rather than
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the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon
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hat" in the paper.
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The sparse implementation of this algorithm (used when the gradient is an
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IndexedSlices object, typically because of `tf.gather` or an embedding
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lookup in the forward pass) does apply momentum to variable slices even if
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they were not used in the forward pass (meaning they have a gradient equal
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to zero). Momentum decay (beta1) is also applied to the entire momentum
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accumulator. This means that the sparse behavior is equivalent to the dense
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behavior (in contrast to some momentum implementations which ignore momentum
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unless a variable slice was actually used).
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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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beta1: A float hyperparameter. The exponential decay rate for the 1st
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moment estimates.
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beta2: A float hyperparameter. The exponential decay rate for the 2nd
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moment estimates.
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epsilon: A float hyperparameter. This epsilon is "epsilon hat" in the
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Kingma and Ba paper (in the formula just before Section 2.1), not the
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epsilon in Algorithm 1 of the paper.
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use_locking: If True use locks for update operations.
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name: Optional name for the operations created when applying gradients.
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Defaults to "Adam".
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"""
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super(AdamOptimizer, self).__init__(use_locking, name)
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self._set_hyper("learning_rate", learning_rate)
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self._set_hyper("beta1", beta1)
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self._set_hyper("beta2", beta2)
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self._set_hyper("epsilon", epsilon)
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def _get_beta_accumulators(self, state=None):
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if state is None:
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state = self._get_per_graph_state()
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return (state.get_non_slot("beta1_power"),
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state.get_non_slot("beta2_power"))
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def _create_vars(self, var_list, state):
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# Non-slot variables end up on the same device(s).
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state.create_non_slot(initial_value=lambda: state.get_hyper("beta1"),
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name="beta1_power")
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state.create_non_slot(initial_value=lambda: state.get_hyper("beta2"),
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name="beta2_power")
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# Create slots for the first and second moments.
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for v in var_list:
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state.zeros_slot(v, "m")
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state.zeros_slot(v, "v")
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def _apply_dense(self, grad, var, state):
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m = state.get_slot(var, "m")
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v = state.get_slot(var, "v")
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beta1_power, beta2_power = self._get_beta_accumulators(state)
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return training_ops.apply_adam(
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var, m, v,
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math_ops.cast(beta1_power, var.dtype.base_dtype),
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math_ops.cast(beta2_power, var.dtype.base_dtype),
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state.get_hyper("learning_rate", var.dtype.base_dtype),
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state.get_hyper("beta1", var.dtype.base_dtype),
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state.get_hyper("beta2", var.dtype.base_dtype),
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state.get_hyper("epsilon", var.dtype.base_dtype),
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grad, use_locking=self._use_locking).op
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def _resource_apply_dense(self, grad, var, state):
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m = state.get_slot(var, "m")
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v = state.get_slot(var, "v")
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beta1_power, beta2_power = self._get_beta_accumulators(state)
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return training_ops.resource_apply_adam(
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var.handle, m.handle, v.handle,
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math_ops.cast(beta1_power, grad.dtype.base_dtype),
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math_ops.cast(beta2_power, grad.dtype.base_dtype),
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state.get_hyper("learning_rate", grad.dtype.base_dtype),
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state.get_hyper("beta1", grad.dtype.base_dtype),
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state.get_hyper("beta2", grad.dtype.base_dtype),
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state.get_hyper("epsilon", grad.dtype.base_dtype),
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grad, use_locking=self._use_locking)
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def _apply_sparse_shared(self, grad, var, indices, scatter_add, state):
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beta1_power, beta2_power = self._get_beta_accumulators(state)
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beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype)
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beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype)
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lr_t = state.get_hyper("learning_rate", var.dtype.base_dtype)
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beta1_t = state.get_hyper("beta1", var.dtype.base_dtype)
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beta2_t = state.get_hyper("beta2", var.dtype.base_dtype)
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epsilon_t = state.get_hyper("epsilon", var.dtype.base_dtype)
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lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
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# m_t = beta1 * m + (1 - beta1) * g_t
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m = state.get_slot(var, "m")
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m_scaled_g_values = grad * (1 - beta1_t)
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m_t = state_ops.assign(m, m * beta1_t,
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use_locking=self._use_locking)
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with ops.control_dependencies([m_t]):
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m_t = scatter_add(m, indices, m_scaled_g_values)
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# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
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v = state.get_slot(var, "v")
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v_scaled_g_values = (grad * grad) * (1 - beta2_t)
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v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking)
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with ops.control_dependencies([v_t]):
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v_t = scatter_add(v, indices, v_scaled_g_values)
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v_sqrt = math_ops.sqrt(v_t)
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var_update = state_ops.assign_sub(var,
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lr * m_t / (v_sqrt + epsilon_t),
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use_locking=self._use_locking)
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return control_flow_ops.group(*[var_update, m_t, v_t])
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def _apply_sparse(self, grad, var, state):
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return self._apply_sparse_shared(
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grad.values, var, grad.indices,
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lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda
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x, i, v, use_locking=self._use_locking),
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state)
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def _resource_scatter_add(self, x, i, v):
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with ops.control_dependencies(
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[resource_variable_ops.resource_scatter_add(
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x.handle, i, v)]):
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return x.value()
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def _resource_apply_sparse(self, grad, var, indices, state):
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return self._apply_sparse_shared(
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grad, var, indices, self._resource_scatter_add, state)
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def _finish(self, state):
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# Update the power accumulators.
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beta1_power, beta2_power = self._get_beta_accumulators(state)
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update_beta1 = beta1_power.assign(
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beta1_power * state.get_hyper("beta1"),
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use_locking=self._use_locking)
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update_beta2 = beta2_power.assign(
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beta2_power * state.get_hyper("beta2"),
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use_locking=self._use_locking)
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return control_flow_ops.group(update_beta1, update_beta2)
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