223 lines
7.8 KiB
Python
223 lines
7.8 KiB
Python
# Copyright 2020 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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"""SGD optimizer implementation."""
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import tensorflow.compat.v2 as tf
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from keras.optimizers.legacy import optimizer_v2
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export(
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"keras.optimizers.legacy.SGD",
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v1=["keras.optimizers.SGD", "keras.optimizers.legacy.SGD"],
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)
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class SGD(optimizer_v2.OptimizerV2):
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r"""Gradient descent (with momentum) optimizer.
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Update rule for parameter `w` with gradient `g` when `momentum=0`:
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```python
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w = w - learning_rate * g
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```
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Update rule when `momentum` is larger than 0:
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```python
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velocity = momentum * velocity - learning_rate * g
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w = w + velocity
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```
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When `nesterov=True`, this rule becomes:
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```python
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velocity = momentum * velocity - learning_rate * g
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w = w + momentum * velocity - learning_rate * g
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```
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Args:
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learning_rate: A `Tensor`, floating point value, or a schedule that is a
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`tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable
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that takes no arguments and returns the actual value to use. The
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learning rate. Defaults to 0.01.
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momentum: float hyperparameter >= 0 that accelerates gradient descent in
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the relevant direction and dampens oscillations. Defaults to 0, i.e.,
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vanilla gradient descent.
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nesterov: boolean. Whether to apply Nesterov momentum.
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Defaults to `False`.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to `"SGD"`.
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**kwargs: keyword arguments. Allowed arguments are `clipvalue`,
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`clipnorm`, `global_clipnorm`.
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If `clipvalue` (float) is set, the gradient of each weight
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is clipped to be no higher than this value.
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If `clipnorm` (float) is set, the gradient of each weight
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is individually clipped so that its norm is no higher than this value.
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If `global_clipnorm` (float) is set the gradient of all weights is
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clipped so that their global norm is no higher than this value.
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Usage:
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>>> opt = tf.keras.optimizers.legacy.SGD(learning_rate=0.1)
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>>> var = tf.Variable(1.0)
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>>> loss = lambda: (var ** 2)/2.0 # d(loss)/d(var1) = var1
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>>> step_count = opt.minimize(loss, [var]).numpy()
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>>> # Step is `- learning_rate * grad`
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>>> var.numpy()
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0.9
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>>> opt = tf.keras.optimizers.legacy.SGD(learning_rate=0.1, momentum=0.9)
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>>> var = tf.Variable(1.0)
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>>> val0 = var.value()
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>>> loss = lambda: (var ** 2)/2.0 # d(loss)/d(var1) = var1
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>>> # First step is `- learning_rate * grad`
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>>> step_count = opt.minimize(loss, [var]).numpy()
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>>> val1 = var.value()
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>>> (val0 - val1).numpy()
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0.1
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>>> # On later steps, step-size increases because of momentum
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>>> step_count = opt.minimize(loss, [var]).numpy()
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>>> val2 = var.value()
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>>> (val1 - val2).numpy()
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0.18
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Reference:
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- For `nesterov=True`, See [Sutskever et al., 2013](
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https://github.com/mlresearch/v28/blob/gh-pages/sutskever13.pdf).
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"""
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_HAS_AGGREGATE_GRAD = True
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def __init__(
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self,
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learning_rate=0.01,
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momentum=0.0,
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nesterov=False,
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name="SGD",
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**kwargs,
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):
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super().__init__(name, **kwargs)
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self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
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self._set_hyper("decay", self._initial_decay)
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self._momentum = False
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if (
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isinstance(momentum, tf.Tensor)
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or callable(momentum)
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or momentum > 0
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):
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self._momentum = True
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if isinstance(momentum, (int, float)) and (
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momentum < 0 or momentum > 1
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):
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raise ValueError(
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"`momentum` must be between [0, 1]. Received: "
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f"momentum={momentum} (of type {type(momentum)})."
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)
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self._set_hyper("momentum", momentum)
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self.nesterov = nesterov
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def _create_slots(self, var_list):
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if self._momentum:
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for var in var_list:
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self.add_slot(var, "momentum")
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def _prepare_local(self, var_device, var_dtype, apply_state):
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super()._prepare_local(var_device, var_dtype, apply_state)
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apply_state[(var_device, var_dtype)]["momentum"] = tf.identity(
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self._get_hyper("momentum", var_dtype)
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)
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def _resource_apply_dense(self, grad, var, apply_state=None):
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var_device, var_dtype = var.device, var.dtype.base_dtype
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coefficients = (apply_state or {}).get(
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(var_device, var_dtype)
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) or self._fallback_apply_state(var_device, var_dtype)
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if self._momentum:
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momentum_var = self.get_slot(var, "momentum")
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return tf.raw_ops.ResourceApplyKerasMomentum(
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var=var.handle,
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accum=momentum_var.handle,
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lr=coefficients["lr_t"],
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grad=grad,
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momentum=coefficients["momentum"],
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use_locking=self._use_locking,
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use_nesterov=self.nesterov,
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)
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else:
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return tf.raw_ops.ResourceApplyGradientDescent(
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var=var.handle,
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alpha=coefficients["lr_t"],
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delta=grad,
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use_locking=self._use_locking,
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)
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def _resource_apply_sparse_duplicate_indices(
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self, grad, var, indices, **kwargs
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):
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if self._momentum:
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return super()._resource_apply_sparse_duplicate_indices(
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grad, var, indices, **kwargs
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)
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else:
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var_device, var_dtype = var.device, var.dtype.base_dtype
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coefficients = kwargs.get("apply_state", {}).get(
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(var_device, var_dtype)
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) or self._fallback_apply_state(var_device, var_dtype)
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return tf.raw_ops.ResourceScatterAdd(
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resource=var.handle,
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indices=indices,
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updates=-grad * coefficients["lr_t"],
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)
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def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
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# This method is only needed for momentum optimization.
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var_device, var_dtype = var.device, var.dtype.base_dtype
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coefficients = (apply_state or {}).get(
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(var_device, var_dtype)
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) or self._fallback_apply_state(var_device, var_dtype)
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momentum_var = self.get_slot(var, "momentum")
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return tf.raw_ops.ResourceSparseApplyKerasMomentum(
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var=var.handle,
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accum=momentum_var.handle,
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lr=coefficients["lr_t"],
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grad=grad,
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indices=indices,
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momentum=coefficients["momentum"],
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use_locking=self._use_locking,
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use_nesterov=self.nesterov,
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)
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def get_config(self):
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config = super().get_config()
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config.update(
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{
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"learning_rate": self._serialize_hyperparameter(
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"learning_rate"
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),
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"decay": self._initial_decay,
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"momentum": self._serialize_hyperparameter("momentum"),
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"nesterov": self.nesterov,
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}
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)
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return config
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