222 lines
8.4 KiB
Python
222 lines
8.4 KiB
Python
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# Copyright 2021 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 implementation."""
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import tensorflow.compat.v2 as tf
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from keras.optimizers import optimizer
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from keras.saving.object_registration import register_keras_serializable
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@register_keras_serializable()
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@keras_export(
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"keras.optimizers.Adam", "keras.optimizers.experimental.Adam", v1=[]
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)
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class Adam(optimizer.Optimizer):
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r"""Optimizer that implements the Adam algorithm.
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Adam optimization is a stochastic gradient descent method that is based on
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adaptive estimation of first-order and second-order moments.
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According to
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[Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
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the method is "*computationally
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efficient, has little memory requirement, invariant to diagonal rescaling of
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gradients, and is well suited for problems that are large in terms of
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data/parameters*".
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Args:
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learning_rate: A `tf.Tensor`, floating point value, 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 0.001.
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beta_1: A float value or a constant float tensor, or a callable
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that takes no arguments and returns the actual value to use. The
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exponential decay rate for the 1st moment estimates. Defaults to 0.9.
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beta_2: A float value or a constant float tensor, or a callable
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that takes no arguments and returns the actual value to use. The
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exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
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epsilon: A small constant for numerical stability. This epsilon is
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"epsilon hat" in the Kingma and Ba paper (in the formula just before
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Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to
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1e-7.
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amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm from
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the paper "On the Convergence of Adam and beyond". Defaults to `False`.
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{{base_optimizer_keyword_args}}
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Reference:
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- [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
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- [Reddi et al., 2018](
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https://openreview.net/pdf?id=ryQu7f-RZ) for `amsgrad`.
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Notes:
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The default value of 1e-7 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 Adam 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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"""
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def __init__(
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self,
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learning_rate=0.001,
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beta_1=0.9,
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beta_2=0.999,
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epsilon=1e-7,
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amsgrad=False,
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weight_decay=None,
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clipnorm=None,
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clipvalue=None,
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global_clipnorm=None,
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use_ema=False,
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ema_momentum=0.99,
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ema_overwrite_frequency=None,
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jit_compile=True,
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name="Adam",
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**kwargs
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):
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super().__init__(
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name=name,
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weight_decay=weight_decay,
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clipnorm=clipnorm,
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clipvalue=clipvalue,
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global_clipnorm=global_clipnorm,
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use_ema=use_ema,
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ema_momentum=ema_momentum,
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ema_overwrite_frequency=ema_overwrite_frequency,
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jit_compile=jit_compile,
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**kwargs
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)
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self._learning_rate = self._build_learning_rate(learning_rate)
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self.beta_1 = beta_1
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self.beta_2 = beta_2
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self.epsilon = epsilon
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self.amsgrad = amsgrad
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def build(self, var_list):
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"""Initialize optimizer variables.
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Adam optimizer has 3 types of variables: momentums, velocities and
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velocity_hat (only set when amsgrad is applied),
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Args:
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var_list: list of model variables to build Adam variables on.
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"""
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super().build(var_list)
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if hasattr(self, "_built") and self._built:
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return
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self._built = True
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self._momentums = []
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self._velocities = []
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for var in var_list:
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self._momentums.append(
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self.add_variable_from_reference(
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model_variable=var, variable_name="m"
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)
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)
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self._velocities.append(
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self.add_variable_from_reference(
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model_variable=var, variable_name="v"
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)
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)
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if self.amsgrad:
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self._velocity_hats = []
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for var in var_list:
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self._velocity_hats.append(
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self.add_variable_from_reference(
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model_variable=var, variable_name="vhat"
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)
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)
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def update_step(self, gradient, variable):
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"""Update step given gradient and the associated model variable."""
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beta_1_power = None
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beta_2_power = None
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lr = tf.cast(self.learning_rate, variable.dtype)
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local_step = tf.cast(self.iterations + 1, variable.dtype)
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beta_1_power = tf.pow(tf.cast(self.beta_1, variable.dtype), local_step)
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beta_2_power = tf.pow(tf.cast(self.beta_2, variable.dtype), local_step)
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var_key = self._var_key(variable)
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m = self._momentums[self._index_dict[var_key]]
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v = self._velocities[self._index_dict[var_key]]
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alpha = lr * tf.sqrt(1 - beta_2_power) / (1 - beta_1_power)
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if isinstance(gradient, tf.IndexedSlices):
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# Sparse gradients.
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m.assign_add(-m * (1 - self.beta_1))
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m.scatter_add(
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tf.IndexedSlices(
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gradient.values * (1 - self.beta_1), gradient.indices
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)
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)
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v.assign_add(-v * (1 - self.beta_2))
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v.scatter_add(
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tf.IndexedSlices(
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tf.square(gradient.values) * (1 - self.beta_2),
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gradient.indices,
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)
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)
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if self.amsgrad:
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v_hat = self._velocity_hats[self._index_dict[var_key]]
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v_hat.assign(tf.maximum(v_hat, v))
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v = v_hat
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variable.assign_sub((m * alpha) / (tf.sqrt(v) + self.epsilon))
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else:
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# Dense gradients.
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m.assign_add((gradient - m) * (1 - self.beta_1))
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v.assign_add((tf.square(gradient) - v) * (1 - self.beta_2))
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if self.amsgrad:
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v_hat = self._velocity_hats[self._index_dict[var_key]]
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v_hat.assign(tf.maximum(v_hat, v))
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v = v_hat
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variable.assign_sub((m * alpha) / (tf.sqrt(v) + self.epsilon))
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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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self._learning_rate
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),
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"beta_1": self.beta_1,
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"beta_2": self.beta_2,
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"epsilon": self.epsilon,
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"amsgrad": self.amsgrad,
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}
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)
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return config
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Adam.__doc__ = Adam.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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