168 lines
5.9 KiB
Python
168 lines
5.9 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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"""Adadelta 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.experimental.Adadelta", "keras.optimizers.Adadelta", v1=[]
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)
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class Adadelta(optimizer.Optimizer):
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r"""Optimizer that implements the Adadelta algorithm.
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Adadelta optimization is a stochastic gradient descent method that is based
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on adaptive learning rate per dimension to address two drawbacks:
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- The continual decay of learning rates throughout training.
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- The need for a manually selected global learning rate.
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Adadelta is a more robust extension of Adagrad that adapts learning rates
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based on a moving window of gradient updates, instead of accumulating all
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past gradients. This way, Adadelta continues learning even when many updates
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have been done. Compared to Adagrad, in the original version of Adadelta you
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don't have to set an initial learning rate. In this version, the initial
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learning rate can be set, as in most other Keras optimizers.
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Args:
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learning_rate: Initial value for the learning rate: either a floating
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point value, or a `tf.keras.optimizers.schedules.LearningRateSchedule`
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instance. Defaults to 0.001. Note that `Adadelta` tends to benefit from
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higher initial learning rate values compared to other optimizers. To
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match the exact form in the original paper, use 1.0.
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rho: A `Tensor` or a floating point value. The decay rate. Defaults to
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0.95.
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epsilon: Small floating point value used to maintain numerical stability.
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Defaults to 1e-7.
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{{base_optimizer_keyword_args}}
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Reference:
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- [Zeiler, 2012](http://arxiv.org/abs/1212.5701)
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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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rho=0.95,
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epsilon=1e-7,
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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="Adadelta",
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**kwargs
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):
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super().__init__(
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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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name=name,
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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.rho = rho
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self.epsilon = epsilon
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def build(self, var_list):
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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._accumulated_grads = []
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self._accumulated_delta_vars = []
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for var in var_list:
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self._accumulated_grads.append(
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self.add_variable_from_reference(var, "accumulated_grad")
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)
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self._accumulated_delta_vars.append(
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self.add_variable_from_reference(var, "accumulated_delta_var")
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)
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def update_step(self, grad, variable):
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"""Update step given gradient and the associated model variable."""
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lr = tf.cast(self.learning_rate, variable.dtype)
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var_key = self._var_key(variable)
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rho = self.rho
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accumulated_grad = self._accumulated_grads[self._index_dict[var_key]]
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accumulated_delta_var = self._accumulated_delta_vars[
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self._index_dict[var_key]
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]
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def rms(x):
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return tf.sqrt(x + self.epsilon)
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if isinstance(grad, tf.IndexedSlices):
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# Sparse gradients.
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accumulated_grad.assign_add((rho - 1) * accumulated_grad)
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accumulated_grad.scatter_add(
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tf.IndexedSlices(
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(1 - rho) * tf.square(grad.values), grad.indices
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)
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)
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delta_var = (
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-rms(accumulated_delta_var) * grad / rms(accumulated_grad)
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)
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accumulated_delta_var.assign(
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rho * accumulated_delta_var + (1 - rho) * delta_var * delta_var
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)
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else:
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# Dense gradients.
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accumulated_grad.assign(
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rho * accumulated_grad + (1 - rho) * grad * grad
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)
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delta_var = (
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-rms(accumulated_delta_var) * grad / rms(accumulated_grad)
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)
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accumulated_delta_var.assign(
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rho * accumulated_delta_var + (1 - rho) * delta_var * delta_var
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)
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variable.assign_add(lr * delta_var)
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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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"rho": self.rho,
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"epsilon": self.epsilon,
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}
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)
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return config
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Adadelta.__doc__ = Adadelta.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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