189 lines
6.4 KiB
Python
189 lines
6.4 KiB
Python
# Copyright 2022 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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"""Adamax 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.Adamax", "keras.optimizers.Adamax", v1=[]
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)
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class Adamax(optimizer.Optimizer):
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"""Optimizer that implements the Adamax algorithm.
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Adamax, a variant of Adam based on the infinity norm, is a first-order
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gradient-based optimization method. Due to its capability of adjusting the
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learning rate based on data characteristics, it is suited to learn
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time-variant process, e.g., speech data with dynamically changed noise
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conditions. Default parameters follow those provided in the paper (see
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references below).
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Initialization:
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```python
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m = 0 # Initialize initial 1st moment vector
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u = 0 # Initialize the exponentially weighted infinity norm
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t = 0 # Initialize timestep
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```
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The update rule for parameter `w` with gradient `g` is described at the end
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of section 7.1 of the paper (see the referenece section):
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```python
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t += 1
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m = beta1 * m + (1 - beta) * g
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u = max(beta2 * u, abs(g))
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current_lr = learning_rate / (1 - beta1 ** t)
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w = w - current_lr * m / (u + epsilon)
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```
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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. The exponential decay
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rate for the 1st moment estimates.
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beta_2: A float value or a constant float tensor. The exponential decay
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rate for the exponentially weighted infinity norm.
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epsilon: A small constant for numerical stability.
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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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"""
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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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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="Adamax",
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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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def build(self, var_list):
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"""Initialize optimizer variables.
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Adamax optimizer has 2 types of variables: momentums (denoted as m),
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exponentially weighted infinity norm (denoted as u).
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Args:
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var_list: list of model variables to build Adamax 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._m = []
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self._u = []
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for var in var_list:
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self._m.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._u.append(
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self.add_variable_from_reference(
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model_variable=var, variable_name="u"
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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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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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var_key = self._var_key(variable)
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m = self._m[self._index_dict[var_key]]
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u = self._u[self._index_dict[var_key]]
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if isinstance(gradient, tf.IndexedSlices):
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# Sparse gradients.
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indices = gradient.indices
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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(gradient.values * (1 - self.beta_1), indices)
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)
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u.assign(u * self.beta_2)
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u_slice = tf.gather(u, indices)
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u_slice_incremental = (
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tf.maximum(u_slice, tf.abs(gradient.values)) - u_slice
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)
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u.scatter_add(tf.IndexedSlices(u_slice_incremental, indices))
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variable.assign_sub(
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(lr * m) / ((1 - beta_1_power) * (u + self.epsilon))
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)
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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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u.assign(tf.maximum(self.beta_2 * u, tf.abs(gradient)))
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variable.assign_sub(
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(lr * m) / ((1 - beta_1_power) * (u + self.epsilon))
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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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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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}
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)
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return config
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Adamax.__doc__ = Adamax.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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