148 lines
5.1 KiB
Python
148 lines
5.1 KiB
Python
# 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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"""Adagrad optimizer implementation."""
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import tensorflow.compat.v2 as tf
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from keras import initializers
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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.Adagrad", "keras.optimizers.Adagrad", v1=[]
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)
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class Adagrad(optimizer.Optimizer):
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r"""Optimizer that implements the Adagrad algorithm.
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Adagrad is an optimizer with parameter-specific learning rates,
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which are adapted relative to how frequently a parameter gets
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updated during training. The more updates a parameter receives,
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the smaller the updates.
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Args:
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learning_rate: Initial value for the learning rate:
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either a floating point value,
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or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance.
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Defaults to 0.001.
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Note that `Adagrad` tends to benefit from higher initial learning rate
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values compared to other optimizers.
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To match the exact form in the original paper, use 1.0.
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initial_accumulator_value: Floating point value.
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Starting value for the accumulators (per-parameter momentum values).
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Must be non-negative.
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epsilon: Small floating point value used to maintain numerical stability.
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{{base_optimizer_keyword_args}}
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Reference:
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- [Duchi et al., 2011](
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http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
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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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initial_accumulator_value=0.1,
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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="Adagrad",
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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.initial_accumulator_value = initial_accumulator_value
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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._accumulators = []
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initializer = initializers.Constant(self.initial_accumulator_value)
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for var in var_list:
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self._accumulators.append(
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self.add_variable_from_reference(
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var,
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"accumulator",
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initial_value=initializer(shape=var.shape, dtype=var.dtype),
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)
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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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accumulator = self._accumulators[self._index_dict[var_key]]
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if isinstance(grad, tf.IndexedSlices):
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# Sparse gradients.
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accumulator.scatter_add(
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tf.IndexedSlices(grad.values * grad.values, grad.indices)
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)
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sparse_accumulator = tf.gather(accumulator, indices=grad.indices)
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sparse_denominator = tf.sqrt(sparse_accumulator + self.epsilon)
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variable.scatter_add(
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tf.IndexedSlices(
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-lr * grad.values / sparse_denominator, grad.indices
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)
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)
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else:
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# Dense gradients.
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accumulator.assign_add(grad * grad)
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variable.assign_sub(lr * grad / tf.sqrt(accumulator + 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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"initial_accumulator_value": self.initial_accumulator_value,
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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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Adagrad.__doc__ = Adagrad.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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