255 lines
9.7 KiB
Python
255 lines
9.7 KiB
Python
# Copyright 2018 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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"""Nadam optimizer implementation."""
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import tensorflow.compat.v2 as tf
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from keras import backend_config
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from keras.optimizers.legacy import optimizer_v2
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from keras.optimizers.schedules import learning_rate_schedule
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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.Nadam",
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v1=["keras.optimizers.Nadam", "keras.optimizers.legacy.Nadam"],
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)
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class Nadam(optimizer_v2.OptimizerV2):
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r"""Optimizer that implements the NAdam algorithm.
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Much like Adam is essentially RMSprop with momentum, Nadam is Adam with
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Nesterov momentum.
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Args:
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learning_rate: A Tensor or a floating point value. The learning rate.
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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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name: Optional name for the operations created when applying gradients.
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Defaults to `"Nadam"`.
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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 Example:
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>>> opt = tf.keras.optimizers.legacy.Nadam(learning_rate=0.2)
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>>> var1 = tf.Variable(10.0)
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>>> loss = lambda: (var1 ** 2) / 2.0
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>>> step_count = opt.minimize(loss, [var1]).numpy()
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>>> "{:.1f}".format(var1.numpy())
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9.8
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Reference:
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- [Dozat, 2015](http://cs229.stanford.edu/proj2015/054_report.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=0.001,
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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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name="Nadam",
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**kwargs
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):
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# Backwards compatibility with keras NAdam optimizer.
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kwargs["decay"] = kwargs.pop("schedule_decay", 0.004)
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learning_rate = kwargs.get("lr", learning_rate)
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if isinstance(
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learning_rate, learning_rate_schedule.LearningRateSchedule
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):
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raise ValueError(
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"The Nadam optimizer does not support "
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"tf.keras.optimizers.LearningRateSchedules as the "
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"learning rate."
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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._set_hyper("beta_1", beta_1)
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self._set_hyper("beta_2", beta_2)
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self.epsilon = epsilon or backend_config.epsilon()
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self._m_cache = None
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def _create_slots(self, var_list):
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var_dtype = var_list[0].dtype.base_dtype
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if self._m_cache is None:
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self._m_cache = self.add_weight(
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"momentum_cache",
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shape=[],
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dtype=var_dtype,
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initializer="ones",
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trainable=False,
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aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,
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)
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self._weights.append(self._m_cache)
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# Separate for-loops to respect the ordering of slot variables from v1.
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for var in var_list:
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# Create slots for the first moments.
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self.add_slot(var, "m")
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for var in var_list:
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# Create slots for the second moments.
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self.add_slot(var, "v")
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def _prepare_local(self, var_device, var_dtype, apply_state):
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lr_t = tf.identity(self._get_hyper("learning_rate", var_dtype))
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beta_1_t = tf.identity(self._get_hyper("beta_1", var_dtype))
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beta_2_t = tf.identity(self._get_hyper("beta_2", var_dtype))
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local_step = tf.cast(self.iterations + 1, var_dtype)
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next_step = tf.cast(self.iterations + 2, var_dtype)
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decay_base = tf.cast(0.96, var_dtype)
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m_t = beta_1_t * (
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1.0 - 0.5 * (tf.pow(decay_base, self._initial_decay * local_step))
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)
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m_t_1 = beta_1_t * (
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1.0 - 0.5 * (tf.pow(decay_base, self._initial_decay * next_step))
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)
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m_schedule_new = tf.cast(self._m_cache_read, var_dtype) * m_t
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if var_dtype is self._m_cache.dtype:
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m_schedule_new = tf.identity(
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tf.compat.v1.assign(
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self._m_cache, m_schedule_new, use_locking=self._use_locking
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)
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)
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m_schedule_next = m_schedule_new * m_t_1
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apply_state[(var_device, var_dtype)] = dict(
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lr_t=lr_t,
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neg_lr_t=-lr_t,
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epsilon=tf.convert_to_tensor(self.epsilon, var_dtype),
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beta_1_t=beta_1_t,
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beta_2_t=beta_2_t,
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m_t=m_t,
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m_t_1=m_t_1,
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one_minus_beta_1_t=1 - beta_1_t,
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one_minus_beta_2_t=1 - beta_2_t,
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one_minus_m_t=1.0 - m_t,
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one_minus_m_schedule_new=1.0 - m_schedule_new,
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one_minus_m_schedule_next=1.0 - m_schedule_next,
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v_t_prime_denominator=1.0 - tf.pow(beta_2_t, local_step),
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)
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def _prepare(self, var_list):
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# Get the value of the momentum cache before starting to apply
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# gradients.
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self._m_cache_read = tf.identity(self._m_cache)
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return super()._prepare(var_list)
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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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m = self.get_slot(var, "m")
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v = self.get_slot(var, "v")
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g_prime = grad / coefficients["one_minus_m_schedule_new"]
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m_t = (
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coefficients["beta_1_t"] * m
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+ coefficients["one_minus_beta_1_t"] * grad
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)
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m_t = tf.compat.v1.assign(m, m_t, use_locking=self._use_locking)
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m_t_prime = m_t / coefficients["one_minus_m_schedule_next"]
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v_t = coefficients["beta_2_t"] * v + coefficients[
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"one_minus_beta_2_t"
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] * tf.square(grad)
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v_t = tf.compat.v1.assign(v, v_t, use_locking=self._use_locking)
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v_t_prime = v_t / coefficients["v_t_prime_denominator"]
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m_t_bar = (
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coefficients["one_minus_m_t"] * g_prime
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+ coefficients["m_t_1"] * m_t_prime
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)
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var_t = var - coefficients["lr_t"] * m_t_bar / (
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tf.sqrt(v_t_prime) + coefficients["epsilon"]
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)
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return tf.compat.v1.assign(var, var_t, use_locking=self._use_locking).op
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def _resource_apply_sparse(self, grad, var, indices, 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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m = self.get_slot(var, "m")
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v = self.get_slot(var, "v")
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g_prime = grad / coefficients["one_minus_m_schedule_new"]
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# m_t = beta1 * m + (1 - beta1) * g_t
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m_scaled_g_values = grad * coefficients["one_minus_beta_1_t"]
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m_t = tf.compat.v1.assign(
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m, m * coefficients["beta_1_t"], use_locking=self._use_locking
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)
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with tf.control_dependencies([m_t]):
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m_t = self._resource_scatter_add(m, indices, m_scaled_g_values)
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m_t_slice = tf.gather(m_t, indices)
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m_t_prime = m_t_slice / coefficients["one_minus_m_schedule_next"]
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m_t_bar = (
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coefficients["one_minus_m_t"] * g_prime
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+ coefficients["m_t_1"] * m_t_prime
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)
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# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
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v_scaled_g_values = (grad * grad) * coefficients["one_minus_beta_2_t"]
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v_t = tf.compat.v1.assign(
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v, v * coefficients["beta_2_t"], use_locking=self._use_locking
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)
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with tf.control_dependencies([v_t]):
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v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)
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v_t_slice = tf.gather(v_t, indices)
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v_t_prime = v_t_slice / coefficients["v_t_prime_denominator"]
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v_prime_sqrt_plus_eps = tf.sqrt(v_t_prime) + coefficients["epsilon"]
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var_update = self._resource_scatter_add(
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var,
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indices,
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coefficients["neg_lr_t"] * m_t_bar / v_prime_sqrt_plus_eps,
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)
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return tf.group(*[var_update, m_t_bar, v_t])
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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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"beta_1": self._serialize_hyperparameter("beta_1"),
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"beta_2": self._serialize_hyperparameter("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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