202 lines
7.9 KiB
Python
202 lines
7.9 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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"""Adamax 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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# 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.Adamax",
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v1=["keras.optimizers.Adamax", "keras.optimizers.legacy.Adamax"],
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)
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class Adamax(optimizer_v2.OptimizerV2):
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"""Optimizer that implements the Adamax algorithm.
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It is a variant of Adam based on the infinity norm.
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Default parameters follow those provided in the paper.
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Adamax is sometimes superior to adam, specially in models with embeddings.
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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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v = 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
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described at the end of section 7.1 of the paper:
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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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v = max(beta2 * v, abs(g))
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current_lr = learning_rate / (1 - beta1 ** t)
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w = w - current_lr * m / (v + epsilon)
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```
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Similarly to `Adam`, the epsilon is added for numerical stability
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(especially to get rid of division by zero when `v_t == 0`).
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In contrast to `Adam`, the sparse implementation of this algorithm
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(used when the gradient is an IndexedSlices object, typically because of
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`tf.gather` or an embedding lookup in the forward pass) only updates
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variable slices and corresponding `m_t`, `v_t` terms when that part of
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the variable was used in the forward pass. This means that the sparse
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behavior is contrast to the dense behavior (similar to some momentum
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implementations which ignore momentum unless a variable slice was actually
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used).
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Args:
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learning_rate: A `Tensor`, floating point value, or a schedule that is a
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`tf.keras.optimizers.schedules.LearningRateSchedule`. 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 `"Adamax"`.
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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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Reference:
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- [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
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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="Adamax",
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**kwargs
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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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def _create_slots(self, var_list):
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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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self.add_slot(var, "m") # Create slots for the first moments.
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for var in var_list:
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self.add_slot(var, "v") # Create slots for the second moments.
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def _prepare_local(self, var_device, var_dtype, apply_state):
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super()._prepare_local(var_device, var_dtype, apply_state)
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local_step = tf.cast(self.iterations + 1, 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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beta_1_power = tf.pow(beta_1_t, local_step)
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lr_t = apply_state[(var_device, var_dtype)]["lr_t"]
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apply_state[(var_device, var_dtype)].update(
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dict(
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neg_scaled_lr=-lr_t / (1 - beta_1_power),
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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_1_power=beta_1_power,
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one_minus_beta_1_t=1 - beta_1_t,
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beta_2_t=beta_2_t,
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zero=tf.zeros((), dtype=tf.int64),
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)
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)
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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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return tf.raw_ops.ResourceApplyAdaMax(
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var=var.handle,
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m=m.handle,
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v=v.handle,
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beta1_power=coefficients["beta_1_power"],
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lr=coefficients["lr_t"],
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beta1=coefficients["beta_1_t"],
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beta2=coefficients["beta_2_t"],
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epsilon=coefficients["epsilon"],
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grad=grad,
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use_locking=self._use_locking,
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)
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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_t = beta1 * m + (1 - beta1) * g_t
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m = self.get_slot(var, "m")
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m_slice = tf.gather(m, indices, axis=coefficients["zero"])
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m_t_slice = (
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m_slice * coefficients["beta_1_t"]
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+ grad * coefficients["one_minus_beta_1_t"]
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)
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with tf.control_dependencies([m_t_slice]):
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m_t = self._resource_scatter_update(m, indices, m_t_slice)
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# u_t = max(beta2 * u, abs(g_t))
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v = self.get_slot(var, "v")
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v_slice = tf.gather(v, indices, axis=coefficients["zero"])
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v_t_slice = tf.maximum(v_slice * coefficients["beta_2_t"], tf.abs(grad))
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with tf.control_dependencies([v_t_slice]):
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v_t = self._resource_scatter_update(v, indices, v_t_slice)
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# theta_t = theta - lr / (1 - beta1^t) * m_t / u_t
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var_slice = coefficients["neg_scaled_lr"] * (
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m_t_slice / (v_t_slice + coefficients["epsilon"])
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)
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with tf.control_dependencies([var_slice]):
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var_update = self._resource_scatter_add(var, indices, var_slice)
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return tf.group(*[var_update, m_t, 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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