190 lines
6.9 KiB
Python
190 lines
6.9 KiB
Python
# Copyright 2020 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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"""SGD optimizer implementation."""
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# pylint: disable=g-classes-have-attributes
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.framework import ops
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from tensorflow.python.keras.optimizer_v2 import optimizer_v2
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.training import training_ops
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.optimizers.SGD")
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class SGD(optimizer_v2.OptimizerV2):
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r"""Gradient descent (with momentum) optimizer.
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Update rule for parameter `w` with gradient `g` when `momentum` is 0:
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```python
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w = w - learning_rate * g
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```
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Update rule when `momentum` is larger than 0:
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```python
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velocity = momentum * velocity - learning_rate * g
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w = w * velocity
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```
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When `nesterov=False`, this rule becomes:
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```python
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velocity = momentum * velocity - learning_rate * g
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w = w + momentum * velocity - learning_rate * g
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```
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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`, 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 0.01.
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momentum: float hyperparameter >= 0 that accelerates gradient descent
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in the relevant
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direction and dampens oscillations. Defaults to 0, i.e., vanilla gradient
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descent.
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nesterov: boolean. Whether to apply Nesterov momentum.
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Defaults to `False`.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to `"SGD"`.
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**kwargs: Keyword arguments. Allowed to be one of
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`"clipnorm"` or `"clipvalue"`.
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`"clipnorm"` (float) clips gradients by norm; `"clipvalue"` (float) clips
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gradients by value.
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Usage:
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>>> opt = tf.keras.optimizers.SGD(learning_rate=0.1)
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>>> var = tf.Variable(1.0)
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>>> loss = lambda: (var ** 2)/2.0 # d(loss)/d(var1) = var1
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>>> step_count = opt.minimize(loss, [var]).numpy()
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>>> # Step is `- learning_rate * grad`
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>>> var.numpy()
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0.9
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>>> opt = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9)
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>>> var = tf.Variable(1.0)
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>>> val0 = var.value()
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>>> loss = lambda: (var ** 2)/2.0 # d(loss)/d(var1) = var1
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>>> # First step is `- learning_rate * grad`
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>>> step_count = opt.minimize(loss, [var]).numpy()
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>>> val1 = var.value()
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>>> (val0 - val1).numpy()
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0.1
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>>> # On later steps, step-size increases because of momentum
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>>> step_count = opt.minimize(loss, [var]).numpy()
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>>> val2 = var.value()
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>>> (val1 - val2).numpy()
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0.18
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Reference:
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- For `nesterov=True`, See [Sutskever et al., 2013](
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http://jmlr.org/proceedings/papers/v28/sutskever13.pdf).
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"""
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_HAS_AGGREGATE_GRAD = True
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def __init__(self,
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learning_rate=0.01,
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momentum=0.0,
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nesterov=False,
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name="SGD",
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**kwargs):
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super(SGD, self).__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._momentum = False
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if isinstance(momentum, ops.Tensor) or callable(momentum) or momentum > 0:
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self._momentum = True
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if isinstance(momentum, (int, float)) and (momentum < 0 or momentum > 1):
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raise ValueError("`momentum` must be between [0, 1].")
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self._set_hyper("momentum", momentum)
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self.nesterov = nesterov
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def _create_slots(self, var_list):
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if self._momentum:
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for var in var_list:
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self.add_slot(var, "momentum")
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def _prepare_local(self, var_device, var_dtype, apply_state):
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super(SGD, self)._prepare_local(var_device, var_dtype, apply_state)
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apply_state[(var_device, var_dtype)]["momentum"] = array_ops.identity(
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self._get_hyper("momentum", var_dtype))
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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((var_device, var_dtype))
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or self._fallback_apply_state(var_device, var_dtype))
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if self._momentum:
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momentum_var = self.get_slot(var, "momentum")
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return training_ops.resource_apply_keras_momentum(
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var.handle,
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momentum_var.handle,
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coefficients["lr_t"],
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grad,
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coefficients["momentum"],
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use_locking=self._use_locking,
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use_nesterov=self.nesterov)
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else:
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return training_ops.resource_apply_gradient_descent(
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var.handle, coefficients["lr_t"], grad, use_locking=self._use_locking)
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def _resource_apply_sparse_duplicate_indices(self, grad, var, indices,
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**kwargs):
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if self._momentum:
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return super(SGD, self)._resource_apply_sparse_duplicate_indices(
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grad, var, indices, **kwargs)
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else:
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var_device, var_dtype = var.device, var.dtype.base_dtype
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coefficients = (kwargs.get("apply_state", {}).get((var_device, var_dtype))
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or self._fallback_apply_state(var_device, var_dtype))
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return resource_variable_ops.resource_scatter_add(
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var.handle, indices, -grad * coefficients["lr_t"])
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def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
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# This method is only needed for momentum optimization.
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var_device, var_dtype = var.device, var.dtype.base_dtype
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coefficients = ((apply_state or {}).get((var_device, var_dtype))
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or self._fallback_apply_state(var_device, var_dtype))
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momentum_var = self.get_slot(var, "momentum")
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return training_ops.resource_sparse_apply_keras_momentum(
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var.handle,
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momentum_var.handle,
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coefficients["lr_t"],
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grad,
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indices,
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coefficients["momentum"],
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use_locking=self._use_locking,
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use_nesterov=self.nesterov)
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def get_config(self):
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config = super(SGD, self).get_config()
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config.update({
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"learning_rate": self._serialize_hyperparameter("learning_rate"),
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"decay": self._serialize_hyperparameter("decay"),
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"momentum": self._serialize_hyperparameter("momentum"),
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"nesterov": self.nesterov,
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})
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return config
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