128 lines
4.0 KiB
Python
128 lines
4.0 KiB
Python
from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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from keras_core.optimizers import optimizer
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@keras_core_export("keras_core.optimizers.SGD")
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class SGD(optimizer.Optimizer):
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"""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=True`, 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 float, a
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`keras_core.optimizers.schedules.LearningRateSchedule` instance, or
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a callable that takes no arguments and returns the actual value to
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use. The learning rate. Defaults to 0.01.
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momentum: float hyperparameter >= 0 that accelerates gradient descent in
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the relevant direction and dampens oscillations. Defaults to 0,
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i.e., vanilla gradient descent.
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nesterov: boolean. Whether to apply Nesterov momentum.
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Defaults to `False`.
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{{base_optimizer_keyword_args}}
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"""
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def __init__(
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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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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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name="SGD",
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):
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super().__init__(
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learning_rate=learning_rate,
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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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)
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if not isinstance(momentum, float) or momentum < 0 or momentum > 1:
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raise ValueError("`momentum` must be a float between [0, 1].")
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self.momentum = momentum
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self.nesterov = nesterov
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def build(self, variables):
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"""Initialize optimizer variables.
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SGD optimizer has one variable `momentums`, only set if `self.momentum`
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is not 0.
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Args:
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var_list: list of model variables to build SGD variables on.
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"""
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if self.built:
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return
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super().build(variables)
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self.momentums = []
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if self.momentum != 0:
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for variable in variables:
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self.momentums.append(
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self.add_variable_from_reference(
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reference_variable=variable, name="m"
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)
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)
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def update_step(self, gradient, variable, learning_rate):
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"""Update step given gradient and the associated model variable."""
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learning_rate = ops.cast(learning_rate, variable.dtype)
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gradient = ops.cast(gradient, variable.dtype)
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m = None
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if self.momentum != 0:
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m = self.momentums[self._get_variable_index(variable)]
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if m is not None:
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momentum = ops.cast(self.momentum, variable.dtype)
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m.assign(-gradient * learning_rate + m * momentum)
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if self.nesterov:
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variable.assign(
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variable - gradient * learning_rate + m * momentum
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)
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else:
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variable.assign(variable + m)
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else:
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variable.assign(variable - gradient * learning_rate)
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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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"momentum": self.momentum,
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"nesterov": self.nesterov,
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}
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)
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return config
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SGD.__doc__ = SGD.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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