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[keras/mixed_precision] Standardise docstring usage of "Default to" #17973

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copybara-service merged 2 commits into from
May 5, 2023
Merged

[keras/mixed_precision] Standardise docstring usage of "Default to" #17973

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[keras/mixed_precision/loss_scale_optimizer.py] Standardise docstring…
SamuelMarks Apr 13, 2023
19dcdeb
[keras/mixed_precision/loss_scale_optimizer.py] Docstring minor impro…
SamuelMarks Apr 23, 2023
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16 changes: 8 additions & 8 deletions keras/mixed_precision/loss_scale_optimizer.py
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Expand Up @@ -406,14 +406,14 @@ class BaseLossScaleOptimizer(metaclass=LossScaleOptimizerMetaclass):
Args:
inner_optimizer: The `tf.keras.optimizers.Optimizer` or
`tf.keras.optimizers.experimental.Optimizer` instance to wrap.
dynamic: Bool indicating whether dynamic loss scaling is used. Defaults to
True. If True, the loss scale will be dynamically updated over time
using an algorithm that keeps the loss scale at approximately its
optimal value. If False, a single fixed loss scale is used and
`initial_scale` must be specified, which is used as the loss scale.
dynamic: Bool indicating whether dynamic loss scaling is used. If `True`,
the loss scale will be dynamically updated over time using an algorithm
that keeps the loss scale at approximately its optimal value. If False,
a single fixed loss scale is used and `initial_scale` must be
specified, which is used as the loss scale.
Recommended to keep as True, as choosing a fixed loss scale can be
tricky. Currently, there is a small performance overhead to dynamic loss
scaling compared to fixed loss scaling.
scaling compared to fixed loss scaling. Defaults to `True`.
initial_scale: The initial loss scale. If `dynamic` is True, this defaults
to `2 ** 15`. If `dynamic` is False, this must be specified and acts as
the sole loss scale, as the loss scale does not change over time. When
Expand All @@ -422,11 +422,11 @@ class BaseLossScaleOptimizer(metaclass=LossScaleOptimizerMetaclass):
quickly than a loss scale that is too low gets raised.
dynamic_growth_steps: With dynamic loss scaling, every
`dynamic_growth_steps` steps with finite gradients, the loss scale is
doubled. Defaults to 2000. If a nonfinite gradient is encountered, the
doubled. If a nonfinite gradient is encountered, the
count is reset back to zero, gradients are skipped that step, and the
loss scale is halved. The count can be queried with
`LossScaleOptimizer.dynamic_counter`. This argument can only be
specified if `dynamic` is True.
specified if `dynamic` is True. Defaults to `2000`.

`LossScaleOptimizer` will occasionally skip applying gradients to the
variables, in which case the trainable variables will not change that step.
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