Fix optimizer docstring rendering issue.
@@ -839,20 +839,24 @@ base_optimizer_keyword_args = """name: String. The name to use
average of the weights of the model (as the weight values change after
each training batch), and periodically overwriting the weights with
their moving average.
- ema_momentum: Float, defaults to 0.99. Only used if `use_ema=True`. This is # noqa: E501
- the momentum to use when computing the EMA of the model's weights:
+ ema_momentum: Float, defaults to 0.99. Only used if `use_ema=True`.
+ This is the momentum to use when computing
+ the EMA of the model's weights:
`new_average = ema_momentum * old_average + (1 - ema_momentum) *
current_variable_value`.
ema_overwrite_frequency: Int or None, defaults to None. Only used if
- `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations, we
- overwrite the model variable by its moving average. If None, the optimizer # noqa: E501
+ `use_ema=True`. Every `ema_overwrite_frequency` steps of iterations,
+ we overwrite the model variable by its moving average.
+ If None, the optimizer
does not overwrite model variables in the middle of training, and you
need to explicitly overwrite the variables at the end of training
- by calling `optimizer.finalize_variable_values()` (which updates the model # noqa: E501
- variables in-place). When using the built-in `fit()` training loop, this
- happens automatically after the last epoch, and you don't need to do
- anything.
- jit_compile: Boolean, defaults to True. If True, the optimizer will use XLA # noqa: E501
+ by calling `optimizer.finalize_variable_values()`
+ (which updates the model
+ variables in-place). When using the built-in `fit()` training loop,
+ this happens automatically after the last epoch,
+ and you don't need to do anything.
+ jit_compile: Boolean, defaults to True.
+ If True, the optimizer will use XLA
compilation. If no GPU device is found, this flag will be ignored.
**kwargs: keyword arguments only used for backward compatibility."""
