|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/layers/util.py#L43-L121) | Makes closure which creates `loc`, `scale` params from `tf.get_variable`. #### View aliases **Main aliases** [`tfp.layers.util.default_loc_scale_fn`](https://www.tensorflow.org/probability/api_docs/python/tfp/layers/default_loc_scale_fn)
tfp.layers.default_loc_scale_fn( is_singular=False, loc_initializer=tf_keras.initializers.RandomNormal(stddev=0.1), untransformed_scale_initializer=tf_keras.initializers.RandomNormal(mean=-3.0, stddev=0.1), loc_regularizer=None, untransformed_scale_regularizer=None, loc_constraint=None, untransformed_scale_constraint=None ) This function produces a closure which produces `loc`, `scale` using `tf.get_variable`. The closure accepts the following arguments: dtype: Type of parameter's event. shape: Python `list`-like representing the parameter's event shape. name: Python `str` name prepended to any created (or existing) [`tf.Variable`](https://www.tensorflow.org/api_docs/python/tf/Variable)s. trainable: Python `bool` indicating all created [`tf.Variable`](https://www.tensorflow.org/api_docs/python/tf/Variable)s should be added to the graph collection `GraphKeys.TRAINABLE_VARIABLES`. add_variable_fn: `tf.get_variable`-like `callable` used to create (or access existing) [`tf.Variable`](https://www.tensorflow.org/api_docs/python/tf/Variable)s.


| ## Args || |---|---| | `is_singular` | Python `bool` indicating if `scale is None`. Default: `False`. | | `loc_initializer` | Initializer function for the `loc` parameters. The default is [`tf.random_normal_initializer(mean=0., stddev=0.1)`](https://www.tensorflow.org/api_docs/python/tf/random_normal_initializer). | | `untransformed_scale_initializer` | Initializer function for the `scale` parameters. Default value: `tf.random_normal_initializer(mean=-3., stddev=0.1)`. This implies the softplus transformed result is initialized near `0`. It allows a `Normal` distribution with `scale` parameter set to this value to approximately act like a point mass. | | `loc_regularizer` | Regularizer function for the `loc` parameters. The default (`None`) is to use the `tf.get_variable` default. | | `untransformed_scale_regularizer` | Regularizer function for the `scale` parameters. The default (`None`) is to use the `tf.get_variable` default. | | `loc_constraint` | An optional projection function to be applied to the loc after being updated by an `Optimizer`. The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. The default (`None`) is to use the `tf.get_variable` default. | | `untransformed_scale_constraint` | An optional projection function to be applied to the `scale` parameters after being updated by an `Optimizer` (e.g. used to implement norm constraints or value constraints). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training. The default (`None`) is to use the `tf.get_variable` default. |



| ## Returns || |---|---| | `default_loc_scale_fn` | Python `callable` which instantiates `loc`, `scale` parameters from args: `dtype, shape, name, trainable, add_variable_fn`. |