|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/glm/fisher_scoring.py#L474-L545) | Returns Python `callable` which indicates fitting procedure has converged. tfp.glm.convergence_criteria_small_relative_norm_weights_change( tolerance=1e-05, norm_order=2 ) Writing old, new `model_coefficients` as `w0`, `w1`, this function defines convergence as, relative_euclidean_norm = (tf.norm(w0 - w1, ord=2, axis=-1) / (1. + tf.norm(w0, ord=2, axis=-1))) reduce_all(relative_euclidean_norm < tolerance) where [`tf.norm(x, ord=2)`](https://www.tensorflow.org/api_docs/python/tf/norm) denotes the [Euclidean norm](https://en.wikipedia.org/wiki/Norm_(mathematics)#Euclidean_norm) of `x`.


| ## Args || |---|---| | `tolerance` | `float`-like `Tensor` indicating convergence, i.e., when max relative Euclidean norm weights difference \< tolerance`. Default value:`1e-5`. `norm_order` Order of the norm. Default value:`2\` (i.e., "Euclidean norm".) |



| ## Returns || |---|---| | `convergence_criteria_fn` | Python `callable` which returns `bool` `Tensor` indicated fitting procedure has converged. (See inner function specification for argument signature.) Default value: `1e-5`. |