class_docstr: ```
  Tracks (and plots) the change in loss of a Keras model as learning rate is gradually increased.
  Used to visually identify a good learning rate, given model and data.
  Reference:
      Original Paper: https://arxiv.org/abs/1506.01186
  ```
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docstr: ```
  Generates two numerical estimates of lr:
    1. lr associated with minum numerical gradient (None if gradient computation fails)
    2. lr associated with minimum loss divided by 10
    3. lr associated with longest valley
  Args:
    tuple: (float, float)
  
    If gradient computation fails, first element of tuple will be None.
  ```
docstr_tok: ["``","`","Generates","two","numerical","estimates","of","lr",":","1.","lr","associated","with","minum","numerical","gradient","(","None","if","gradient","computation","fails",")","2.","lr","associated","with","minimum","loss","divided","by","10","3.","lr","associated","with","longest","valley","Args",":","tuple",":","(","float",",","float",")","If","gradient","computation","fails",",","first","element","of","tuple","will","be","None",".","``","`"]
code: def estimate_lr(self):
      """
      ```
      Generates two numerical estimates of lr:
        1. lr associated with minum numerical gradient (None if gradient computation fails)
        2. lr associated with minimum loss divided by 10
        3. lr associated with longest valley
      Args:
        tuple: (float, float)
  
        If gradient computation fails, first element of tuple will be None.
      ```
      """
      if not self.find_called():
          raise ValueError("Please call find first.")
      lr1 = None
      lr2 = None
      if self.mg is not None:
          lr1 = self.lrs[self.mg]
      lr2 = self.lrs[self.ml] / 10
      lr3 = self.lrs[self.valley(self.lrs, self.losses)]
  
      return (lr1, lr2, lr3)
  
file_path: ktrain/ktrain/lroptimize/lrfinder.py
imports: from ..None import utils
  from ..imports import *
label_desc: Use image node_id 7 for calling the LRFinder obj's underlying member method code with example usage: obj.estimate_lr() and returns: lr1,  lr2,  lr3