class_docstr: A text detector using the CRNN architecture.
  
  Args:
      alphabet: The alphabet the model should recognize.
      build_params: A dictionary of build parameters for the model.
          See `keras_ocr.recognition.build_model` for details.
      weights: The starting weight configuration for the model.
      include_top: Whether to include the final classification layer in the model (set
          to False to use a custom alphabet).
class_docstr_tok: ["A","text","detector","using","the","CRNN","architecture",".","Args",":","alphabet",":","The","alphabet","the","model","should","recognize",".","build_params",":","A","dictionary","of","build","parameters","for","the","model",".","See","`","keras_ocr.recognition.build_model","`","for","details",".","weights",":","The","starting","weight","configuration","for","the","model",".","include_top",":","Whether","to","include","the","final","classification","layer","in","the","model","(","set","to","False","to","use","a","custom","alphabet",")","."]
docstr: Compile the training model.
docstr_tok: ["Compile","the","training","model","."]
code: def compile(self, *args, **kwargs):
      """Compile the training model."""
      if "optimizer" not in kwargs:
          kwargs["optimizer"] = "RMSprop"
      if "loss" not in kwargs:
          kwargs["loss"] = lambda _, y_pred: y_pred
      self.training_model.compile(*args, **kwargs)
  
file_path: keras-ocr/keras_ocr/recognition.py
imports: import typing
  import string
  import tensorflow
  from tensorflow import keras
  import numpy
  import cv2
  from .None import tools
label_desc: Use image node_id 5 for calling the Recognizer obj's underlying member method code with example usage: obj.compile() without return types