class_docstr: ```
  Abstract class used to tune and train Keras models. The fit method is
  an abstract method and must be implemented by subclasses.
  ```
class_docstr_tok: ["``","`","Abstract","class","used","to","tune","and","train","Keras","models",".","The","fit","method","is","an","abstract","method","and","must","be","implemented","by","subclasses",".","``","`"]
docstr: ```
  Makes predictions on validation set
  ```
docstr_tok: ["``","`","Makes","predictions","on","validation","set","``","`"]
code: def predict(self, val_data=None):
      """
      ```
      Makes predictions on validation set
      ```
      """
      if val_data is not None:
          val = val_data
      else:
          val = self.val_data
      if val is None:
          raise Exception(
              "val_data must be supplied to get_learner or predict"
          )
      if U.is_iter(val):
          if hasattr(val, "reset"):
              val.reset()
          steps = np.ceil(U.nsamples_from_data(val) / val.batch_size)
          # *_generator methods are deprecated from TF 2.1.0
          # result = self.model.predict_generator(self._prepare(val, train=False),
          # steps=steps)
          result = self.model.predict(
              self._prepare(val, train=False), steps=steps
          )
          return result
      else:
          return self.model.predict(
              val[0], batch_size=self.eval_batch_size
          )
  
imports: from .None import utils
  from .graph.predictor import LinkPredictor, NodePredictor
  from .graph.preprocessor import LinkPreprocessor, NodePreprocessor
  from .imports import *
  from .lroptimize.lrfinder import *
  from .tabular.predictor import TabularPredictor
  from .tabular.preprocessor import TabularPreprocessor
  from .text.ner.predictor import NERPredictor
  from .text.ner.preprocessor import NERPreprocessor
  from .text.predictor import TextPredictor
  from .text.preprocessor import BERTPreprocessor, TextPreprocessor, TransformersPreprocessor
  from .vision.predictor import ImagePredictor
  from .vision.preprocessor import ImagePreprocessor
label_desc: Use image node_id 35 for calling the Learner obj's underlying member method code with example usage: obj.predict(val_data) and returns: result, self