title: How to use KerasClassifier validation split and using scitkit learn GridSearchCV
body: <p>I want to try to test some hyperparameters, thats i want to use the GridSearchCV, because it seems like thats the way to do it.</p>
  
  <p>But i also want to use the validation split. To use Callsbacks like EarlyStopping or/and ReduceLROnPlateau. So my question is:
  How do i implement GridSearchCV + validation_split correctly that none of the data in validation split is using for training and the whole training set is used to train my model? 
  Afaik GridSearchCV split again my remaining train data (which is 1-validation_split) and split it again? I get kinda high accuracy and im thinking that i dont split the data correctly</p>
  
  <pre><code>model = KerasClassifier(build_fn=create_model,verbose=2, validation_split=0.1)
  optimizers = ['rmsprop', 'adam']
  init = ['glorot_uniform',
          #'normal',
          'uniform',
          'he_normal',
          #'lecun_normal',
          #'he_uniform'
         ]
  epochs = [3] #5,8,10,30
  batches = [64] #32,64
  param_grid = dict(optimizer=optimizers, epochs=epochs, batch_size=batches, init=init)
  grid = GridSearchCV(estimator=model, param_grid=param_grid)
  grid_result = grid.fit(X_train, Y_train)
  </code></pre>