title: Keras GridSearchCV using metrics other than Accuracy
body: <p>Q1: Why keras gridsearchCV does not allow using metrics other than "Accuracy". Like I want to use:
  <strong>categorical_accuracy</strong> in place of <strong>accuracy</strong>.</p>
  
  <p>Q2: How does this accuracy work for one-hot-encoded data as I am giving now?
  model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])</p>
  
  <p>#-----------------------</p>
  
  <pre><code>  model = KerasClassifier(build_fn=grid_create_model, verbose=1)
      #grid
  
    learn_rate=[0.1,0.001]
    batch_size=[50,100]
    epochs =[10,20]
    param_grid = dict(  learn_rate = learn_rate, batch_size =batch_size, epochs =epochs)
    grid = GridSearchCV(estimator = model, param_grid = param_grid, n_jobs=1)
  
    earlyStopping = keras.callbacks.EarlyStopping(monitor='accuracy', patience=0, verbose=1, mode='auto') 
  #  y_train = np.reshape(y_train, (-1,np.shape(y_train)[1]))
    grid_result = grid.fit(X_train, y_train,callbacks=[earlyStopping])
    print ("\n\ngrid score using params: \n", grid_result.best_score_, "   ",grid_result.best_params_)
  </code></pre>