code: def model(x_train, y_train, x_test, y_test):
      
      from keras.models import Sequential
      from keras.layers.core import Dense, Dropout, Activation
      from keras.optimizers import RMSprop
  
      keras_model = Sequential()
      keras_model.add(Dense(512, input_shape=(784,)))
      keras_model.add(Activation('relu'))
      keras_model.add(Dropout({{uniform(0, 1)}}))
      keras_model.add(Dense({{choice([256, 512, 1024])}}))
      keras_model.add(Activation('relu'))
      keras_model.add(Dropout({{uniform(0, 1)}}))
      keras_model.add(Dense(10))
      keras_model.add(Activation('softmax'))
  
      rms = RMSprop()
      keras_model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['acc'])
  
      keras_model.fit(x_train, y_train,
                      batch_size={{choice([64, 128])}},
                      epochs=1,
                      verbose=2,
                      validation_data=(x_test, y_test))
      score, acc = keras_model.evaluate(x_test, y_test, verbose=0)
      print('Test accuracy:', acc)
      return {'loss': -acc, 'status': STATUS_OK, 'model': keras_model.to_yaml(),
              'weights': pickle.dumps(keras_model.get_weights())}
docs: Model providing function:
  
      Create Keras model with double curly brackets dropped-in as needed.
      Return value has to be a valid python dictionary with two customary keys:
          - loss: Specify a numeric evaluation metric to be minimized
          - status: Just use STATUS_OK and see hyperopt documentation if not feasible
      The last one is optional, though recommended, namely:
          - model: specify the model just created so that we can later use it again.