code: def model(x_train, y_train, x_test, y_test):
      
      model = Sequential()
      model.add(Dense(512, input_shape=(784,)))
      model.add(Activation('relu'))
      model.add(Dropout({{uniform(0, 1)}}))
      model.add(Dense({{choice([256, 512, 1024])}}))
      model.add(Activation({{choice(['relu', 'sigmoid'])}}))
      model.add(Dropout({{uniform(0, 1)}}))
  
      # If we choose 'four', add an additional fourth layer
      if {{choice(['three', 'four'])}} == 'four':
          model.add(Dense(100))
  
          # We can also choose between complete sets of layers
  
          model.add({{choice([Dropout(0.5), Activation('linear')])}})
          model.add(Activation('relu'))
  
      model.add(Dense(10))
      model.add(Activation('softmax'))
  
      model.compile(loss='categorical_crossentropy', metrics=['accuracy'],
                    optimizer={{choice(['rmsprop', 'adam', 'sgd'])}})
  
      model.fit(x_train, y_train,
                batch_size={{choice([64, 128])}},
                epochs=1,
                verbose=2,
                validation_data=(x_test, y_test))
      score, acc = model.evaluate(x_test, y_test, verbose=0)
      print('Test accuracy:', acc)
      return {'loss': -acc, 'status': STATUS_OK, 'model': model}
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.