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.

def model(x_train, y_train, x_test, y_test):
    """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.
    """
    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())}