def build_model():
    model = keras.Sequential([
        layers.Dense(32, activation=tf.nn.sigmoid, input_shape=[len(trainingData.keys())]),
        layers.Dense(32, activation=tf.nn.sigmoid),
        layers.Dense(32, activation=tf.nn.sigmoid),
        layers.Dense(32, activation=tf.nn.sigmoid),
        layers.Dense(1)
    ])

    optimizer = tf.train.RMSPropOptimizer(0.01)

    model.compile(loss='mse',
                  optimizer=optimizer,
                  metrics=['mae', 'mse'])
    return model