rnn-cnn-gan-enhancer.py 文件源码

def generate(SONG_LENGTH, nb):

    generator = generator_model()
    generator.compile(loss='binary_crossentropy', optimizer="SGD")
    generator.load_weights('generator')

    print "loading_latent_music"
    latent_music = trainLoadMusic.loadMusic("lstm_outputs", SONG_LENGTH)

    for i in range(nb):

        latent = random.choice(latent_music)

        song = generator.predict(latent, verbose=1)

        song = song.reshape((SONG_LENGTH,note_span_with_ligatures/2,2))
        song_0 = generate_from_probabilities(song_0)
        matrixToMidi(song_0,'outputs/example {}'.format(i))