如何在 Keras 中重新训练权重

Question

我正在 Colab 中训练模型，但是，我关闭了计算机，训练停止了。每 5 个 epoch 我保存权重。我认为是，但我不知道如何。如何使用之前保存的权重继续训练？

谢谢。

Answer 1

在colab 中训练模型时，训练不会在您关闭计算机时停止，而是会在一段时间后停止。

如果您在colab 中保存权重，当colab 关闭时，所有内容都会被删除。

如果您已将gdrive 安装在colab and 中，则您将权重保存在gdrive 中，您的权重将在那里。

如果您的权重在您的 gdrive 中，您可以通过将存储的权重加载到您的 keras 模型来继续训练

model.load_weights('path_to_weights')

Answer 2

感谢您的回答，@Ioannis Nasios。是的，我的体重在“gdrive”中。我正在训练一个 GAN 网络，我试图弄清楚如何加载这些权重并继续训练。我保存了鉴别器和生成器的权重以及 gan_loss 和 discriminator_loss。好吧，我是否必须编译生成器和鉴别器网络，加载权重并编译带有损失的 gan 网络？我认为这可能是一个愚蠢的问题。这是我第一次训练 GAN 网络。 这里我贴出代码：

# Combined network
def get_gan_network(discriminator, shape, generator, optimizer, loss):
    discriminator.trainable = False
    gan_input = Input(shape=shape)
    x = generator(gan_input)
    gan_output = discriminator(x)
    gan = Model(inputs=gan_input, outputs=[x,gan_output])
    gan.compile(loss=[loss, "binary_crossentropy"],
                loss_weights=[1., 1e-3],
                optimizer=optimizer)

    return gan

def train(x_train_lr, x_train_hr, x_test_lr, x_test_hr, epochs, batch_size, output_dir, model_save_dir, weights_save_dir):


    loss = VGG_LOSS(image_shape)  

    batch_count = int(x_train_hr.shape[0] / batch_size)
    #### SI LAS IMAGENES NO SON CUADRADAS ESTO DEBERIA CAMBIAR
    shape_lr = (image_shape[0]//downscale_factor, image_shape[1]//downscale_factor, image_shape[2])
    shape_hr = x_train_hr[0].shape
    ####
    generator = Generator(shape_lr, shape_hr).generator()
    discriminator = Discriminator(image_shape).discriminator()

    optimizer = Utils_model.get_optimizer()
    generator.compile(loss=loss.vgg_loss, optimizer=optimizer)
    discriminator.compile(loss="binary_crossentropy", optimizer=optimizer)

    gan = get_gan_network(discriminator, shape_lr, generator, optimizer, loss.vgg_loss)

    loss_file = open(model_save_dir + '/losses.txt' , 'w+')
    loss_file.close()

    for e in range(1, epochs+1):
        print ('-'*15, 'Epoch %d' % e, '-'*15)
        for _ in tqdm(range(batch_count)):

            rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)

            image_batch_hr = x_train_hr[rand_nums]
            image_batch_lr = x_train_lr[rand_nums]

            generated_images_sr = generator.predict(image_batch_lr)

            real_data_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
            fake_data_Y = np.random.random_sample(batch_size)*0.2

            discriminator.trainable = True

            d_loss_real = discriminator.train_on_batch(image_batch_hr, real_data_Y)
            d_loss_fake = discriminator.train_on_batch(generated_images_sr, fake_data_Y)
            discriminator_loss = 0.5 * np.add(d_loss_fake, d_loss_real)

            rand_nums = np.random.randint(0, x_train_hr.shape[0], size=batch_size)
            image_batch_hr = x_train_hr[rand_nums]
            image_batch_lr = x_train_lr[rand_nums]

            gan_Y = np.ones(batch_size) - np.random.random_sample(batch_size)*0.2
            discriminator.trainable = False
            gan_loss = gan.train_on_batch(image_batch_lr, [image_batch_hr,gan_Y])


        print("discriminator_loss : %f" % discriminator_loss)
        print("gan_loss :", gan_loss)
        gan_loss = str(gan_loss)

        loss_file = open(model_save_dir + 'losses.txt' , 'a')
        loss_file.write('epoch%d : gan_loss = %s ; discriminator_loss = %f\n' %(e, gan_loss, discriminator_loss) )
        loss_file.close()

        if e == 1 or e % 5 == 0:
            Utils.plot_generated_images(output_dir, e, generator, x_test_hr, x_test_lr)
            generator.save_weights(weights_save_dir + '%d_gen_weights.h5' % e)
            discriminator.save_weights(weights_save_dir + '%d_dis_weights.h5' % e)

        if e % 500 == 0 or e == epochs+1:
            generator.save(model_save_dir + 'gen_model%d.h5' % e)
            discriminator.save(model_save_dir + 'dis_model%d.h5' % e)