How to retrain with weights in Keras

When training a model in colab, training doesn't stop when you close you computer, it stops some time afterwards.

If you are saving the weights in colab, when colab closes everything is deleted.

If you have mounted your gdrive in colab and you save weights in gdrive, your weights will be there.

If your weights are in your gdrive you can continue training by loading your stored weights to your keras model simple by

model.load_weights('path_to_weights')

Thank you for your answer, @Ioannis Nasios. Yes, my weights are in 'gdrive'. I'm training a GAN network and I trying to figure out how to load these weights and continue the training. I saved the discriminator and generator weights and also gan_loss and discriminator_loss. Well, do I have to compile generator and discriminator networks, load weights and compile gan network with their loss? I think it could be a stupid question. It is my first time training a GAN network. Here I post the code:

# 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)