在训练vae，保存和重用模型以及在许多图像上训练模型时，如何迭代许多图像？

Question

这是我的代码

以tf.Session（）作为会话：

    sess.run(tf.global_variables_initializer(), feed_dict={keep_prob : 0.9})

    for epoch in range(n_epochs):

        # Random shuffling
        np.random.shuffle(train_total_data)
        train_data_ = train_total_data[:, :-mnist_data.NUM_LABELS]

        # Loop over all batches
        for i in range(total_batch):
            # Compute the offset of the current minibatch in the data.
            offset = (i * batch_size) % (n_samples)
            batch_xs_input = train_data_[offset:(offset + batch_size), :]

            batch_xs_target = batch_xs_input

            # add salt & pepper noise
            if ADD_NOISE:
                batch_xs_input = batch_xs_input * np.random.randint(2, size=batch_xs_input.shape)
                batch_xs_input += np.random.randint(2, size=batch_xs_input.shape)

            _, tot_loss, loss_likelihood, loss_divergence = sess.run(
                (train_op, loss, neg_marginal_likelihood, KL_divergence),
                feed_dict={x_hat: batch_xs_input, x: batch_xs_target, keep_prob : 0.9})

        # print cost every epoch
        print("epoch %d: L_tot %03.2f L_likelihood %03.2f L_divergence %03.2f" % (epoch, tot_loss, loss_likelihood, loss_divergence))

        # if minimum loss is updated or final epoch, plot results
        if min_tot_loss > tot_loss or epoch+1 == n_epochs:
            min_tot_loss = tot_loss
            # Plot for reproduce performance
            if PRR:
                y_PRR = sess.run(y, feed_dict={x_hat: x_PRR, keep_prob : 1})
                y_PRR_img = y_PRR.reshape(PRR.n_tot_imgs, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST)
                PRR.save_images(y_PRR_img, name="/PRR_epoch_%02d" %(epoch) + ".jpg")

            # Plot for manifold learning result
            if PMLR and dim_z == 2:
                y_PMLR = sess.run(decoded, feed_dict={z_in: PMLR.z, keep_prob : 1})
                y_PMLR_img = y_PMLR.reshape(PMLR.n_tot_imgs, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST)
                PMLR.save_images(y_PMLR_img, name="/PMLR_epoch_%02d" % (epoch) + ".jpg")

                # plot distribution of labeled images
                z_PMLR = sess.run(z, feed_dict={x_hat: x_PMLR, keep_prob : 1})
                PMLR.save_scattered_image(z_PMLR,id_PMLR, name="/PMLR_map_epoch_%02d" % (epoch) + ".jpg")

它是从此仓库中提取的： https://github.com/hwalsuklee/tensorflow-mnist-VAE

我想生成一个像这样的视频：https://www.youtube.com/watch?v=Sc7RiNgHHaE 在10:18

所以我要在设定的时间间隔内打印演示图像吗？ 另外，我将如何保存np数组并对解码器进行编码？ 接下来，我将如何训练许多图像？ 另外，我不知道如何保存模型并将其重新用于编码新的未经训练的图像。