Deep Learning By Example

Sampling from the generator

In the previous section, we went through some examples that were generated during the training process of this GAN architecture. We can also generate completely new images from the generator by loading the checkpoints that we have saved and feeding the generator with a new latent space that it can use to generate new images:

# Sampling from the generatorsaver = tf.train.Saver(var_list=g_vars)with tf.Session() as sess:        #restoring the saved checkpints    saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))    gen_sample_z = np.random.uniform(-1, 1, size=(16, z_size))    generated_samples = sess.run(                   generator(generator_input_z, input_img_size, reuse_vars=True),                   feed_dict={generator_input_z: gen_sample_z})view_generated_samples(0, ...