xwgan_mnist.py 文件源码

def build_summaries(gan_graph):
    """
    """
    generator_loss_summary = tf.summary.scalar(
        'generator loss', gan_graph['generator_loss'])

    discriminator_loss_summary = tf.summary.scalar(
        'discriminator loss', gan_graph['discriminator_loss'])

    fake_grid = tf.reshape(gan_graph['generator_fake'], [1, 64 * 32, 32, 1])
    fake_grid = tf.split(fake_grid, 8, axis=1)
    fake_grid = tf.concat(fake_grid, axis=2)
    fake_grid = tf.saturate_cast(fake_grid * 127.5 + 127.5, tf.uint8)

    generator_fake_summary = tf.summary.image(
        'generated image', fake_grid, max_outputs=18)

    return {
        'generator_fake_summary': generator_fake_summary,
        'generator_loss_summary': generator_loss_summary,
        'discriminator_loss_summary': discriminator_loss_summary,
    }