main.py 文件源码

def generator(z, latent_c):
    depths = [32, 64, 64, 64, 64, 64, 3]
    sizes = zip(
        np.linspace(4, IMAGE_SIZE['resized'][0], len(depths)).astype(np.int),
        np.linspace(6, IMAGE_SIZE['resized'][1], len(depths)).astype(np.int))
    with slim.arg_scope([slim.conv2d_transpose],
                        normalizer_fn=slim.batch_norm,
                        kernel_size=3):
        with tf.variable_scope("gen"):
            size = sizes.pop(0)
            net = tf.concat(1, [z, latent_c])
            net = slim.fully_connected(net, depths[0] * size[0] * size[1])
            net = tf.reshape(net, [-1, size[0], size[1], depths[0]])
            for depth in depths[1:-1] + [None]:
                net = tf.image.resize_images(
                    net, sizes.pop(0),
                    tf.image.ResizeMethod.NEAREST_NEIGHBOR)
                if depth:
                    net = slim.conv2d_transpose(net, depth)
            net = slim.conv2d_transpose(
                net, depths[-1], activation_fn=tf.nn.tanh, stride=1, normalizer_fn=None)
            tf.image_summary("gen", net, max_images=8)
    return net