gan网络生成图片质量评价代码

以下是使用Inception Score评价GAN网络生成图片质量的Python代码：

import tensorflow as tf
import numpy as np
import os
import functools

from tensorflow.python.ops import array_ops
from tensorflow.python.ops import functional_ops

slim = tf.contrib.slim


def inception_score(images, num_classes=1000, batch_size=32, splits=10):
    """
    计算Inception Score

    :param images: 生成图片的张量，shape为[batch_size, height, width, channels]，像素值范围为[0, 255]
    :param num_classes: Inception网络的类别数，默认为1000
    :param batch_size: 计算Inception Score时的batch大小，默认为32
    :param splits: 分割数量，默认为10
    :return: Inception Score
    """
    assert (images.shape[1] == images.shape[2])  # 保证图片为正方形

    inception_images = tf.image.resize_bilinear(images, [299, 299])
    inception_images = tf.divide(inception_images, 255.0)

    logits = []
    for i in range(0, images.shape[0], batch_size):
        batch = inception_images[i:i + batch_size, :, :, :]
        logit = functional_ops.softmax(
            functional_ops.in_top_k(
                predictions=tf.cast(batch, tf.float32),
                targets=tf.constant(np.arange(num_classes)),
                k=1)
        )
        logits.append(logit)
    logits = array_ops.concat(logits, 0)
    scores = []
    for i in range(splits):
        part = logits[
               (i * logits.shape[0] // splits):
               ((i + 1) * logits.shape[0] // splits), :]
        kl = part * (tf.log(part) - tf.log(tf.reduce_mean(part, 0, keepdims=True)))
        kl = tf.reduce_mean(tf.reduce_sum(kl, 1))
        scores.append(tf.exp(kl))
    return tf.reduce_mean(scores)


def get_inception_score(sess, images_ph, fake_images):
    """
    计算Inception Score

    :param sess: TensorFlow会话
    :param images_ph: 真实图片的占位符
    :param fake_images: 生成图片的张量
    :return: Inception Score
    """
    assert (fake_images.shape[1] == fake_images.shape[2])  # 保证图片为正方形

    fake_images = (fake_images + 1.0) / 2.0  # 将像素值从[-1, 1]转换为[0, 1]

    num_images = fake_images.shape[0]
    num_batches = int(np.ceil(num_images / 32))

    scores = []
    for i in range(num_batches):
        batch_fake_images = fake_images[i * 32:(i + 1) * 32]
        score = sess.run(inception_score(images_ph, fake_images=batch_fake_images))
        scores.append(score)
    return np.mean(scores)


if __name__ == '__main__':
    # 在此处定义生成器生成的图片张量fake_images
    # ...

    # 加载Inception模型
    inception_path = os.path.join('inception', 'inception_v3.ckpt')

    sess = tf.Session()

    images_ph = tf.placeholder(tf.float32, shape=[None, None, None, 3])
    with slim.arg_scope(inception_v3_arg_scope()):
        _, end_points = inception_v3(images_ph, num_classes=1000, is_training=False)
    inception_variables = slim.get_variables(scope='InceptionV3')

    saver = tf.train.Saver(var_list=inception_variables)
    saver.restore(sess, inception_path)

    # 计算Inception Score
    inception_score = get_inception_score(sess, images_ph, fake_images)
    print('Inception Score:', inception_score)

    sess.close()

需要注意的是，这里假设生成器生成的图片像素值范围为[-1, 1]，因此需要先将像素值从[-1, 1]转换为[0, 1]。同时，需要将Inception模型文件（`inception_v3.ckpt`）和代码文件放在同一个目录下。