python程序GAN程序运行报错

报错
Traceback (most recent call last):
File "E:\Keras-GAN-master\gan\gan.py", line 161, in
gan.train(epochs=30000, batch_size=32, sample_interval=200)
File "E:\Keras-GAN-master\gan\gan.py", line 94, in train
X_train, T, _ = int(data_preprocess)('kmeans.csv', 100)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'function'

X_train, T, _ = data_preprocess('kmeans.csv', 100)

    # Rescale -1 to 1
    X_train = X_train / 127.5 - 1.
    X_train = np.expand_dims(X_train, axis=3)

    # Adversarial ground truths
    valid = np.ones((batch_size, 1))
    fake = np.zeros((batch_size, 1))

    for epoch in range(epochs):

        # ---------------------
        #  Train Discriminator
        # ---------------------

        # Select a random batch of images
        idx = np.random.randint(0, X_train.shape[0], batch_size)
        imgs = X_train[idx]

        noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

        # Generate a batch of new images
        gen_imgs = self.generator.predict(noise)

        # Train the discriminator
        d_loss_real = self.discriminator.train_on_batch(imgs, valid)
        d_loss_fake = self.discriminator.train_on_batch(gen_imgs, fake)
        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)

        # ---------------------
        #  Train Generator
        # ---------------------

        noise = np.random.normal(0, 1, (batch_size, self.latent_dim))

        # Train the generator (to have the discriminator label samples as valid)
        g_loss = self.combined.train_on_batch(noise, valid)

        # Plot the progress
        print ("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" % (epoch, d_loss[0], 100*d_loss[1], g_loss))

        # If at save interval => save generated image samples
        if epoch % sample_interval == 0:
            self.sample_images(epoch)

def sample_images(self, epoch):
    r, c = 5, 5
    noise = np.random.normal(0, 1, (r * c, self.latent_dim))
    gen_imgs = self.generator.predict(noise)

    # Rescale images 0 - 1
    gen_imgs = 0.5 * gen_imgs + 0.5

    fig, axs = plt.subplots(r, c)
    cnt = 0
    for i in range(r):
        for j in range(c):
            axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
            axs[i,j].axis('off')
            cnt += 1
    fig.savefig("images/%d.png" % epoch)
    plt.close()

if name == 'main':
gan = GAN()
gan.train(epochs=30000, batch_size=32, sample_interval=200)

报错指出，int函数使用错误，参数需要是一个数值，或字符串，字节类型对象，不能是一个函数。

这个报错以前出现过没有