【Python】【Keras】【GAN】指定ディレクトリ下の画像の学習ができない

お世話になります
機械(深層)学習の超初心者です

現在 WGAN-GPで カラー画像を (Colab上で) 学習させようとチャレンジしております 
ここのコードを使っております

画像は読み込んでいるようなのですが 以下のエラーが出て 実行できません

Traceback (most recent call last):
  File "wgan_gp_3.py", line 270, in <module>
    wgan.train(epochs=30000, batch_size=8, sample_interval=100)
  File "wgan_gp_3.py", line 234, in train
    [valid, fake, dummy])
  File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1217, in train_on_batch
    outputs = self.train_function(ins)
  File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__
    return self._call(inputs)
  File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2675, in _call
    fetched = self._callable_fn(*array_vals)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1439, in __call__
    run_metadata_ptr)
  File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [32,1,1,1] vs. [8,3,256,256]
     [[{{node random_weighted_average_1/mul_1}}]]
     [[{{node loss/model_2_loss/Mean_3}}]]

変更部分コード

from __future__ import print_function, division

from keras.datasets import mnist
from keras.layers.merge import _Merge
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import RMSprop
from functools import partial
from keras.preprocessing.image import load_img, img_to_array,save_img , array_to_img


from tensorflow.python.keras.preprocessing.image import ImageDataGenerator

import keras.backend as K

import matplotlib.pyplot as plt

import glob

import sys

import numpy as np


class WGANGP():
    def __init__(self):
        self.img_rows = 256
        self.img_cols = 256
        self.channels = 3
        self.img_shape = (self.img_rows, self.img_cols, self.channels)
        self.latent_dim = 100

        data_dir = '/content/drive/My Drive/images/'

        batch_size = 8


        gen = ImageDataGenerator(rescale=1/127.5, samplewise_center=True)
        iters = gen.flow_from_directory(
        directory=data_dir,
        classes=['test'],
        class_mode=None,
        color_mode='rgb',
        target_size=self.img_shape[:2],
        batch_size=batch_size,
        shuffle=True
        )
        self.x_train_batch=next(iters)


        # Following parameter and optimizer set as recommended in paper
        self.n_critic = 5
        optimizer = RMSprop(lr=0.00005)

        # Build the generator and critic
        self.generator = self.build_generator()
        self.critic = self.build_critic()

        #-------------------------------
        # Construct Computational Graph
        #       for the Critic
        #-------------------------------

        # Freeze generator's layers while training critic
        self.generator.trainable = False

        # Image input (real sample)
        real_img = Input(shape=self.img_shape)

        # Noise input
        z_disc = Input(shape=(self.latent_dim,))
        # Generate image based of noise (fake sample)
        fake_img = self.generator(z_disc)

        # Discriminator determines validity of the real and fake images
        fake = self.critic(fake_img)
        valid = self.critic(real_img)

        # Construct weighted average between real and fake images
        interpolated_img = RandomWeightedAverage()([real_img, fake_img])
        # Determine validity of weighted sample
        validity_interpolated = self.critic(interpolated_img)

        # Use Python partial to provide loss function with additional
        # 'averaged_samples' argument
        partial_gp_loss = partial(self.gradient_penalty_loss,
                          averaged_samples=interpolated_img)
        partial_gp_loss.__name__ = 'gradient_penalty' # Keras requires function names

        self.critic_model = Model(inputs=[real_img, z_disc],
                            outputs=[valid, fake, validity_interpolated])
        self.critic_model.compile(loss=[self.wasserstein_loss,
                                              self.wasserstein_loss,
                                              partial_gp_loss],
                                        optimizer=optimizer,
                                        loss_weights=[1, 1, 10])
        #-------------------------------
        # Construct Computational Graph
        #         for Generator
        #-------------------------------

        # For the generator we freeze the critic's layers
        self.critic.trainable = False
        self.generator.trainable = True

        # Sampled noise for input to generator
        z_gen = Input(shape=(100,))
        # Generate images based of noise
        img = self.generator(z_gen)
        # Discriminator determines validity
        valid = self.critic(img)
        # Defines generator model
        self.generator_model = Model(z_gen, valid)
        self.generator_model.compile(loss=self.wasserstein_loss, optimizer=optimizer)


    def build_generator(self):

        model = Sequential()

        model.add(Dense(128 * 64 * 64, activation="relu", input_dim=self.latent_dim))
        model.add(Reshape((64, 64, 128)))
        model.add(UpSampling2D())
        model.add(Conv2D(128, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(UpSampling2D())
        model.add(Conv2D(64, kernel_size=4, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(Activation("relu"))
        model.add(Conv2D(self.channels, kernel_size=4, padding="same"))
        model.add(Activation("tanh"))

        model.summary()

        noise = Input(shape=(self.latent_dim,))
        img = model(noise)

        return Model(noise, img)



    def train(self, epochs, batch_size, sample_interval=50):


        # Adversarial ground truths
        valid = -np.ones((batch_size, 1))
        fake =  np.ones((batch_size, 1))
        dummy = np.zeros((batch_size, 1)) # Dummy gt for gradient penalty
        for epoch in range(epochs):

            for _ in range(self.n_critic):

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

                # Select a random batch of images
                X_train = self.x_train_batch
                X_train = ((np.array(X_train, dtype=np.float32) - 127.5) / 127.5)
                imgs = X_train
                # Sample generator input
                noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
                # Train the critic
                d_loss = self.critic_model.train_on_batch([imgs, noise],
                                                                [valid, fake, dummy])

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

            g_loss = self.generator_model.train_on_batch(noise, valid)

            # Plot the progress
            print ("%d [D loss: %f] [G loss: %f]" % (epoch, d_loss[0], g_loss))

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

試したこと
色々と 数値を変えて試してみましたが 解決に至りませんでした

どなたか わかる方 ご教示 よろしく お願い致します

追記：

画像は　256 x 256 のカラー画像です (538枚)

他 ^C　が表示されたコード

class RandomWeightedAverage(_Merge):
    """Provides a (random) weighted average between real and generated image samples"""
    def _merge_function(self, inputs):
        alpha = K.random_uniform((256, 1, 1, 1))
        return (alpha * inputs[0]) + ((1 - alpha) * inputs[1])

def build_critic(self):

        model = Sequential()

        model.add(Conv2D(256, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(512, kernel_size=3, strides=2, padding="same"))
        model.add(ZeroPadding2D(padding=((0,1),(0,1))))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(1024, kernel_size=3, strides=2, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Conv2D(2048, kernel_size=3, strides=1, padding="same"))
        model.add(BatchNormalization(momentum=0.8))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dropout(0.25))
        model.add(Flatten())
        model.add(Dense(1))

        model.summary()

        img = Input(shape=self.img_shape)
        validity = model(img)

        return Model(img, validity)