问Tensorflow2.0卷积时报错？

from __future__ import absolute_import, division, print_function, unicode_literals

import os

import os.path as osp

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import datasets, layers, models

#import cv2

import numpy as np

import matplotlib.pyplot as plt

# =====================================================================

# 不加这几句，则CONV 报错

physical_devices = tf.config.experimental.list_physical_devices('GPU')

assert len(physical_devices) > 0, "Not enough GPU hardware devices available"

tf.config.experimental.set_memory_growth(physical_devices[0], True)

(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

train_images = train_images.reshape((60000, 28, 28, 1))

test_images = test_images.reshape((10000, 28, 28, 1))

# 特征缩放[0, 1]区间

train_images, test_images = train_images / 255.0, test_images / 255.0

model = models.Sequential()

model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.MaxPooling2D((2, 2)))

model.add(layers.Conv2D(64, (3, 3), activation='relu'))

model.add(layers.Flatten())

model.add(layers.Dense(64, activation='relu'))

model.add(layers.Dense(10, activation='softmax'))

model.summary() # 显示模型的架构

model.compile(optimizer='adam',

loss='sparse_categorical_crossentropy',

metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=5,batch_size=64)

运行后报错：

RuntimeError: Physical devices cannot be modified after being initialized

小白一枚，该怎么办啊，调了好多次，网上都没有该类描述，心态要蹦了