# 导入和预处理训练数据集
参考[TensorFlow官网的教程](https://www.tensorflow.org/tutorials/keras/classification)，创建一个简单的图片分类模型。
查看当前TensorFlow版本，单击![](https://support.huaweicloud.com/bestpractice-cloudide/figure/zh-cn_image_0000002377441769.png)或者敲击Shift+Enter运行cell。
```
from __future__ import absolute_import, division, print_function, unicode_literals
# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
# print tensorflow version
print(tf.__version__)
```
![](https://support.huaweicloud.com/bestpractice-cloudide/figure/zh-cn_image_0000002343243998.gif "点击放大")
下载Fashion MNIST图片数据集，该数据集包含了10个类型共60000张训练图片以及10000张测试图片。
```
# download Fashion MNIST dataset
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
```
![](https://support.huaweicloud.com/bestpractice-cloudide/figure/zh-cn_image_0000002377281893.gif "点击放大")
对训练数据做预处理，并查看训练集中最开始的25个图片。
```
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
# preprocessing
train_images = train_images / 255.0
test_images = test_images / 255.0
# display first 25 images
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()
```
![](https://support.huaweicloud.com/bestpractice-cloudide/figure/zh-cn_image_0000002343403818.gif "点击放大")