数据集介绍

fashion mnist数据集是mnist的进阶版本,有10种对应的结果

训练集有60000个,每一个都是28*28的图像,每一个对应一个标签(0-9)表示

测试集有10000个

代码
import tensorflow as tf
import keras
import numpy as np
import matplotlib.pyplot as plt

#导入fashioin_mnist数据集
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

#分别于0-9对应
class_names = ['上衣','裤子','套衫','裙子','外套','凉鞋','衬衫','运动鞋','包包','踝靴']

#压缩像素值到0-1之间
train_images = train_images / 255.0
test_images = test_images / 255.0

#查看前几个数据的图像
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]])
    
model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),   #输入图像大小为28*28
    keras.layers.Dense(128, activation=tf.nn.relu),  #用relu函数作为激活函数
    keras.layers.Dense(10, activation=tf.nn.softmax)   #softmax之后输出10个值,分别表示对应的概率
])

model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(train_images,train_labels,epochs= 10)  #运行完准确率有91.13%

test_loss, test_acc = model.evaluate(test_images, test_labels)

print('Test accuracy:', test_acc)	#运行完在测试集上的准确率为88.58%
#测试集的准确率小于训练集,说明过拟合

参考

https://www.tensorflow.org/tutorials/keras/basic_classification?hl=zh-cn