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