https://keras.io/api/datasets/
DNN--全连接神经网络
fashion_mnist玩具集
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow.keras as ks
fm=ks.datasets.fashion_mnist# 德国时装多分类
#6万个训练数据,1万个测试数据,每个样本形状28*28
(train_imgs,train_lbs),(test_imgs,test_lbs)=fm.load_data()
plt.figure()
plt.imshow(train_imgs0)
plt.colorbar()
plt.show()
plt.figure(figsize=(10,10))
for i in range(25):
#分成5行5列的子视图
plt.subplot(5,5,i+1)
#不显示轴
# plt.axis('off')
#不显示刻度
plt.xticks(\[\])
plt.yticks(\[\])
plt.imshow(train_imgsi,cmap=plt.cm.binary)
plt.xlabel(namestrain_lbs\[i])
train_imgs=train_imgs/255.0
test_imgs=test_imgs/255.0
#神经网络堆层
model=ks.Sequential([
#输入图片形状是(28x28),也可以写成(28*28,)因为图片是
#被摊平的
ks.layers.Flatten(input_shape=(28,28)),
#128个神经元,激活函数relu,隐藏层
ks.layers.Dense(128,activation='relu'),
#因为目标分类是10种,所以目标层神经元10个
ks.layers.Dense(10,activation='softmax')
])
model.summary()#28*28,(784+1)*128,(128+1)*10
#one-hot编码
from keras.utils import to_categorical
#能把标签处理成机器容易识别的行式,one-hot编码
train_lbs=to_categorical(train_lbs)
test_lbs=to_categorical(test_lbs)
#编译模型,SparseCategoricalCrossentropy能做onehot编码处理
model.compile(optimizer='adam',loss=ks.losses.SparseCategoricalCrossentropy(
from_logits=True),metrics='accuracy')
#batch_size=128小批量的丢进去
model.fit(train_imgs,train_lbs,epochs=10,batch_size=128)
#sigm=lambda x:1/(1+np.exp(-x))
#sigm(5.757487 )
#误差,准确率,评估
test_loss,test_acc=model.evaluate(test_imgs,test_lbs,verbose=2)
display(type(test_loss),type(test_acc),test_loss,test_acc)
#这个是线性值Z
np.set_printoptions(suppress=True)
a=model.predict(test_imgs)
display(a0,test_lbs0)