# 导包 import numpy as np import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier # 获取数据 feature = [] target = [] for i in range(10): for j in range(1,501): img_arr = plt.imread('F:/data/%d/%d_%d.bmp'%(i,i,j)) feature.append(
import numpy as np import matplotlib .pyplot as plt from sklearn.neighbors import KNeighborsClassifier 读取样本数据,图片 样本数据的提取 特征:每一张图片对应的numpy数组 目标:0,1,2,3,4,5,6,7,8,9 feature = [] target = [] for i in range(10):#i:0-9表示的是文件夹的名称 for j in range(1,501):#j:1
# -*- coding: utf-8 -*- import numpy as np np.random.seed(1337) from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import SimpleRNN,Activation,Dense from keras.optimizers import Ada