批量数据处理，next_batch()

# 随机取batch_size个训练样本
import numpy as np
#train_data训练集特征，train_target训练集对应的标签，batch_size
def next_batch(train_data, train_target, batch_size):
#打乱数据集
index = [ i for i in range(0,len(train_target)) ]
np.random.shuffle(index);
#建立batch_data与batch_target的空列表
batch_data = [];
batch_target = [];
#向空列表加入训练集及标签
for i in range(0,batch_size):
batch_data.append(train_data[index[i]]);
batch_target.append(train_target[index[i]])
return batch_data, batch_target #返回
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版权声明：本文为CSDN博主「黄鑫huangxin」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/qq_33373858/article/details/83012236