使用以下代码可以实现自己数据库的next_batch函数
# -*- coding: utf-8 -*-
import numpy as np
np.set_printoptions(threshold=200)
class Dataset:
def __init__(self,data,label):
self._index_in_epoch = 0
self._epochs_completed = 0
self._data = data
self._label = label
self._num_examples = data.shape[0]
pass
@property
def data(self):
return self._data
@property
def label(self):
return self._label
def next_batch(self,batch_size,shuffle = True):
start = self._index_in_epoch
if start == 0 and self._epochs_completed == 0:
idx = np.arange(0, self._num_examples) # get all possible indexes
np.random.shuffle(idx) # shuffle indexe
self._data = self.data[idx] # get list of `num` random samples
self._label = self.label[idx]
# go to the next batch
if start + batch_size > self._num_examples:
self._epochs_completed += 1
rest_num_examples = self._num_examples - start
data_rest_part = self.data[start:self._num_examples]
label_rest_part = self.label[start:self._num_examples]
idx0 = np.arange(0, self._num_examples) # get all possible indexes
np.random.shuffle(idx0) # shuffle indexes
self._data = self.data[idx0] # get list of `num` random samples
self._label = self.label[idx0] # get list of `num` random samples
start = 0
self._index_in_epoch = batch_size - rest_num_examples #avoid the case where the #sample != integar times of batch_size
end = self._index_in_epoch
data_new_part = self._data[start:end]
label_new_part = self._label[start:end]
return np.concatenate((data_rest_part, data_new_part), axis=0), np.concatenate((label_rest_part, label_new_part), axis=0)
else:
self._index_in_epoch += batch_size
end = self._index_in_epoch
return self._data[start:end], self._label[start:end]