import numpy as np
def batch_generator(all_data , batch_size, shuffle=True):
 """
 :param all_data : all_data整個(gè)數(shù)據(jù)集，包含輸入和輸出標(biāo)簽
 :param batch_size: batch_size表示每個(gè)batch的大小
 :param shuffle: 是否打亂順序
 :return:
 """
 # 輸入all_datas的每一項(xiàng)必須是numpy數(shù)組，保證后面能按p所示取值
 all_data = [np.array(d) for d in all_data]
 # 獲取樣本大小
 data_size = all_data[0].shape[0]
 print("data_size: ", data_size)
 if shuffle:
  # 隨機(jī)生成打亂的索引
  p = np.random.permutation(data_size)
  # 重新組織數(shù)據(jù)
  all_data = [d[p] for d in all_data]
 batch_count = 0
 while True:
  # 數(shù)據(jù)一輪循環(huán)(epoch)完成，打亂一次順序
  if batch_count * batch_size + batch_size > data_size:
   batch_count = 0
   if shuffle:
    p = np.random.permutation(data_size)
    all_data = [d[p] for d in all_data]
  start = batch_count * batch_size
  end = start + batch_size
  batch_count += 1
  yield [d[start: end] for d in all_data]

# 輸入x表示有23個(gè)樣本，每個(gè)樣本有兩個(gè)特征
# 輸出y表示有23個(gè)標(biāo)簽，每個(gè)標(biāo)簽取值為0或1
x = np.random.random(size=[23, 2])
y = np.random.randint(2, size=[23,1])
count = x.shape[0]
batch_size = 5
epochs = 20
batch_num = count // batch_size
batch_gen = batch_generator([x, y], batch_size)
for i in range(epochs):
 print("##### epoch %s ##### " % i)
 for j in range(batch_num):
  batch_x, batch_y = next(batch_gen)
  print("-----epoch=%s, batch=%s-----" % (i, j))
  print(batch_x, batch_y)

import tensorflow as tf
import numpy as np
def _parse_function(x):
 num_list = np.arange(10)
 return num_list
def _from_tensor_slice(x):
 return tf.data.Dataset.from_tensor_slices(x)
softmax_data = tf.data.Dataset.range(1000) # 構(gòu)造一個(gè)隊(duì)列
softmax_data = softmax_data.map(lambda x:tf.py_func(_parse_function, [x], [tf.int32]))# 將數(shù)據(jù)進(jìn)行傳入
softmax_data = softmax_data.flat_map(_from_tensor_slice) #將數(shù)據(jù)進(jìn)行平鋪, 將其變?yōu)橐痪S的數(shù)據(jù)，from_tensor_slice將數(shù)據(jù)可以輸出
softmax_data = softmax_data.batch(1) #構(gòu)造一個(gè)batch的數(shù)量
softmax_iter = softmax_data.make_initializable_iterator() # 構(gòu)造數(shù)據(jù)迭代器
softmax_element = softmax_iter.get_next() # 獲得一個(gè)batch的數(shù)據(jù)
sess = tf.Session()
sess.run(softmax_iter.initializer) # 數(shù)據(jù)迭代器的初始化操作
print(sess.run(softmax_element)) # 實(shí)際獲得一個(gè)數(shù)據(jù)
print(sess.run(softmax_data))