Question

如何多次输出数据集中的值？（数据集由tensorflow的Dataset API创建）

import tensorflow as tf

dataset = tf.contrib.data.Dataset.range(100)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
sess = tf.Session()
epoch = 10

for i in range(epoch):
   for j in range(100):
      value = sess.run(next_element)
      assert j == value
      print(j)

错误讯息：

tensorflow.python.framework.errors_impl.OutOfRangeError: End of sequence
 [[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[]], output_types=[DT_INT64], _device="/job:localhost/replica:0/task:0/cpu:0"](OneShotIterator)]]

如何使这项工作？

Answer 1

首先，我建议你阅读Data Set Guide。描述了DataSet API的所有细节。

您的问题是多次迭代数据。以下是两种解决方案：

立刻迭代所有时代，没有关于个别时代结束的信息

import tensorflow as tf

epoch   = 10
dataset = tf.data.Dataset.range(100)
dataset = dataset.repeat(epoch)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
sess = tf.Session()

num_batch = 0
j = 0
while True:
    try:
        value = sess.run(next_element)
        assert j == value
        j += 1
        num_batch += 1
        if j > 99: # new epoch
            j = 0
    except tf.errors.OutOfRangeError:
        break

print ("Num Batch: ", num_batch)

第二个选项告诉您结束每个纪元，所以你可以。检查验证丢失：

import tensorflow as tf

epoch = 10
dataset = tf.data.Dataset.range(100)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
sess = tf.Session()

num_batch = 0

for e in range(epoch):
    print ("Epoch: ", e)
    j = 0
    sess.run(iterator.initializer)
    while True:
        try:
            value = sess.run(next_element)
            assert j == value
            j += 1
            num_batch += 1
        except tf.errors.OutOfRangeError:
            break

print ("Num Batch: ", num_batch)

Answer 2

如果您的tensorflow版本是1.3+，我推荐使用高级API tf.train.MonitoredTrainingSession。此API创建的sess可以使用tf.errors.OutOfRangeError自动检测sess.should_stop()。对于大多数培训情况，您需要随机播放数据并获取批处理，我已在以下代码中添加了这些内容。

import tensorflow as tf

epoch = 10
dataset = tf.data.Dataset.range(100)
dataset = dataset.shuffle(buffer_size=100) # comment this line if you don't want to shuffle data
dataset = dataset.batch(batch_size=32)     # batch_size=1 if you want to get only one element per step
dataset = dataset.repeat(epoch)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()

num_batch = 0
with tf.train.MonitoredTrainingSession() as sess:
    while not sess.should_stop():
        value = sess.run(next_element)
        num_batch += 1
        print("Num Batch: ", num_batch)

Answer 3

试试这个

while True:
  try:
    print(sess.run(value))
  except tf.errors.OutOfRangeError:
    break

每当数据集迭代器到达数据末尾时，它都会引发tf.errors.OutOfRangeError，您可以使用except来捕获它，并从头开始数据集。

Answer 4

类似于Toms的答案，对于tensorflow 2+，您可以使用以下高级API调用（在他的答案中建议的代码在tensorflow 2+中已弃用）：

epoch = 10
batch_size = 32
dataset = tf.data.Dataset.range(100) 

dataset = dataset.shuffle(buffer_size=100) # comment this line if you don't want to shuffle data
dataset = dataset.batch(batch_size=batch_size)
dataset = dataset.repeat(epoch)

num_batch = 0
for batch in dataset:
        num_batch += 1
        print("Num Batch: ", num_batch)

跟踪进度的有用电话是要迭代的批次总数（在batch和repeat调用之后之后使用）

num_batches = tf.data.experimental.cardinality(dataset)

请注意，目前（tensorflow 2.1），cardinality方法仍处于实验阶段。

如何使用tensorflow数据集API多次迭代数据集

4 个答案: