9

我从youtube-8m 项目*.tfrecord下载了一些数据。您可以使用以下命令下载数据的“小”部分：

curl data.yt8m.org/download.py | shard=1,100 partition=2/video/train mirror=us python

我试图了解如何使用新的 tf.data API。我想熟悉人们遍历数据集的典型方式。我一直在使用 TF 网站上的指南和这张幻灯片：Derek Murray 的幻灯片

这是我定义数据集的方式：

# Use interleave() and prefetch() to read many files concurrently.
files = tf.data.Dataset.list_files("./youtube_vids/*.tfrecord")
dataset = files.interleave(lambda x: tf.data.TFRecordDataset(x).prefetch(100),
                           cycle_length=8)

# Use num_parallel_calls to parallelize map().
dataset = dataset.map(lambda record: tf.parse_single_example(record, feature_map),
                     num_parallel_calls=2) #

# put in x,y output form
dataset = dataset.map(lambda x: (x['mean_rgb'], x['id']))

# shuffle
dataset = dataset.shuffle(10000)

#one epoch
dataset = dataset.repeat(1)
dataset = dataset.batch(200)

#Use prefetch() to overlap the producer and consumer.
dataset = dataset.prefetch(10)

现在，我知道在急切执行模式下我可以

for x,y in dataset:
    x,y

但是，当我尝试按如下方式创建迭代器时：

# A one-shot iterator automatically initializes itself on first use.
iterator = dset.make_one_shot_iterator()

# The return value of get_next() matches the dataset element type.
images, labels = iterator.get_next()

并与会话一起运行

with tf.Session() as sess:

    # Loop until all elements have been consumed.
    try:
        while True:
            r = sess.run(images)
    except tf.errors.OutOfRangeError:
        pass

我收到警告

Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.

所以，这是我的问题：

在会话中迭代数据集的正确方法是什么？只是v1和v2差异的问题吗？

此外，将数据集直接传递给估计器的建议意味着输入函数也有一个迭代器，如上面 Derek Murray 的幻灯片中定义的那样，对吗？

4

1 回答 1

9

As for Estimator API, no you don't have to specify iterator, just pass dataset object as input function. 

def input_fn(filename):
    dataset = tf.data.TFRecordDataset(filename)
    dataset = dataset.shuffle().repeat()
    dataset = dataset.map(parse_func)
    dataset = dataset.batch()
    return dataset

estimator.train(input_fn=lambda: input_fn())

In TF 2.0 dataset became iterable, so, just as warning message says, you can use 

for x,y in dataset:
    x,y
于 2019-05-31T21:49:10.560 回答