Tensorflow tf.data中tf.SparseFeature的等价物

我目前正在研究的神经网络接受稀疏张量作为输入。我正在从TFR记录中读取数据，如下所示：

_, examples = tf.TFRecordReader(options=options).read_up_to(
    filename_queue, num_records=batch_size)

features = tf.parse_example(examples, features={
          'input_feat': tf.SparseFeature(index_key='input_feat_idx',
                                         value_key='input_feat_values',
                                         dtype=tf.int64,
                                         size=SIZE_FEATURE)})

---

它的工作原理很有魅力，但我正在研究

tf.data

API，它看起来对许多任务都更方便，我不知道如何读取

tf.SparseTensor

对象，就像我使用

tf.RecordReader

和

tf.parse_example（）

一样。有什么想法吗？

TensorFlow 1.5将在核心转换中添加对

tf.SparseTensor

的本机支持。（如果您

pip每晚安装tf

，或者从TensorFlow的主分支上的源代码构建，则此选项当前可用。）这意味着您可以按照以下方式编写管道：

# Create a dataset of string records from the input files.
dataset = tf.data.TFRecordReader(filenames)

# Convert each string record into a `tf.SparseTensor` representing a single example.
dataset = dataset.map(lambda record: tf.parse_single_example(
    record, features={'input_feat': tf.SparseFeature(index_key='input_feat_idx',
                                                     value_key='input_feat_values',
                                                     dtype=tf.int64,
                                                     size=SIZE_FEATURE)})

# Stack together up to `batch_size` consecutive elements into a `tf.SparseTensor`
# representing a batch of examples.
dataset = dataset.batch(batch_size)

# Create an iterator to access the elements of `dataset` sequentially.
iterator = dataset.make_one_shot_iterator()

# `next_element` is a `tf.SparseTensor`.
next_element = iterator.get_next()

---

TensorFlow 1.5将在核心转换中添加对

tf.SparseTensor

的本机支持。（如果您

pip每晚安装tf

，或者从TensorFlow的主分支上的源代码构建，则此选项当前可用。）这意味着您可以按照以下方式编写管道：

# Create a dataset of string records from the input files.
dataset = tf.data.TFRecordReader(filenames)

# Convert each string record into a `tf.SparseTensor` representing a single example.
dataset = dataset.map(lambda record: tf.parse_single_example(
    record, features={'input_feat': tf.SparseFeature(index_key='input_feat_idx',
                                                     value_key='input_feat_values',
                                                     dtype=tf.int64,
                                                     size=SIZE_FEATURE)})

# Stack together up to `batch_size` consecutive elements into a `tf.SparseTensor`
# representing a batch of examples.
dataset = dataset.batch(batch_size)

# Create an iterator to access the elements of `dataset` sequentially.
iterator = dataset.make_one_shot_iterator()

# `next_element` is a `tf.SparseTensor`.
next_element = iterator.get_next()