Function parse_example_v2

tensorflow/python/ops/parsing_ops.py:585–808

Parses `Example` protos into a `dict` of tensors. Parses a number of serialized [`Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto) protos given in `serialized`. We refer to `serialized` as a batch with `batch_size` many entries of individual `Example` protos.

(serialized, features, example_names=None, name=None)

@tf_export("io.parse_example", v1=[])
def parse_example_v2(serialized, features, example_names=None, name=None):
  # pylint: disable=line-too-long
  """Parses `Example` protos into a `dict` of tensors.

  Parses a number of serialized [`Example`](https://www.tensorflow.org/code/tensorflow/core/example/example.proto)
  protos given in `serialized`. We refer to `serialized` as a batch with
  `batch_size` many entries of individual `Example` protos.

  `example_names` may contain descriptive names for the corresponding serialized
  protos. These may be useful for debugging purposes, but they have no effect on
  the output. If not `None`, `example_names` must be the same length as
  `serialized`.

  This op parses serialized examples into a dictionary mapping keys to `Tensor`
  and `SparseTensor` objects. `features` is a dict from keys to `VarLenFeature`,
  `SparseFeature`, and `FixedLenFeature` objects. Each `VarLenFeature`
  and `SparseFeature` is mapped to a `SparseTensor`, and each
  `FixedLenFeature` is mapped to a `Tensor`.

  Each `VarLenFeature` maps to a `SparseTensor` of the specified type
  representing a ragged matrix. Its indices are `[batch, index]` where `batch`
  identifies the example in `serialized`, and `index` is the value's index in
  the list of values associated with that feature and example.

  Each `SparseFeature` maps to a `SparseTensor` of the specified type
  representing a Tensor of `dense_shape` `[batch_size] + SparseFeature.size`.
  Its `values` come from the feature in the examples with key `value_key`.
  A `values[i]` comes from a position `k` in the feature of an example at batch
  entry `batch`. This positional information is recorded in `indices[i]` as
  `[batch, index_0, index_1, ...]` where `index_j` is the `k-th` value of
  the feature in the example at with key `SparseFeature.index_key[j]`.
  In other words, we split the indices (except the first index indicating the
  batch entry) of a `SparseTensor` by dimension into different features of the
  `Example`. Due to its complexity a `VarLenFeature` should be preferred over a
  `SparseFeature` whenever possible.

  Each `FixedLenFeature` `df` maps to a `Tensor` of the specified type (or
  `tf.float32` if not specified) and shape `(serialized.size(),) + df.shape`.

  `FixedLenFeature` entries with a `default_value` are optional. With no default
  value, we will fail if that `Feature` is missing from any example in
  `serialized`.

  Each `FixedLenSequenceFeature` `df` maps to a `Tensor` of the specified type
  (or `tf.float32` if not specified) and shape
  `(serialized.size(), None) + df.shape`.
  All examples in `serialized` will be padded with `default_value` along the
  second dimension.

  Examples:

  For example, if one expects a `tf.float32` `VarLenFeature` `ft` and three
  serialized `Example`s are provided:

  ```
  serialized = [
    features
      { feature { key: "ft" value { float_list { value: [1.0, 2.0] } } } },