Transforms a tf.Example proto (as a string) into typed tensors.
tf.raw_ops.ParseSingleExample(
serialized,
dense_defaults,
num_sparse,
sparse_keys,
dense_keys,
sparse_types,
dense_shapes,
name=None
)
Args | |
|---|---|
serialized
|
A Tensor of type string.
A vector containing a batch of binary serialized Example protos.
|
dense_defaults
|
A list of Tensor objects with types from: float32, int64, string.
A list of Tensors (some may be empty), whose length matches
the length of dense_keys. dense_defaults[j] provides default values
when the example's feature_map lacks dense_key[j]. If an empty Tensor is
provided for dense_defaults[j], then the Feature dense_keys[j] is required.
The input type is inferred from dense_defaults[j], even when it's empty.
If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined,
then the shape of dense_defaults[j] must match that of dense_shapes[j].
If dense_shapes[j] has an undefined major dimension (variable strides dense
feature), dense_defaults[j] must contain a single element:
the padding element.
|
num_sparse
|
An int that is >= 0.
The number of sparse features to be parsed from the example. This
must match the lengths of sparse_keys and sparse_types.
|
sparse_keys
|
A list of strings. A list of num_sparse strings.
The keys expected in the Examples' features associated with sparse values.
|
dense_keys
|
A list of strings.
The keys expected in the Examples' features associated with dense
values.
|
sparse_types
|
A list of tf.DTypes from: tf.float32, tf.int64, tf.string.
A list of num_sparse types; the data types of data in each
Feature given in sparse_keys.
Currently the ParseSingleExample op supports DT_FLOAT (FloatList),
DT_INT64 (Int64List), and DT_STRING (BytesList).
|
dense_shapes
|
A list of shapes (each a tf.TensorShape or list of ints).
The shapes of data in each Feature given in dense_keys.
The length of this list must match the length of dense_keys. The
number of elements in the Feature corresponding to dense_key[j] must
always equal dense_shapes[j].NumEntries(). If dense_shapes[j] ==
(D0, D1, ..., DN) then the shape of output Tensor dense_values[j]
will be (D0, D1, ..., DN): In the case dense_shapes[j] = (-1, D1,
..., DN), the shape of the output Tensor dense_values[j] will be (M,
D1, .., DN), where M is the number of blocks of elements of length
D1 * .... * DN, in the input.
|
name
|
A name for the operation (optional). |