For dense tensors, the returned Tensor is identical to the output of
parse_example, except there is no batch dimension, the output shape is the
same as the shape given in dense_shape.
For SparseTensors, the first (batch) column of the indices matrix is removed
(the indices matrix is a column vector), the values vector is unchanged, and
the first (batch_size) entry of the shape vector is removed (it is now a
single element vector).
One might see performance advantages by batching Example protos with
parse_example instead of using this function directly.
Args
serialized
A scalar string Tensor, a single serialized Example.
features
A mapping of feature keys to FixedLenFeature or
VarLenFeature values.
name
A name for this operation (optional).
example_names
(Optional) A scalar string Tensor, the associated name.
Returns
A dict mapping feature keys to Tensor and SparseTensor values.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]
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