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tfr.data.parse_from_example_in_example

Parses an ExampleInExample batch to a feature map.

An ExampleInExample is a tf.train.Example that has two fields:

  • serialized_context is a scalar of bytes. The value is a serialized tf.train.Example that contains context features.
  • serialized_examples is a repeated field of bytes. The value is a list of serialized tf.train.Example with each representing an example that contains example features.

For example:

serialized_context_string = Serialize({
  features {
    feature {
      key: "query_length"
      value { int64_list { value: 3 } }
    }
  }
})

serialized_examples_string = [
  Serialize({
    features {
      feature {
        key: "unigrams"
        value { bytes_list { value: "tensorflow" } }
      }
      feature {
        key: "utility"
        value { float_list { value: 0.0 } }
      }
    }
  }),

  Serialize({
    features {
      feature {
        key: "unigrams"
        value { bytes_list { value: ["learning" "to" "rank" } }
      }
      feature {
        key: "utility"
        value { float_list { value: 1.0 } }
      }
    }
  })
]

serialized_context_string_2 = Serialize({
  features {
    feature {
      key: "query_length"
      value { int64_list { value: 2 } }
    }
  }
})

serialized_examples_string_2 = [
  Serialize({
    features {
      feature {
        key: "unigrams"
        value { bytes_list { value: "gbdt" } }
      }
      feature {
        key: "utility"
        value { float_list { value: 0.0 } }
      }
    }
  }),

  Serialize({
    features {
      feature {
        key: "unigrams"
        value { bytes_list { value: ["neural" "network" } }
      }
      feature {
        key: "utility"
        value { float_list { value: 1.0 } }
      }
    }
  })
]

serialized = [
  {
    serialized_context: serialized_context_string,
    serialized_examples: serialized_examples_string,
  },
  {
    serialized_context: serialized_context_string_2,
    serialized_examples: serialized_examples_string_2,
  },
]

We can use arguments:

context_feature_spec: {
  "query_length": tf.io.FixedenFeature([1], dtypes.int64),
}
example_feature_spec: {
  "unigrams": tf.io.VarLenFeature(dtypes.string),
  "utility": tf.io.FixedLenFeature([1], dtypes.float32),
}

And the expected output is:

{
  "unigrams": SparseTensor(
    indices=array([[0, 0, 0], [0, 1, 0], [0, 1, 1], [0, 1, 2], [1, 0, 0],
      [1, 1, 0], [1, 1, 1]]),
    values=["tensorflow", "learning", "to", "rank", "gbdt", "neural" ,
      "network"],
    dense_shape=array([2, 2, 3])),
  "utility": [[[ 0.], [ 1.]], [[ 0.], [ 1.]]],
  "query_length": [[3], [2]],
}

serialized (Tensor) 1-D Tensor and each entry is a serialized ExampleListWithContext proto that contains context and example list.
list_size (int) The number of examples for each list. If specified, truncation or padding is applied to make 2nd dim of output Tensors aligned to list_size. Otherwise, the 2nd dim of the output Tensors is dynamic.
context_feature_spec (dict) A mapping from feature keys to FixedLenFeature or VarLenFeature values for context in ExampleListWithContext proto.
example_feature_spec (dict) A mapping from feature keys to FixedLenFeature or VarLenFeature values for examples in ExampleListWithContext proto.
size_feature_name (str) Name of feature for example list sizes. Populates the feature dictionary with a tf.int32 Tensor of shape [batch_size] for this feature name. If None, which is default, this feature is not generated.
mask_feature_name (str) Name of feature for example list masks. Populates the feature dictionary with a tf.bool Tensor of shape [batch_size, list_size] for this feature name. If None, which is default, this feature is not generated.
shuffle_examples (bool) A boolean to indicate whether examples within a list are shuffled before the list is trimmed down to list_size elements (when list has more than list_size elements).
seed (int) A seed passed onto random_ops.uniform() to shuffle examples.

A mapping from feature keys to Tensor or SparseTensor.