(inputs, labels)
return tf.train.SequenceExample(feature_lists=feature_lists)
Returns a SequenceExample for the given inputs and labels.

  Args:
    inputs: A list of input vectors. Each input vector is a list of floats.
    labels: A list of ints.

  Returns:
    A tf.train.SequenceExample containing inputs and labels.
Returns a SequenceExample for the given inputs and labels.
Returns a SequenceExample for the given inputs and labels .
def make_sequence_example(inputs, labels):
  """Returns a SequenceExample for the given inputs and labels.

  Args:
    inputs: A list of input vectors. Each input vector is a list of floats.
    labels: A list of ints.

  Returns:
    A tf.train.SequenceExample containing inputs and labels.
  """
  input_features = [
      tf.train.Feature(float_list=tf.train.FloatList(value=input_))
      for input_ in inputs]
  label_features = []
  for label in labels:
    if isinstance(label, numbers.Number):
      label = [label]
    label_features.append(
        tf.train.Feature(int64_list=tf.train.Int64List(value=label)))
  feature_list = {
      'inputs': tf.train.FeatureList(feature=input_features),
      'labels': tf.train.FeatureList(feature=label_features)
  }
  feature_lists = tf.train.FeatureLists(feature_list=feature_list)
  return tf.train.SequenceExample(feature_lists=feature_lists)
def make_sequence_example ( inputs , labels ) : input_features = [ tf . train . Feature ( float_list = tf . train . FloatList ( value = input_ ) ) for input_ in inputs ] label_features = [ ] for label in labels : if isinstance ( label , numbers . Number ) : label = [ label ] label_features . append ( tf . train . Feature ( int64_list = tf . train . Int64List ( value = label ) ) ) feature_list = { 'inputs' : tf . train . FeatureList ( feature = input_features ) , 'labels' : tf . train . FeatureList ( feature = label_features ) } feature_lists = tf . train . FeatureLists ( feature_list = feature_list ) return tf . train . SequenceExample ( feature_lists = feature_lists )