tf.contrib.feature_column.sequence_numeric_column

tf.contrib.feature_column.sequence_numeric_column(
    key,
    shape=(1,),
    default_value=0.0,
    dtype=tf.dtypes.float32,
    normalizer_fn=None
)

Defined in tensorflow/contrib/feature_column/python/feature_column/sequence_feature_column.py.

Returns a feature column that represents sequences of numeric data.

Example:

temperature = sequence_numeric_column('temperature')
columns = [temperature]

features = tf.parse_example(..., features=make_parse_example_spec(columns))
input_layer, sequence_length = sequence_input_layer(features, columns)

rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
outputs, state = tf.nn.dynamic_rnn(
    rnn_cell, inputs=input_layer, sequence_length=sequence_length)

Args:

• key: A unique string identifying the input features.
• shape: The shape of the input data per sequence id. E.g. if shape=(2,), each example must contain 2 * sequence_length values.
• default_value: A single value compatible with dtype that is used for padding the sparse data into a dense Tensor.
• dtype: The type of values.
• normalizer_fn: If not None, a function that can be used to normalize the value of the tensor after default_value is applied for parsing. Normalizer function takes the input Tensor as its argument, and returns the output Tensor. (e.g. lambda x: (x - 3.0) / 4.2). Please note that even though the most common use case of this function is normalization, it can be used for any kind of Tensorflow transformations.

Returns:

A _SequenceNumericColumn.

Raises:

• TypeError: if any dimension in shape is not an int.
• ValueError: if any dimension in shape is not a positive integer.
• ValueError: if dtype is not convertible to tf.float32.