tf.feature_column.sequence_numeric_column

Returns a feature column that represents sequences of numeric data. (deprecated)

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

Example:

temperature = sequence_numeric_column('temperature') columns = [temperature] features = tf.io.parse_example(..., features=make_parse_example_spec(columns)) sequence_feature_layer = SequenceFeatures(columns) sequence_input, sequence_length = sequence_feature_layer(features) sequence_length_mask = tf.sequence_mask(sequence_length) rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size) rnn_layer = tf.keras.layers.RNN(rnn_cell) outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)

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.

A SequenceNumericColumn.

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.