tf.feature_column.sequence_numeric_column
Returns a feature column that represents sequences of numeric data.
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)
| 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. |