tf.data.experimental.Counter

Creates a Dataset that counts from start in steps of size step. (deprecated)

tf.data.experimental.Counter(
    start=0,
    step=1,
    dtype=tf.dtypes.int64
)

Used in the notebooks

Used in the guide	Used in the tutorials
tf.data: Build TensorFlow input pipelines	Data augmentation

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.data.Dataset.counter(...) instead.

Unlike tf.data.Dataset.range which will stop at some ending number, Counter will produce elements indefinitely.

dataset = tf.data.experimental.Counter().take(5)
list(dataset.as_numpy_iterator())
[0, 1, 2, 3, 4]
dataset.element_spec
TensorSpec(shape=(), dtype=tf.int64, name=None)
dataset = tf.data.experimental.Counter(dtype=tf.int32)
dataset.element_spec
TensorSpec(shape=(), dtype=tf.int32, name=None)
dataset = tf.data.experimental.Counter(start=2).take(5)
list(dataset.as_numpy_iterator())
[2, 3, 4, 5, 6]
dataset = tf.data.experimental.Counter(start=2, step=5).take(5)
list(dataset.as_numpy_iterator())
[2, 7, 12, 17, 22]
dataset = tf.data.experimental.Counter(start=10, step=-1).take(5)
list(dataset.as_numpy_iterator())
[10, 9, 8, 7, 6]

Args
start	(Optional.) The starting value for the counter. Defaults to 0.
step	(Optional.) The step size for the counter. Defaults to 1.
dtype	(Optional.) The data type for counter elements. Defaults to tf.int64.

Returns
A Dataset of scalar dtype elements.