tf.train.string_input_producer

tf.train.string_input_producer(
    string_tensor,
    num_epochs=None,
    shuffle=True,
    seed=None,
    capacity=32,
    shared_name=None,
    name=None,
    cancel_op=None
)

Defined in tensorflow/python/training/input.py.

Output strings (e.g. filenames) to a queue for an input pipeline. (deprecated)

THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Queue-based input pipelines have been replaced by tf.data. Use tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Args:

  • string_tensor: A 1-D string tensor with the strings to produce.
  • num_epochs: An integer (optional). If specified, string_input_producer produces each string from string_tensor num_epochs times before generating an OutOfRange error. If not specified, string_input_producer can cycle through the strings in string_tensor an unlimited number of times.
  • shuffle: Boolean. If true, the strings are randomly shuffled within each epoch.
  • seed: An integer (optional). Seed used if shuffle == True.
  • capacity: An integer. Sets the queue capacity.
  • shared_name: (optional). If set, this queue will be shared under the given name across multiple sessions. All sessions open to the device which has this queue will be able to access it via the shared_name. Using this in a distributed setting means each name will only be seen by one of the sessions which has access to this operation.
  • name: A name for the operations (optional).
  • cancel_op: Cancel op for the queue (optional).

Returns:

A queue with the output strings. A QueueRunner for the Queue is added to the current Graph's QUEUE_RUNNER collection.

Raises:

  • ValueError: If the string_tensor is a null Python list. At runtime, will fail with an assertion if string_tensor becomes a null tensor.

Eager Compatibility

Input pipelines based on Queues are not supported when eager execution is enabled. Please use the tf.data API to ingest data under eager execution.