tf.train.input_producer

tf.train.input_producer(
    input_tensor,
    element_shape=None,
    num_epochs=None,
    shuffle=True,
    seed=None,
    capacity=32,
    shared_name=None,
    summary_name=None,
    name=None,
    cancel_op=None
)

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

Output the rows of input_tensor 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 Instructions for updating: Queue-based input pipelines have been replaced by tf.data.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(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

Args:

  • input_tensor: A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined.
  • element_shape: (Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be inferred.
  • num_epochs: (Optional.) An integer. If specified input_producer produces each row of input_tensor num_epochs times before generating an OutOfRange error. If not specified, input_producer can cycle through the rows of input_tensor an unlimited number of times.
  • shuffle: (Optional.) A boolean. If true, the rows are randomly shuffled within each epoch.
  • seed: (Optional.) An integer. The seed to use if shuffle is true.
  • capacity: (Optional.) The capacity of the queue to be used for buffering the input.
  • shared_name: (Optional.) If set, this queue will be shared under the given name across multiple sessions.
  • summary_name: (Optional.) If set, a scalar summary for the current queue size will be generated, using this name as part of the tag.
  • name: (Optional.) A name for queue.
  • cancel_op: (Optional.) Cancel op for the queue

Returns:

A queue with the output rows. A QueueRunner for the queue is added to the current QUEUE_RUNNER collection of the current graph.

Raises:

  • ValueError: If the shape of the input cannot be inferred from the arguments.
  • RuntimeError: If called with eager execution enabled.

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.