tf.train.input_producer

Output the rows of input_tensor to a queue for an input pipeline. (deprecated)

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.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
)

Note: if num_epochs is not None, this function creates local counter epochs. Use local_variables_initializer() to initialize local variables.

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.