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)

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)

See the guide: Inputs and Readers > Input pipeline

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

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.

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