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Output the rows of input_tensor
to a queue for an input pipeline. (deprecated)
tf.compat.v1.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
)
Warning: 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Queue-based input pipelines have been replaced by tf.data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(...)
.
Note: if num_epochs
is not None
, this function creates local counter
epochs
. Use local_variables_initializer()
to initialize local variables.
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 queueA queue with the output rows. A QueueRunner
for the queue is
added to the current QUEUE_RUNNER
collection of the current
graph.
ValueError
: If the shape of the input cannot be inferred from the arguments.RuntimeError
: If called with eager execution enabled.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.