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_tensornum_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.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.compat.v1.train.input_producer\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/training/input.py#L115-L200) |\n\nOutput the rows of `input_tensor` to a queue for an input pipeline. (deprecated) \n\n tf.compat.v1.train.input_producer(\n input_tensor,\n element_shape=None,\n num_epochs=None,\n shuffle=True,\n seed=None,\n capacity=32,\n shared_name=None,\n summary_name=None,\n name=None,\n cancel_op=None\n )\n\n| **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 Instructions for updating: Queue-based input pipelines have been replaced by [`tf.data`](../../../../tf/data).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(...)`.\n| **Note:** if `num_epochs` is not `None`, this function creates local counter `epochs`. Use `local_variables_initializer()` to initialize local variables.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `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. |\n| `element_shape` | (Optional.) A `TensorShape` representing the shape of a row of `input_tensor`, if it cannot be inferred. |\n| `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. |\n| `shuffle` | (Optional.) A boolean. If true, the rows are randomly shuffled within each epoch. |\n| `seed` | (Optional.) An integer. The seed to use if `shuffle` is true. |\n| `capacity` | (Optional.) The capacity of the queue to be used for buffering the input. |\n| `shared_name` | (Optional.) If set, this queue will be shared under the given name across multiple sessions. |\n| `summary_name` | (Optional.) If set, a scalar summary for the current queue size will be generated, using this name as part of the tag. |\n| `name` | (Optional.) A name for queue. |\n| `cancel_op` | (Optional.) Cancel op for the queue |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A queue with the output rows. A `QueueRunner` for the queue is added to the current `QUEUE_RUNNER` collection of the current graph. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|----------------|------------------------------------------------------------------|\n| `ValueError` | If the shape of the input cannot be inferred from the arguments. |\n| `RuntimeError` | If called with eager execution enabled. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\neager compatibility\n-------------------\n\n\u003cbr /\u003e\n\nInput pipelines based on Queues are not supported when eager execution is\nenabled. Please use the [`tf.data`](../../../../tf/data) API to ingest data under eager execution.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e"]]
View source on GitHub