Implemented using a Queue -- a QueueRunner for the Queue
is added to the current Graph's QUEUE_RUNNER collection.
Args
tensor_list
A list of Tensor objects. Every Tensor in
tensor_list must have the same size in the first dimension.
num_epochs
An integer (optional). If specified, slice_input_producer
produces each slice num_epochs times before generating
an OutOfRange error. If not specified, slice_input_producer can cycle
through the slices an unlimited number of times.
shuffle
Boolean. If true, the integers are randomly shuffled within each
epoch.
seed
An integer (optional). Seed used if shuffle == True.
capacity
An integer. Sets the queue capacity.
shared_name
(optional). If set, this queue will be shared under the given
name across multiple sessions.
name
A name for the operations (optional).
Returns
A list of tensors, one for each element of tensor_list. If the tensor
in tensor_list has shape [N, a, b, .., z], then the corresponding output
tensor will have shape [a, b, ..., z].
Raises
ValueError
if slice_input_producer produces nothing from tensor_list.
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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.compat.v1.train.slice_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#L320-L375) |\n\nProduces a slice of each `Tensor` in `tensor_list`. (deprecated) \n\n tf.compat.v1.train.slice_input_producer(\n tensor_list,\n num_epochs=None,\n shuffle=True,\n seed=None,\n capacity=32,\n shared_name=None,\n name=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 Instructions for updating: Queue-based input pipelines have been replaced by [`tf.data`](../../../../tf/data).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(tuple(tensor_list)).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.\n\nImplemented using a Queue -- a `QueueRunner` for the Queue\nis added to the current `Graph`'s `QUEUE_RUNNER` collection.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `tensor_list` | A list of `Tensor` objects. Every `Tensor` in `tensor_list` must have the same size in the first dimension. |\n| `num_epochs` | An integer (optional). If specified, `slice_input_producer` produces each slice `num_epochs` times before generating an `OutOfRange` error. If not specified, `slice_input_producer` can cycle through the slices an unlimited number of times. |\n| `shuffle` | Boolean. If true, the integers are randomly shuffled within each epoch. |\n| `seed` | An integer (optional). Seed used if shuffle == True. |\n| `capacity` | An integer. Sets the queue capacity. |\n| `shared_name` | (optional). If set, this queue will be shared under the given name across multiple sessions. |\n| `name` | A name for the operations (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A list of tensors, one for each element of `tensor_list`. If the tensor in `tensor_list` has shape `[N, a, b, .., z]`, then the corresponding output tensor will have shape `[a, b, ..., z]`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|----------------------------------------------------------------|\n| `ValueError` | if `slice_input_producer` produces nothing from `tensor_list`. |\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"]]