tf.compat.v1.train.slice_input_producer

Produces a slice of each Tensor in tensor_list. (deprecated)

tf.compat.v1.train.slice_input_producer(
    tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32,
    shared_name=None, name=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 Instructions for updating: Queue-based input pipelines have been replaced by tf.data.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(tuple(tensor_list)).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs). If shuffle=False, omit the .shuffle(...).

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.