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 Instructions for updating: Queue-based input pipelines have been replaced by tf.data.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.