diff options
Diffstat (limited to 'tensorflow/g3doc/api_docs/python/io_ops.md')
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/io_ops.md | 99 |
1 files changed, 72 insertions, 27 deletions
diff --git a/tensorflow/g3doc/api_docs/python/io_ops.md b/tensorflow/g3doc/api_docs/python/io_ops.md index ca1c3c6788..dbb358f45f 100644 --- a/tensorflow/g3doc/api_docs/python/io_ops.md +++ b/tensorflow/g3doc/api_docs/python/io_ops.md @@ -1640,7 +1640,7 @@ Returns tensor num_epochs times and then raises an OutOfRange error. ##### Args: -* <b>`tensor`</b>: Any Tensor. +* <b>`tensor`</b>: Any `Tensor`. * <b>`num_epochs`</b>: An integer (optional). If specified, limits the number of steps the output tensor may be evaluated. * <b>`name`</b>: A name for the operations (optional). @@ -1672,27 +1672,27 @@ Produces the integers from 0 to limit-1 in a queue. ##### Returns: - A Queue with the output integers. A QueueRunner for the Queue - is added to the current Graph's QUEUE_RUNNER collection. + A Queue with the output integers. A `QueueRunner` for the Queue + is added to the current `Graph`'s `QUEUE_RUNNER` collection. - - - ### `tf.train.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, name=None)` {#slice_input_producer} -Produces a slice of each Tensor in tensor_list. +Produces a slice of each `Tensor` in `tensor_list`. -Implemented using a Queue -- a QueueRunner for the Queue -is added to the current Graph's QUEUE_RUNNER collection. +Implemented using a Queue -- a `QueueRunner` for the Queue +is added to the current `Graph`'s `QUEUE_RUNNER` collection. ##### Args: -* <b>`tensor_list`</b>: A list of Tensors. Every Tensor in tensor_list must - have the same size in the first dimension. +* <b>`tensor_list`</b>: A list of `Tensor` objects. Every `Tensor` in + `tensor_list` must have the same size in the first dimension. * <b>`num_epochs`</b>: 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 + an `OutOfRange` error. If not specified, `slice_input_producer` can cycle through the slices an unlimited number of times. * <b>`seed`</b>: An integer (optional). Seed used if shuffle == True. * <b>`capacity`</b>: An integer. Sets the queue capacity. @@ -1700,9 +1700,9 @@ is added to the current Graph's QUEUE_RUNNER collection. ##### 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]. + 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]`. - - - @@ -1728,29 +1728,30 @@ Output strings (e.g. filenames) to a queue for an input pipeline. ##### Returns: - A queue with the output strings. A QueueRunner for the Queue - is added to the current Graph's QUEUE_RUNNER collection. + A queue with the output strings. A `QueueRunner` for the Queue + is added to the current `Graph`'s `QUEUE_RUNNER` collection. ### Batching at the end of an input pipeline -These functions add a queue to the graph to assemble a batch of examples, with -possible shuffling. They also add a `QueueRunner` for running the subgraph -that fills that queue. +These functions add a queue to the graph to assemble a batch of +examples, with possible shuffling. They also add a `QueueRunner` for +running the subgraph that fills that queue. -Use [batch](#batch) or [batch_join](#batch_join) for batching examples that have -already been well shuffled. Use [shuffle_batch](#shuffle_batch) or -[shuffle_batch_join](#shuffle_batch_join) for examples that -would benefit from additional shuffling. +Use [`batch`](#batch) or [`batch_join`](#batch_join) for batching +examples that have already been well shuffled. Use +[`shuffle_batch`](#shuffle_batch) or +[`shuffle_batch_join`](#shuffle_batch_join) for examples that would +benefit from additional shuffling. -Use [batch](#batch) or [shuffle_batch](#shuffle_batch) if you want a +Use [`batch`](#batch) or [`shuffle_batch`](#shuffle_batch) if you want a single thread producing examples to batch, or if you have a -single subgraph producing examples but you want to run it in N threads -(where you increase N until it can keep the queue full). Use -[batch_join](#batch_join) or [shuffle_batch_join](#shuffle_batch_join) -if you have N different subgraphs producing examples to batch and you -want them run by N threads. +single subgraph producing examples but you want to run it in *N* threads +(where you increase *N* until it can keep the queue full). Use +[`batch_join`](#batch_join) or [`shuffle_batch_join`](#shuffle_batch_join) +if you have *N* different subgraphs producing examples to batch and you +want them run by *N* threads. - - - @@ -1772,6 +1773,11 @@ first dimension. If an input tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x, y, z]`. The `capacity` argument controls the how long the prefetching is allowed to grow the queues. +*N.B.:* You must ensure that either (i) the `shapes` argument is +passed, or (ii) all of the tensors in `tensor_list` must have +fully-defined shapes. `ValueError` will be raised if neither of +these conditions holds. + ##### Args: @@ -1788,6 +1794,12 @@ controls the how long the prefetching is allowed to grow the queues. A list of tensors with the same number and types as `tensor_list`. +##### Raises: + + +* <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be + inferred from the elements of `tensor_list`. + - - - @@ -1819,6 +1831,11 @@ same size in the first dimension. The slices of any input tensor The `capacity` argument controls the how long the prefetching is allowed to grow the queues. +*N.B.:* You must ensure that either (i) the `shapes` argument is +passed, or (ii) all of the tensors in `tensor_list_list` must have +fully-defined shapes. `ValueError` will be raised if neither of +these conditions holds. + ##### Args: @@ -1836,6 +1853,12 @@ grow the queues. A list of tensors with the same number and types as `tensor_list_list[i]`. +##### Raises: + + +* <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be + inferred from the elements of `tensor_list_list`. + - - - @@ -1875,6 +1898,11 @@ image_batch, label_batch = tf.train.shuffle_batch( min_after_dequeue=10000) ``` +*N.B.:* You must ensure that either (i) the `shapes` argument is +passed, or (ii) all of the tensors in `tensor_list` must have +fully-defined shapes. `ValueError` will be raised if neither of +these conditions holds. + ##### Args: @@ -1894,6 +1922,12 @@ image_batch, label_batch = tf.train.shuffle_batch( A list of tensors with the same number and types as `tensor_list`. +##### Raises: + + +* <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be + inferred from the elements of `tensor_list`. + - - - @@ -1927,6 +1961,11 @@ y, z]`, the output will have shape `[batch_size, x, y, z]`. The `capacity` argument controls the how long the prefetching is allowed to grow the queues. +*N.B.:* You must ensure that either (i) the `shapes` argument is +passed, or (ii) all of the tensors in `tensor_list_list` must have +fully-defined shapes. `ValueError` will be raised if neither of +these conditions holds. + ##### Args: @@ -1946,4 +1985,10 @@ grow the queues. A list of tensors with the same number and types as `tensor_list_list[i]`. +##### Raises: + + +* <b>`ValueError`</b>: If the `shapes` are not specified, and cannot be + inferred from the elements of `tensor_list_list`. + |