分类目录：《深入浅出TensorFlow2函数》总目录
函数：
batch(batch_size, drop_remainder=False, num_parallel_calls=None, deterministic=None,name=None)
该函数可以将此数据集的连续元素合并到batch中。
dataset = tf.data.Dataset.range(8) dataset = dataset.batch(3) list(dataset.as_numpy_iterator()) # [array([0, 1, 2], dtype=int64), array([3, 4, 5], dtype=int64)]
函数返回值将有一个额外的外部维度，即batch_size。如果batch_size未将输入元素的数量 N N N平均分割，且drop_remainder为False，则最后一个元素的batch_size为N % batch_size。如果需要依赖于具有相同尺寸的batch，则应将drop_rements参数设置为True，以防止生成较小的批。如果程序要求数据具有静态已知形状，则应使用drop_rements=True。如果没有drop_rements=True，则输出数据集的形状将具有未知的前导维度，因为最终批次可能更小。
参数：
| 参数 | 意义 |
|---|---|
| batch_size | [tf.int64 /tf.Tensor]表示要在单个批次中组合的此数据集的连续元素数。 |
| drop_remainder | [可选，tf.bool /tf.Tensor]表示如果最后一批元素少于批次大小，是否应删除最后一批元素，默认为False。 |
| num_parallel_calls | [可选，tf.int64 /tf.Tensor]表示异步并行计算的批数。如果未指定，将按顺序计算批次。如果值为tf.data.AUTOTUNE被使用，则根据可用资源动态设置并行调用的数量。 |
| deterministic | [可选]当num_parallel_calls被指定时，如果指定了此布尔值（True或False），则它控制转换生成元素的顺序。如果设置为False，则允许转换产生无序的元素，即损失性能的情况下换取确定性。如果未指定，则tf.data.Options.deterministic（默认为True）来控制行为。 |
| name | [可选]tf.data操作的名称 |
返回值：
| 返回值 | 意义 |
|---|---|
| Dataset | 一个tf.data.Dataset的数据集。 |
函数实现：
def batch(self,
batch_size,
drop_remainder=False,
num_parallel_calls=None,
deterministic=None,
name=None):
"""Combines consecutive elements of this dataset into batches.
>>> dataset = tf.data.Dataset.range(8)
>>> dataset = dataset.batch(3)
>>> list(dataset.as_numpy_iterator())
[array([0, 1, 2]), array([3, 4, 5]), array([6, 7])]
>>> dataset = tf.data.Dataset.range(8)
>>> dataset = dataset.batch(3, drop_remainder=True)
>>> list(dataset.as_numpy_iterator())
[array([0, 1, 2]), array([3, 4, 5])]
The components of the resulting element will have an additional outer
dimension, which will be `batch_size` (or `N % batch_size` for the last
element if `batch_size` does not divide the number of input elements `N`
evenly and `drop_remainder` is `False`). If your program depends on the
batches having the same outer dimension, you should set the `drop_remainder`
argument to `True` to prevent the smaller batch from being produced.
Note: If your program requires data to have a statically known shape (e.g.,
when using XLA), you should use `drop_remainder=True`. Without
`drop_remainder=True` the shape of the output dataset will have an unknown
leading dimension due to the possibility of a smaller final batch.
Args:
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
consecutive elements of this dataset to combine in a single batch.
drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
whether the last batch should be dropped in the case it has fewer than
`batch_size` elements; the default behavior is not to drop the smaller
batch.
num_parallel_calls: (Optional.) A `tf.int64` scalar `tf.Tensor`,
representing the number of batches to compute asynchronously in
parallel.
If not specified, batches will be computed sequentially. If the value
`tf.data.AUTOTUNE` is used, then the number of parallel
calls is set dynamically based on available resources.
deterministic: (Optional.) When `num_parallel_calls` is specified, if this
boolean is specified (`True` or `False`), it controls the order in which
the transformation produces elements. If set to `False`, the
transformation is allowed to yield elements out of order to trade
determinism for performance. If not specified, the
`tf.data.Options.deterministic` option (`True` by default) controls the
behavior.
name: (Optional.) A name for the tf.data operation.
Returns:
Dataset: A `Dataset`.
"""
if num_parallel_calls is None or DEBUG_MODE:
if deterministic is not None and not DEBUG_MODE:
warnings.warn("The `deterministic` argument has no effect unless the "
"`num_parallel_calls` argument is specified.")
return BatchDataset(self, batch_size, drop_remainder, name=name)
else:
return ParallelBatchDataset(
self,
batch_size,
drop_remainder,
num_parallel_calls,
deterministic,
name=name)
