深入浅出TensorFlow2函数——tf.data.Dataset.batch

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	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	意义
Dataset	一个 tf.data.Dataset 的数据集。

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)