from python\data\ops\dataset_ops.py

def batch(self, batch_size, drop_remainder=False):"""Combines consecutive elements of this dataset into batches.將此數據集的連續元素合并為批。The tensors in the resulting element will have an additional outerdimension, which will be `batch_size` (or `N % batch_size` for the lastelement if `batch_size` does not divide the number of input elements `N`evenly and `drop_remainder` is `False`). If your program depends on thebatches having the same outer dimension, you should set the `drop_remainder`argument to `True` to prevent the smaller batch from being produced.結果元素中的張量將具有一個附加的外部尺寸，該尺寸將為`batch_size`（或最后一個元素的`N％batch_size`，如果`batch_size`不能將輸入元素`N`的數量均勻劃分和`drop_remainder` 是“假”）。 如果您的程序依賴于具有相同外部尺寸的批次，則應將drop_remainder參數設置為True，以防止產生較小的批次。Args:batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number ofconsecutive elements of this dataset to combine in a single batch.tf.int64標量tf.Tensor，表示要在單個批次中合并的此數據集的連續元素數。drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representingwhether the last batch should be dropped in the case its has fewer than`batch_size` elements; the default behavior is not to drop the smallerbatch.tf.bool標量tf.Tensor，表示如果最后一個批次的數量少于`batch_size`個元素，是否應刪除最后一個批次； 默認行為是不刪除較小的批次。Returns:Dataset: A `Dataset`. 數據集：一個“數據集”。"""return BatchDataset(self, batch_size, drop_remainder)

總結

以上是生活随笔為你收集整理的tensorflow dataset_ops batch()方法 （将此数据集的连续元素合并为批）的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。