Dataset.from_tensors 和 Dataset.from_tensor_slices 有什么区别? [英] What is the difference between Dataset.from_tensors and Dataset.from_tensor_slices?

问题描述

我有一个数据集表示为形状 (num_features, num_examples) 的 NumPy 矩阵，我希望将其转换为 TensorFlow 类型 tf.Dataset.

我正在努力理解这两种方法之间的区别:Dataset.from_tensors 和 Dataset.from_tensor_slices.什么是正确的，为什么?

TensorFlow 文档(link)说这两种方法都接受嵌套张量的结构虽然当使用 from_tensor_slices 时，张量在第 0 维中应该具有相同的大小.

解决方案

from_tensors 组合输入并返回包含单个元素的数据集:

<预><代码>>>>t = tf.constant([[1, 2], [3, 4]])>>>ds = tf.data.Dataset.from_tensors(t)>>>[x 代表 ds 中的 x][<tf.Tensor: shape=(2, 2), dtype=int32, numpy=数组([[1, 2],[3, 4]], dtype=int32)>]

from_tensor_slices 为输入张量的每一行创建一个具有单独元素的数据集:

<预><代码>>>>t = tf.constant([[1, 2], [3, 4]])>>>ds = tf.data.Dataset.from_tensor_slices(t)>>>[x 代表 ds 中的 x][<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>,<tf.Tensor: shape=(2,), dtype=int32, numpy=array([3, 4], dtype=int32)>]

I have a dataset represented as a NumPy matrix of shape (num_features, num_examples) and I wish to convert it to TensorFlow type tf.Dataset.

I am struggling trying to understand the difference between these two methods: Dataset.from_tensors and Dataset.from_tensor_slices. What is the right one and why?

TensorFlow documentation (link) says that both method accept a nested structure of tensor although when using from_tensor_slices the tensor should have same size in the 0-th dimension.

解决方案

from_tensors combines the input and returns a dataset with a single element:

>>> t = tf.constant([[1, 2], [3, 4]])
>>> ds = tf.data.Dataset.from_tensors(t)
>>> [x for x in ds]
[<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
 array([[1, 2],
        [3, 4]], dtype=int32)>]

from_tensor_slices creates a dataset with a separate element for each row of the input tensor:

>>> t = tf.constant([[1, 2], [3, 4]])
>>> ds = tf.data.Dataset.from_tensor_slices(t)
>>> [x for x in ds]
[<tf.Tensor: shape=(2,), dtype=int32, numpy=array([1, 2], dtype=int32)>,
 <tf.Tensor: shape=(2,), dtype=int32, numpy=array([3, 4], dtype=int32)>]

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