What is the difference between Dataset.from_tensors and Dataset.from_tensor_slices?

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.


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)>]