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.

## Answer

`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)>]