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

Question

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 1

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)>] 

Answer 2

1) Main difference between the two is that nested elements in from_tensor_slices must have the same dimension in 0th rank:

# exception: ValueError: Dimensions 10 and 9 are not compatible dataset1 = tf.data.Dataset.from_tensor_slices(     (tf.random_uniform([10, 4]), tf.random_uniform([9]))) # OK, first dimension is same dataset2 = tf.data.Dataset.from_tensors(     (tf.random_uniform([10, 4]), tf.random_uniform([10]))) 

2) The second difference, explained here, is when the input to a tf.Dataset is a list. For example:

dataset1 = tf.data.Dataset.from_tensor_slices(     [tf.random_uniform([2, 3]), tf.random_uniform([2, 3])])  dataset2 = tf.data.Dataset.from_tensors(     [tf.random_uniform([2, 3]), tf.random_uniform([2, 3])])  print(dataset1) # shapes: (2, 3) print(dataset2) # shapes: (2, 2, 3) 

In the above, from_tensors creates a 3D tensor while from_tensor_slices merge the input tensor. This can be handy if you have different sources of different image channels and want to concatenate them into a one RGB image tensor.

3) A mentioned in the previous answer, from_tensors convert the input tensor into one big tensor:

import tensorflow as tf  tf.enable_eager_execution()  dataset1 = tf.data.Dataset.from_tensor_slices(     (tf.random_uniform([4, 2]), tf.random_uniform([4])))  dataset2 = tf.data.Dataset.from_tensors(     (tf.random_uniform([4, 2]), tf.random_uniform([4])))  for i, item in enumerate(dataset1):     print('element: ' + str(i + 1), item[0], item[1])  print(30*'-')  for i, item in enumerate(dataset2):     print('element: ' + str(i + 1), item[0], item[1]) 

output:

element: 1 tf.Tensor(... shapes: ((2,), ())) element: 2 tf.Tensor(... shapes: ((2,), ())) element: 3 tf.Tensor(... shapes: ((2,), ())) element: 4 tf.Tensor(... shapes: ((2,), ())) ------------------------- element: 1 tf.Tensor(... shapes: ((4, 2), (4,)))