What is the difference between Dataset.from_tensors and Dataset.from_tensor_slices?
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) # [[1, 2], [3, 4]]
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) # [1, 2], [3, 4]
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,)))
I think @MatthewScarpino clearly explained the differences between these two methods.
Here I try to describe the typical usage of these two methods:
from_tensors
can be used to construct a larger dataset from several small datasets, i.e., the size (length) of the dataset becomes larger;while
from_tensor_slices
can be used to combine different elements into one dataset, e.g., combine features and labels into one dataset (that's also why the 1st dimension of the tensors should be the same). That is, the dataset becomes "wider".