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.

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Answer 1

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,)))