Both numpy.split() and tf.split() can split an array or a tensor into some sub arrays and tensors. However, there exsits some differences between them. In this tutorial, we will compare with them to help you understand.

## Syntax of numpy.split() and tf.split

numpy.split(ary, indices_or_sections, axis=0) | tf.split(value, num_or_size_splits, axis=0, num=None, ame=’split’ ) |

From syntax of numpy.split() and tf.split(), we will find the main difference between them is on the second parameter.

## As to numpy.split()

indices_or_sections: The value of it determines the way to get sub arrays from ary is to use indicues.

For example, as to code below

x = np.array([1, 2, 3, 4, 2, 3, 4, 5], dtype = np.float32) xs = np.split(x,indices_or_sections = [1, 2, 3], axis = 0)

indices_or_sections = [1, 2, 3], which means we will get sub arrays from x by indices, these sub arrays are: x[0: 1], x[1: 2], x[2: 3] and x[3:]

You can get more from tutorial below.

Understand numpy.split(): Split an Array into Sub-Arrays – NumPy Tutorial

## As to tf.split()

num_or_size_splits: The value of it determines the element counts of sub tensors.

As to code below

x = tf.Variable(np.array([1, 2, 3, 4, 2, 3, 4, 5], dtype = np.float32)) xs = tf.split(x,num_or_size_splits = [1, 2, 5], axis = 0)

Which means we will split tensor x into 3 tensors, the first tensor contains 1 elements, the second tensor contains 2 elements and the last tensor contains 5 elements.

You can find more details in this tutorial:

TensorFlow tf.split(): Splits a Tensor into Sub Tensors – TensorFlow Tutorial

Understand the difference between numpy.split() and tf.split(), we can use them correctly.