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Load NumPy data in Tensorflow

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  • Last Updated : 18 Mar, 2022
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In this article, we will be looking at the approach to load Numpy data in Tensorflow in the Python programming language.

Using tf.data.Dataset.from_tensor_slices() function

Under this approach, we are loading a Numpy array with the use of tf.data.Dataset.from_tensor_slices() method, we can get the slices of an array in the form of objects by using tf.data.Dataset.from_tensor_slices() method from the TensorFlow module.

Syntax : tf.data.Dataset.from_tensor_slices(list)

Return : Return the objects of sliced elements.

Example 1:

In this example, we are using tf.data.Dataset.from_tensor_slices() method, to get the slices of the 2D-array and then load this to a variable gfg.

Python3




# import modules
import tensorflow as tf
import numpy as np
  
# Creating data
arr = np.array([[1, 2, 3, 4],
                [4, 5, 6, 0],
                [2, 0, 7, 8],
                [3, 7, 4, 2]])
  
# using tf.data.Dataset.from_tensor_slices() 
# method
gfg = tf.data.Dataset.from_tensor_slices(arr)
  
for i in gfg:
    print(i.numpy())

Output:

[1 2 3 4]
[4 5 6 0]
[2 0 7 8]
[3 7 4 2]

Example 2:

In this example, we will load the NumPy list of the variable gfg using the tf.data.Dataset.from_tensor_slices() function from the TensorFlow library in the Python programming language.

Python3




# import modules
import tensorflow as tf
import numpy as np
  
# Creating data
list = [[5, 10], [3, 6], [1, 2], [5, 0]]
  
# using tf.data.Dataset.from_tensor_slices()
# method
gfg = tf.data.Dataset.from_tensor_slices(list)
  
for i in gfg:
    print(i.numpy())

Output:

[ 5 10]
[3 6]
[1 2]
[5 0]

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