Load NumPy data in Tensorflow
Last Updated :
18 Mar, 2022
In this article, we will be looking at the approach to load Numpy data in Tensorflow in the Python programming language.
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 tensorflow as tf
import numpy as np
arr = np.array([[ 1 , 2 , 3 , 4 ],
[ 4 , 5 , 6 , 0 ],
[ 2 , 0 , 7 , 8 ],
[ 3 , 7 , 4 , 2 ]])
gfg = tf.data.Dataset.from_tensor_slices(arr)
for i in gfg:
print (i.numpy())
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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 tensorflow as tf
import numpy as np
list = [[ 5 , 10 ], [ 3 , 6 ], [ 1 , 2 ], [ 5 , 0 ]]
gfg = tf.data.Dataset.from_tensor_slices( list )
for i in gfg:
print (i.numpy())
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Output:
[ 5 10]
[3 6]
[1 2]
[5 0]
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