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]
