Convert Numpy Array into Keras Tensor

In this blog, we will explore the process of converting a Numpy array to a Keras tensor. Converting Numpy arrays to TensorFlow tensors is essential for seamlessly integrating Numpy data with TensorFlow models. We will dive into the different methods and techniques to perform this conversion.

What is Numpy

Numpy is a popular Python library for scientific computing that provides efficient functions to perform various operations on multidimensional arrays and metrics. It is widely used in data analysis, machine learning, and deep learning field.

What is Keras

Keras is a high-level deep-learning framework developed by Google for building, training, and deploying deep neural networks. It allows users easily define and customize various neural networks such as CNN, and RNN. It supports various applications like image processing, image classification, natural language processing, and generative modeling.

What is Tensor

Tensor is a primary data structure used in popular deep-learning libraries such as TensorFlow and PyTorch.It allows us to efficient computing and transformation of data in high dimensions.

Import the Libraries

import tensorflow as tf
import numpy as np

Create a Numpy Array

numpy_array=np.array([[1,2,3,4],[5,6,7,8]])
print(f'The Numpy array:\n{numpy_array}')

• np.array() –  This function takes an input sequence such as a list or tuple and converts it into a Numpy array.

Output

The NumPy array:

[[1 2 3 4]

[5 6 7 8]]

Numpy Array to Keras Tensor

keras_tensor=tf.convert_to_tensor(numpy_array)
print(keras_tensor)

• tf.convert_to_tensor(numpy_array) – This Function converts Python objects to various types of tensors.

Output

tf.Tensor(

[[1 2 3 4]

[5 6 7 8]], shape=(2, 4), dtype=int64)

type(keras_tensor)