AttributeError: ‘Tensor’ object has no attribute ‘numpy’ errors because a parameter run_eagerly value is set as False. There are two versions of the TensorFlow module, Tensorflow 1. x and Tensorflow 2. x. In Tensorflow 1. x, we have to manually set this parameter, but in Tensorflow 2.0, it is, by default, True.
Solution 1 : Enable eager_execution (Only for Tenorflow 1.x)
If you are using TensorFlow 1. x, we need to explicitly invoke eager_execution.
tf.enable_eager_execution(config=None, device_policy=None,
execution_mode=None)Solution 2 : invoke run_functions_eagerly (For Tensorflow 2.x)
Set the tf.enable_eager_execution() function to True.
tf.config.run_functions_eagerly(run_eagerly)Solution 3
Another solution to fix the AttributeError: ‘Tensor’ object has no attribute ‘numpy’ error, convert a Tensor to a NumPy array using the .numpy() method of the Tensor object.
import tensorflow as tf
tensor = tf.constant([[11, 21], [19, 46]])
array = tensor.numpy()
print(array)Output
[[11, 21], [19, 46]]In this code, first, we imported the tensorflow library and defined a tensor using the tf.constant() method.
To convert a tensor to a numpy array, we used the tensor.numpy() function.
If you run the above code, we won’t get any error, instead, it will return this output: '[[11, 21], [19, 46]]'.
That’s pretty much it.
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.
