Notimplementederror: numpy() is only available when eager execution is enabled.

Answer:

The “NotImplementedError: numpy() is only available when eager execution is enabled” occurs when you try to use the numpy library function without having enabled eager execution in your TensorFlow environment.

Eager execution is a mode in TensorFlow that allows operations to be executed immediately as they are called, providing a more intuitive and interactive programming experience. On the other hand, in graph mode (default mode), TensorFlow builds a computational graph first and then executes it.

To resolve this error, you can enable eager execution in TensorFlow using the following code snippet:

import tensorflow as tf
tf.compat.v1.enable_eager_execution()
    

After enabling eager execution, you should be able to use numpy functions without any issues. Here’s an example using numpy and TensorFlow together with eager execution enabled:

import tensorflow as tf
import numpy as np

tf.compat.v1.enable_eager_execution()

# Create a numpy array
arr = np.array([1, 2, 3])

# Convert numpy array to TensorFlow tensor
tensor = tf.constant(arr)

# Perform some operations using TensorFlow functions
result = tf.reduce_sum(tensor)

# Print the result
print(result.numpy())
    

In this example, we enable eager execution, create a numpy array, convert it to a TensorFlow tensor, perform a summation operation using TensorFlow’s reduce_sum function, and finally print the result using numpy’s numpy() function. By enabling eager execution, we can seamlessly use numpy and TensorFlow together.