The “NotImplementedError: numpy() is only available when eager execution is enabled.” error occurs when you are trying to use the numpy library in TensorFlow but eager execution is not enabled. Let’s explain in detail with an example:
When using TensorFlow, there are two execution modes: lazy execution (default) and eager execution.
In the lazy execution mode, the operations are not executed immediately, and instead, a computational graph is built. This graph is then executed when a Session is run. On the other hand, eager execution allows immediate execution of operations, making TensorFlow behave like other usual programming environments like Python.
To enable eager execution, you need to import the tensorflow module and enable it by calling the function tf.enable_eager_execution().
Here is an example of how to enable eager execution and fix the “NotImplementedError” error when using numpy in TensorFlow:
“`python
import tensorflow as tf
tf.enable_eager_execution()
import numpy as np
# Your code using numpy and TensorFlow goes here
np_array = np.array([1, 2, 3])
tf_tensor = tf.constant(np_array)
# Perform TensorFlow operations with the numpy array, without encountering the “NotImplementedError” error
“`
In this example, we first import TensorFlow and enable eager execution using tf.enable_eager_execution(). Then, we import numpy and can use it in conjunction with TensorFlow without encountering the “NotImplementedError” error.
By enabling eager execution, you make TensorFlow execute operations immediately, allowing the usage of numpy functions seamlessly. It is important to note that enabling eager execution may have some performance implications, especially for large-scale models or complex computations, so you may need to consider this when using it.