If you are a Python developer working with TensorFlow, you might have across the** ****runtimeerror: Attempting to capture an EagerTensor without building a function**.

In this post, we will explain to you how to solve the** attempting to capture an eagertensor without building a function**.

## Why does this error occur?

**The error typically occurs in TensorFlow when you attempt to use a TensorFlow EagerTensor in a context where it is expected to be used within a TensorFlow function, but you cannot create the function yet.**

## How to Solve the Error?

Here are several solutions to solve this error, depending on the cause.

**Solution 1: Build the Function First**

The most common cause of this error is trying to pass an EagerTensor to a function that expects a Graph Tensor without building the function first.

To solve this error, you need to create the function first before passing any tensors to it.

We can use the tf.function decorator to create a callable function from a TensorFlow graph.

Here is the example program:

**@tf.function
def my_function(x):
# Function body here
pass**

**Solution 2: Convert EagerTensor to Graph Tensor**

If we have already created the function that expects a Graph Tensor, but you are still getting the error, it could be because you are passing an EagerTensor to it.

In this case, you need to convert the EagerTensor to a Graph Tensor using the tf.compat.v1.graph_util.convert_variables_to_constants function.

For example:

**import tensorflow.compat.v1 as tf
from tensorflow.python.framework import graph_util
def my_function(x):
# Function body here
pass
graph_def = tf.compat.v1.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['output_node_name'])**

**Solution 3: Use tf.py_function**

If we are trying to use a TensorFlow operation outside of a function, you can use the tf.py_function wrapper to convert the operation into a callable Python function.

This will allow you to use the operation outside of a function.

**def my_function(x):
# Function body here
pass
def my_wrapper(x):
y = tf.py_function(my_function, [x], tf.float32)
return y
**

**Solution 4: Use tf.compat.v1.disable_eager_execution**

If you encounter an error in a situation where the previous solutions cannot be applied, you can try disabling eager execution by using the tf.compat.v1.disable_eager_execution() function in TensorFlow.

This function can be used to turn off the eager execution mode, which allows for the immediate execution of TensorFlow operations.

For example:

**import tensorflow.compat.v1 as tf
tf.compat.v1.disable_eager_execution()
a = tf.constant(5)
b = tf.constant(10)
c = tf.multiply(a,b)
with tf.compat.v1.Session() as sess:
print(sess.run(c))
**

## Additional Resources

Here are some additional resources for learning more about common Python Runtimeerror:

- Cannot add middleware after an application has started
- Runtimeerror: this event loop is already running
- Runtimeerror: grad can be implicitly created only for scalar outputs
- Runtimeerror tf placeholder is not compatible with eager execution

## Conclusion

In conclusion, the “**RuntimeError: Attempting to Capture an EagerTensor without Building a Function**” error is a common error in TensorFlow, but it can be easily fixed by building the function first, converting the EagerTensor to a Graph Tensor, using tf.py_function, or disabling eager execution.

## Frequently Asked Questions (FAQs)

**What is the difference between an EagerTensor and a Graph Tensor?**

An EagerTensor is a type of tensor in TensorFlow that is evaluated immediately as operations are executed, while a Graph Tensor is lazily evaluated and is used in a TensorFlow graph.

**Can I use EagerTensors and Graph Tensors together in a single function?**

Yes, you can use EagerTensors and Graph Tensors together in a single function as long as you convert the EagerTensors to Graph Tensors before passing them to the function.

**What is the purpose of tf.compat.v1.disable_eager_execution?**

The tf.compat.v1.disable_eager_execution function is used to disable eager execution for the current session and allow the use of Graph Tensors.