In this post, we will explain how to solve the attributeerror: module ‘tensorflow’ has no attribute ‘get_default_graph’, what this error means in python, and what causes the error occurs.
Table of contents
Why the error module tensorflow has no attribute get_default_graph occur?
The Attributeerror module tensorflow has no attribute get_default_graph error usually occurs because you are trying to import get_default_graph function in tensorflow, yet it is not found in the package.
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In the next topic, we will discuss the possible causes of the error.
Possible Causes of the Error
There are multiple reasons why you might get the “attributeerror module tensorflow has no attribute get_default_graph” error in TensorFlow. Some of the few causes include:
- You are using an outdated version of TensorFlow that doesn’t support the get_default_graph() function.
- You are using an incompatible version of Python.
- Failing to import the graph before calling get_default_graph().
- You are using the wrong syntax when calling the get_default_graph() function.
For the next topic, we will provide step-by-step instructions on how to solve the error.
How to solve the attributeerror: module ‘tensorflow’ has no attribute ‘get_default_graph’?
Time needed: 3 minutes
Here are the steps to solve the attributeerror: module ‘tensorflow’ has no attribute ‘get_default_graph’.
- Step 1: Check the Version of TensorFlow
The first step in solving the “AttributeError: module ‘tensorflow’ has no attribute ‘get_default_graph’” error is to check the version of TensorFlow that you are using.
This error usually occurs if you are using an outdated version of TensorFlow which is not support the get_default_graph() function.
To check the version of TensorFlow that you are using, open a Python terminal and enter the following command:
import tensorflow as tf
print(tf.version)
This command above will print the version of TensorFlow that you are using. If the version is out-of-date, you can update it using the following command:
!pip install --upgrade tensorflow - Step 2: Using tf.compat.v1.get_default_graph()
If you are installed the version of tensorflow is 2.x version. If the code has get_default_graph() then it will get the same error. To avoid this you can use tf.compat.v1.get_default_graph().
Here is the following code.
import tensorflow as tf
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf) tf.compat.v1.keras.backend.set_session(sess) - Step 3: Incorrect import of tensorflow
As a result of the package migration, the import statement is required to change. You can use the following code.
The incorrect import:
from keras.models import Sequential
The correct import:
from tensorflow.keras.models import Sequential
Conclusion
The “AttributeError: module ‘tensorflow’ has no attribute ‘get_default_graph’” error can be frustrating to debug, yet it can be resolved through following the steps by steps outlined in this tutorial.
By checking the version of TensorFlow and Python, importing the graph, using the with statement, and trying other possible solutions.
You can successfully resolve this error and continue developing your python project models with TensorFlow.
FAQ’s
TensorFlow is an open-source platform for developing and creating machine learning models.
This error usually occurs when you are using an outdated version of TensorFlow, an incompatible version of Python, failing to import the graph before calling get_default_graph().
You can check the version of TensorFlow through opening a Python terminal and enter the following command: “import tensorflow as tf print(tf.version)“
You can import the graph in TensorFlow through the following code: “import tensorflow as tf graph = tf.Graph() with graph.as_default(): # Your code here“
If none of the steps above are working, you should try other possible solutions, like checking for typos in your code, reinstalling TensorFlow or Python.
