Modulenotfounderror: no module named 'tensorflow.contrib'

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Resolving the "ModuleNotFoundError: No Module Named 'tensorflow.contrib'" Error

Somtimes, while working with python modules, user face an error "ModuleNotFoundError: No Module Named 'tensorflow.contrib'". The "ModuleNotFoundError" is a common error in Python, and it occurs when the interpreter cannot find the specified module or package. In this case, the error message indicates that Python is unable to locate the "tensorflow.contrib" module, which was used in older versions of TensorFlow but has been deprecated and removed in more recent versions. So, In this article, we will discuss the most common reasons of this error and what are the possible solutions to get rid from it. Let's look at code examples that demonstrate the error and its resolution:

Code with "tensorflow.contrib" (Deprecated)

import tensorflow as tf
# Using deprecated 'tensorflow.contrib'
my_variable = tf.contrib.layers.variable()

The above code will display following output as shown in the image as well.

AttributeError: module 'tensorflow' has no attribute 'contrib'

Common Causes of the Error

Reason 1: Outdated TensorFlow Version

The "tensorflow.contrib" module was part of earlier versions of TensorFlow but has been removed in TensorFlow 2.x. If you are using a newer TensorFlow version, code that relies on "tensorflow.contrib" will result in this error.

Reason 2: Deprecated Functionality

Functions and features that were once available in "tensorflow.contrib" have been moved to other parts of TensorFlow or external libraries. Using the deprecated "tensorflow.contrib" module will not work in newer versions.

Can I still use older TensorFlow versions with 'tensorflow.contrib'?
Yes, if you have code that relies on "tensorflow.contrib" and you prefer to use an older TensorFlow version, you can create a separate environment with the desired TensorFlow version using tools like virtual environments or Conda.

Reason 3: Outdated Code

If you are using code or tutorials that were written for older TensorFlow versions, they may still reference "tensorflow.contrib." Updating such code to be compatible with the latest TensorFlow is necessary to avoid this error.


To resolve the "ModuleNotFoundError: No module named 'tensorflow.contrib'" error, follow these steps:

Solution 1: Update TensorFlow

Ensure you are using a recent version of TensorFlow (2.x or later). You can upgrade TensorFlow using pip:

pip install --upgrade tensorflow

Solution 2: Review and Update Code

Examine your code for any references to "tensorflow.contrib" and replace them with the appropriate TensorFlow alternatives or external libraries that offer similar functionality.

Solution 3: Consult TensorFlow Documentation

Refer to the official TensorFlow documentation to find the equivalent functions or modules for the features you need. TensorFlow's documentation is well-maintained and provides guidance for using the latest APIs.

Solution4: Community Forums and GitHub

If you encounter difficulties during the transition from "tensorflow.contrib" to newer TensorFlow versions, consider seeking help on community forums like Stack Overflow or checking TensorFlow's GitHub repository for discussions and issues related to deprecated functionality.

Are there any migration guides for transitioning from ?'tensorflow.contrib'
Yes, TensorFlow provides migration guides and release notes that detail changes and alternatives for code that previously used "tensorflow.contrib." These resources can be helpful during the transition.


The "ModuleNotFoundError: No module named 'tensorflow.contrib'" error occurs when attempting to use the deprecated "tensorflow.contrib" module in newer versions of TensorFlow. To resolve this error, update your TensorFlow library to the latest version, review your code for references to "tensorflow.contrib," and replace them with the appropriate alternatives provided by TensorFlow 2.x or consult the official documentation for guidance. Keeping your code up-to-date ensures compatibility with the latest TensorFlow features and best practices in machine learning and deep learning projects.

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