Resolving ‘module tensorflow has no attribute contrib’ Error

Resolving 'module tensorflow has no attribute contrib' Error

Encountering the error message ‘AttributeError: module ‘tensorflow’ has no attribute ‘contrib’ can be quite frustrating for developers working with TensorFlow. Understanding the implications of this error and the changes in TensorFlow versions is crucial to resolving the issue effectively. In this article, we will delve into the details of why this error occurs and provide actionable steps to address it, ensuring a seamless continuation of your TensorFlow development journey.

Resolving TensorFlow AttributeError ‘contrib’ Module Issue

The error message you’re encountering, specifically the AttributeError stating that the module 'tensorflow' has no attribute 'contrib', is likely due to changes in TensorFlow versions. The 'contrib' module was present in earlier versions of TensorFlow but has been deprecated in recent releases.

To resolve this issue, consider the following steps:

  1. Upgrade to TensorFlow 2.x:

    • Starting from TensorFlow 2.0 alpha, the 'contrib' module has been removed. You can upgrade your existing TensorFlow 1.x code to TensorFlow 2.x using the tf_upgrade_v2 script. This will help you adapt your code to the latest APIs and avoid the 'contrib' references.
  2. Use

    • If you’re working with object detection, consider using instead of is updated and doesn’t rely on the deprecated 'contrib' module.

The Evolution of TensorFlow contrib Module

The TensorFlow contrib module has undergone significant changes in recent versions. Let’s delve into the details:

  1. Background:

    • The tf.contrib module in TensorFlow was initially designed to house additional functionalities and extensions beyond the core TensorFlow library.
    • Over time, tf.contrib grew substantially, making it challenging to maintain and support within a single repository.
  2. Deprecation and Migration:

    • With the release of TensorFlow 2.0, the decision was made to deprecate tf.contrib.
    • Here’s what happened:
      • TensorFlow’s team decided to handle larger projects separately while incubating smaller extensions alongside the main TensorFlow code.
      • As part of this transition, they stopped distributing tf.contrib.
      • For each contrib module, they considered three options:
        • Integrate the project into TensorFlow.
        • Move it to a separate repository.
        • Remove it entirely.
      • Consequently, all of tf.contrib was deprecated, and no new projects were added to it.
  3. Migration Plans:

    • The TensorFlow team worked on detailed migration plans for each contrib module.
    • Some projects would be integrated into TensorFlow, while others would find new homes outside the core repository.
    • The tf.contrib.lookup functionality, for example, will move to the core after TensorFlow 2.0.
    • If you’re using TensorFlow 2.0 or later, you won’t have access to tensorflow.contrib directly.
  4. What to Do:

    • If you were relying on specific tf.contrib features, consider the following alternatives:
      • Explore similar functionality in non-contrib TensorFlow.
      • Check out community-maintained repositories like tensorflow/addons.
      • If necessary, consider forking the required code.

In summary, while tf.contrib

: Source

Resolving ‘AttributeError: module ‘tensorflow’ has no attribute ‘contrib’’ Issue in TensorFlow 2.0

The error message you’re encountering, “AttributeError: module ‘tensorflow’ has no attribute ‘contrib'”, occurs because the tf.contrib module has been deprecated in TensorFlow 2.0. Fear not! I’ll guide you through the steps to resolve this issue.

Here are two solutions:

  1. Downgrade TensorFlow:

    • If your code relies on the tf.contrib module, consider creating a separate environment where you can install TensorFlow 1.14. This way, you can run your existing code without affecting your TensorFlow 2.0 environment.
    • To do this, set up a new environment (e.g., using conda or virtualenv), and then install TensorFlow 1.14 within that environment. You can use the following commands:
      pip uninstall tensorflow
      pip install tensorflow==1.14.0
  2. Update Your Code:

    • Instead of using tf.contrib.rnn.LSTMCell, switch to tf.compat.v1.nn.rnn_cell.LSTMCell or tf.keras.layers.LSTMCell.
    • Similarly, replace tf.contrib.rnn.DropoutWrapper with tf.compat.v1.nn.rnn_cell.DropoutWrapper or tf.keras.layers.DropOut.
    • For tf.contrib.rnn.MultiRNNCell, use tf.compat.v1.nn.rnn_cell.MultiRNNCell or tf.keras.layers.RNN.

Remember that TensorFlow 2.0 introduces several changes, and the removal of the tf.contrib

Alternatives to TensorFlow

If you’re exploring alternatives to TensorFlow, there are several other deep learning frameworks and libraries that you might find useful. Here are some noteworthy options:

  1. DataRobot:

    • Description: DataRobot is an enterprise-level machine learning platform that offers a comprehensive library of open-source ML techniques and algorithms.
    • Unique Features:
      • Utilizes automated time series models for accurate predictions based on historical data.
      • Supports informed decision-making by analyzing and understanding various machine learning models.
      • Incorporates Paxata for shaping, exploring, and combining different data sets.
      • Includes Zepl for self-service, cloud-native notebooks and collaborative work.
    • Use Cases:
      • Marketing forecasts
      • Lead scoring
      • Product demand prediction
      • Revenue analytics
      • Financial insights
    • Deployment Options: Fully managed service, cloud platform, or on-premise.
  2. Jupyter Notebook:

    • Description: Jupyter Notebook is an open-source web application that facilitates collaboration among researchers, data scientists, mathematicians, and other users.
    • Features:
      • Enables interactive data exploration, visualization, and code execution.
      • Supports various programming languages (Python, R, Julia, etc.).
      • Ideal for documenting research, sharing insights, and creating reproducible workflows.
    • Use Cases:
      • Data analysis
      • Machine learning experimentation
      • Research documentation
    • Collaboration: Multiple users can work on the same notebook simultaneously.
  3. scikit-learn:

    • Description: A Python module for machine learning built on top of SciPy.
    • Features:
      • Provides a wide range of machine learning algorithms.
      • Easy integration with other Python libraries.
      • Suitable for both beginners and experienced practitioners.
    • Use Cases:
      • Classification
      • Regression
      • Clustering
      • Dimensionality reduction
    • Popular Alternatives: Theano, PyTorch, OpenCV, Keras, and Apache Spark.
  4. mlpack:

    • Description: A machine-learning software library focused on easy usage and scalability.
    • Features:
      • Provides suggestions for machine learning tasks.
      • Offers a variety of algorithms.
      • Aims to simplify access for users.
    • Use Cases:
      • General machine learning tasks
      • Scalable solutions
    • Mandatory to Learn: Recommended for 2022.

Managing TensorFlow Warnings

When working with TensorFlow, it’s essential to handle warnings and errors effectively to ensure smooth development. Here are some strategies to prevent and manage TensorFlow-related issues:

  1. Suppressing TensorFlow Warnings:

    • To disable or suppress TensorFlow warnings in Python, you can use the os.environ module to set the TF_CPP_MIN_LOG_LEVEL environment variable.
    • Set the value of TF_CPP_MIN_LOG_LEVEL to '3' to suppress all TensorFlow warnings. This means that info, warning, and error messages won’t be logged.
    • Here’s an example of how to do it in your Python code:
      # Disable TensorFlow warnings
      import os
      os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
      import tensorflow as tf
      print("TensorFlow version:", tf.__version__)
    • Make sure to add the import statement for the os module and set the environment variable above your TensorFlow import in your code.
  2. Setting the Environment Variable via Command Line:

    • Alternatively, you can set the TF_CPP_MIN_LOG_LEVEL environment variable directly on your machine.
    • On macOS and Linux, use the following command in your terminal:
      export TF_CPP_MIN_LOG_LEVEL="3"
    • On Windows (CMD), use:
      set TF_CPP_MIN_LOG_LEVEL="3"
    • If you’re using PowerShell on Windows, run:
    • Keep in mind that this approach sets the environment variable only for the current shell session.
  3. Permanent Configuration (macOS and Linux):

    • To set the TF_CPP_MIN_LOG_LEVEL environment variable permanently, modify your profile file (e.g., ~/.bashrc or ~/.zshrc).
    • Add the following line at the bottom of your profile file:
      export TF_CPP_MIN_LOG_LEVEL="3"
    • Source the updated profile file or restart your server.


In conclusion, the deprecation of the ‘tf.contrib’ module in TensorFlow 2.0 has brought about significant changes in how developers interact with the TensorFlow ecosystem. As the error ‘module tensorflow has no attribute contrib’ continues to pose challenges for many, it’s essential to adapt to the evolving TensorFlow landscape. By following the migration strategies outlined earlier and keeping abreast of TensorFlow updates and best practices, you can navigate through such errors with confidence and elevate your TensorFlow development skills.

Remember, embracing change and staying informed are key to overcoming obstacles in your TensorFlow projects and ensuring smooth progress in your deep learning endeavors.


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