Converting deprecated dynamic_rnn to TensorFlow 2.0

What will you learn?

This tutorial will guide you through updating code that utilizes the deprecated dynamic_rnn function in TensorFlow to the latest version for TensorFlow 2.0. By the end of this tutorial, you will be able to seamlessly transition your codebase to leverage the enhanced features and best practices of TensorFlow 2.0.

Introduction to the Problem and Solution

As developers upgrade their TensorFlow projects from version 1.x to 2.x, they often encounter challenges related to deprecated functions like dynamic_rnn. The evolution of TensorFlow introduces more efficient and streamlined alternatives that align with current standards. To address this issue, it is essential to adapt our codebase by embracing the advancements provided by TensorFlow 2.0.

To facilitate this transition, we will utilize tf.keras.layers.RNN as a replacement for dynamic_rnn. This updated layer not only simplifies the implementation but also ensures compliance with the latest practices endorsed by TensorFlow.


import tensorflow as tf

# Define your RNN model using tf.keras.layers.RNN
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim),

# Compile and train your model as usual
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']), epochs=num_epochs)

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# Copyright PHD


The provided code snippet showcases the refactoring process of an RNN model by replacing dynamic_rnn with tf.keras.layers.RNN. Here’s a breakdown of key points: – Importing TensorFlow: Essential modules are imported from TensorFlow. – Defining Model: The RNN model is structured using a sequential API for stacking layers. – RNN Layer: Instead of dynamic_rnn, tf.keras.layers.RNN with an LSTM cell is employed. – Compilation and Training: Standard procedures are followed for compiling and training the model in Tensorflow 2.0.

By adopting this approach, compatibility with newer versions is ensured while potentially enhancing performance compared to outdated implementations.

  1. How do I identify if my code still employs deprecated functions?

  2. Warnings or errors during execution may indicate usage of deprecated functions. Referencing official documentation for updated APIs is advisable.

  3. Can I continue using old functions without updating them?

  4. While some older functionalities might remain functional due to backward compatibility, updating your codebase promotes long-term maintainability and efficiency gains.

  5. Are there tools available for automating updates related to deprecated functions?

  6. Yes, tools like ‘tf_upgrade_v2’ provided by TensorFlow can automate certain aspects of this process; however, manual verification is recommended.

  7. Will my existing models break after transitioning from dynamic_rnn?

  8. Minor adjustments might be necessary due to parameter or behavioral changes between versions; nevertheless, most models can be smoothly migrated without significant disruptions.

  9. Is it mandatory to update all instances of dynamic_rnn simultaneously within my project?

  10. Although advisable, gradual updates based on priority or criticality within your project scope are feasible as well.

  11. What advantages come with switching from dynamic_rnn to tf.keras.layers.RNN?

  12. Transitioning to updated APIs enhances readability, maintainability, performance optimization, access to new features & enhancements released by Tensorflow over time.


In summary, transitioning from deprecated functions such as dynamic_rnn in earlier versions of Tensorflow towards modern equivalents like RNN layers under Keras API not only future-proofs your projects but also leverages improved functionality & efficiency offered by recent updates within Tensorflow ecosystem.

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