How to convert tf.contrib to Tensorflow 2.0
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In the transition from TensorFlow 1.x to TensorFlow 2.0, one of the significant changes involved the removal of the tf.contrib
module. TensorFlow 2.0 embraces simplicity and clarity, scaling down various pieces of functionality that were previously experimental and not necessarily stable or widely adopted. Consequently, developers seeking to upgrade legacy TensorFlow models or codebases must understand how to migrate from tf.contrib
to TensorFlow 2.0. This article explores conversion strategies, with examples and explanations to ease this migration.
Understanding tf.contrib
tf.contrib
was a vast, catch-all namespace that held experimental and unsupported features in TensorFlow 1.x. It accommodated a wide variety of functionalities, from specific layers and operations to specialized optimizers and learning rate schedules. As these features were experimental, TensorFlow did not guarantee backward compatibility or long-term support in future releases.
Key Changes in TensorFlow 2.0
TensorFlow 2.0 introduces several major enhancements, including:
- Eager Execution by Default: Simplifies coding and debugging by evaluating operations immediately.
- Comprehensive Keras Integration: Offers a higher-level API for managing models and layers.
- Unified
RNNAPI: Streamlines recurrent neural network layers. - AutoGraph and
tf.function: Convert Python code into efficient, graph-based computations.
Strategies for Migrating from tf.contrib
to TensorFlow 2.0
The conversion process from tf.contrib
involves identifying and replacing deprecated functionalities with supported APIs or other open-source alternatives.
Step 1: Identify tf.contrib
Usage
Begin by scanning your codebase to identify where tf.contrib
is used. This can often be done through simple search queries or by running your code and noting deprecation warnings.
Step 2: Check for Built-in Alternatives
TensorFlow 2.0 or other established libraries might now include certain functionalities previously found in tf.contrib
. Below is a table summarizing replacements for common tf.contrib
components:
tf.contrib | Component | Replacement in TensorFlow 2.0 |
tf.contrib.layers | ||
tf.keras.layers | ||
or tf.keras | ||
| equivalent | ||
tf.contrib.learn | ||
| (Estimators) | tf.estimator | |
| (may require some adjustments) | ||
tf.contrib.rnn | ||
tf.keras.layers | ||
| (RNN, LSTM, GRU) | ||
tf.contrib.data.Dataset | ||
tf.data.Dataset | ||
tf.contrib.saved_model | ||
tf.saved_model | ||
BayesFlow (e.g., tf.contrib.bayesflow | ||
| ) | External libraries like tensorflow-probability |
Step 3: Use Community Resources
If no direct replacement exists within TensorFlow 2.0, consider the following options:
- TensorFlow Addons: An official repository for contributions above the core functionalities of TensorFlow, which includes experimental or widely used utilities not available in TF 2.0 directly.
- Keras or other External Libraries: Libraries such as Keras offer advanced model-building capabilities beyond what TensorFlow provides.
- Open-source Implementations: Search for equivalent implementations in the TensorFlow GitHub community or repositories specializing in machine learning operations.
Step 4: Refactor Code
After identifying replacements, update your code. Here's an example:
Example
Suppose the original TensorFlow 1.x code uses tf.contrib
for regularization:
