Upgrade Your TensorFlow Version
Ensure you upgrade TensorFlow to 2.x in your Python environment. You can achieve this using pip:
pip install --upgrade tensorflow
Use the Compatibility Module
TensorFlow 2.x includes a compatibility module to bridge the gap for 1.x codebases. This allows you to write code in a style compatible with 2.x while maintaining some TensorFlow 1.x constructs.
- Import the compatibility module:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
Convert Code from Sessions to Eager Execution
TensorFlow 2.x uses eager execution by default, eliminating the need for Session.run() to evaluate tensors.
- Review and remove any code that explicitly manages TensorFlow sessions.
- Operations will now execute upon call, so validate that tensors are being evaluated as expected.
Update Model Building to Keras API
Switch to TensorFlow's Keras API for a more streamlined approach to model building and training.
- Instead of using placeholders, directly instantiate layers using the `tf.keras` API.
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(10)
])
Adjust tf.data Pipelines
The tf.data API is leveraged in TensorFlow 2.0 for input pipeline preparation. It's more efficient and straightforward compared to earlier methods.
- Refactor the input data pipeline using `tf.data.Dataset` to streamline the batching and preprocessing steps.
Replace tf.contrib Dependencies
tf.contrib has been deprecated in TensorFlow 2.0. You'll need to identify replacements.
- Search for the functions or classes used from `tf.contrib` and find their new equivalents in TensorFlow 2.x or relevant external libraries.
Handle Removed and Deprecated Symbols
Various functions in TensorFlow 1.x are deprecated in 2.x. Review the TensorFlow migration guide and use compatibility aliases or remove the symbols appropriately.
- Check TensorFlow's documentation for deprecated functions and suggested alternatives.
Modify Estimator Code
For those using TensorFlow Estimator APIs, you may migrate to using tf.keras models or use tf.estimator for compatibility.
- Replace Estimator APIs with `tf.keras` whenever feasible for more succinct model management.
Kernel and V1 Compatibility
TensorFlow 2 still provides the tf.compat.v1 module, which accesses the TensorFlow 1.x APIs. Use this feature only for transitional compatibility.
- Convert any code using the `tf.compat.v1` backend to the native 2.x APIs, ensuring your codebase aligns with the latest TensorFlow standards.
Testing and Validation
Thoroughly test your migrated code. TensorFlow 2.x offers new paradigms that may significantly differ in performance or semantics.
- Execute unit tests for your computational graphs and models to ensure the correctness post-migration.
- Make use of TensorBoard to visualize training and debugging strategies efficiently.
By carefully following these steps and utilizing TensorFlow's extensive documentation, you should be able to transition your projects from TensorFlow 1.x to the more efficient and intuitive 2.x framework smoothly.
