TensorFlow has become a cornerstone library for deep learning practitioners, but as with any evolving software, it undergoes API changes that might deprecate some methods or classes you are accustomed to using. It's crucial for developers to keep their code up-to-date with the current TensorFlow APIs to leverage new features, performance improvements, and critical bug fixes. In this article, we will explore how to update deprecated TensorFlow APIs using the tensorflow.compat module.
Understanding TensorFlow Compat
The tensorflow.compat module allows you to write your code in a way that is compatible across TensorFlow versions, or help transition code from an older version to a newer one. Essentially, it provides a bridge for managing API changes without sacrificing your project's functionality.
Identifying Deprecated API Warnings
When running your TensorFlow application, you might encounter warnings that notify you about deprecated functions or classes. These warnings usually suggest a newer API that should be used instead. For example:
import tensorflow as tf
# Using a deprecated API
x = tf.placeholder(tf.float32, shape=(None, 2), name='input')
# This will raise a deprecation warning to change to:
# x = tf.compat.v1.placeholder(tf.float32, shape=(None, 2), name='input')
Using the Compat Module
The tensorflow.compat module helps smoothly transition your TensorFlow code. Consider the case where you need to switch from using tf.Session in TensorFlow 1.x to the eager execution model in TensorFlow 2.x:
# Original TensorFlow 1.x code
import tensorflow as tf
def run_session():
a = tf.constant(5)
b = tf.constant(6)
c = a * b
with tf.Session() as sess:
print(sess.run(c))
# Updating using compat module
import tensorflow.compat.v1 as tf
# Disable eager execution to use placeholder and sessions
tf.disable_v2_behavior()
def run_session_compatible():
a = tf.constant(5)
b = tf.constant(6)
c = a * b
with tf.Session() as sess:
print(sess.run(c))
run_session_compatible() # Output will be 30
Migrating to TensorFlow 2.x Directly
If you aim to fully embrace TensorFlow 2.x, refactoring to utilize its new features is possible. Consider converting eager execution code that doesn’t rely on sessions:
import tensorflow as tf
def execute_eagerly():
a = tf.constant(5)
b = tf.constant(6)
c = a * b
print(c.numpy())
execute_eagerly() # Output will be 30
This approach enhances code simplicity and performance by removing overhead linked with graph-based execution.
Upgrading SavedModels and Checkpoints
One often-overlooked area during upgrades is how to transition saved models and checkpoints from TensorFlow 1.x to 2.x:
# Load a SavedModel using TensorFlow 2.x
model = tf.keras.models.load_model('path_to_saved_model')
model.summary()
# Convert a model from TensorFlow 1.x compatible format
import tensorflow.compat.v1 as tf
def save_model_v1_to_v2(sess, input_tensor, output_tensor):
tf.saved_model.simple_save(
sess,
'path_to_converted_model',
inputs={'input': input_tensor},
outputs={'output': output_tensor})
# Assuming `sess` has your TensorFlow 1.x graph loaded
Updating models ensures they retain functionality and performance optimizations provided by TensorFlow 2.x's runtime.
Conclusion
Refactoring your TensorFlow code with the help of the tensorflow.compat module simplifies the transition between versions, significantly reducing maintenance difficulties. Staying current with TensorFlow's evolving APIs not only aids in achieving better computational efficiencies but also aligns well with new developments and contributions to the library by the open-source community. Begin taking steps to refactor and enhance your code today, ensuring it adheres voluntarily to the latest TensorFlow release features.
