Migrating TensorFlow 1.x Models to 2.x Using Compat

The release of TensorFlow 2.x introduced a simpler, more intuitive and performance-oriented approach compared to its predecessor, TensorFlow 1.x. While these improvements are significant, the transition from 1.x to 2.x requires developers to adapt their old models to fit the new structural paradigm. Fortunately, TensorFlow provides a module called compat to ease the migration process.

In this article, we will walk through the essential steps required to migrate a TensorFlow 1.x model to TensorFlow 2.x using the tf.compat module. Along the way, we’ll explore code snippets that illustrate these processes for better understanding.

Understanding Key Changes in TensorFlow 2.x

Before diving into code, let’s review some key changes introduced in TensorFlow 2.x:

• Eager Execution: Enabled by default, making TensorFlow more Pythonic and user-friendly.
• Enhanced APIs: Simplified APIs and better integrations with Keras.
• No More Session: Graph and session management is handled automatically.
• Robust tf.function: Converts Python code into TensorFlow graph for performance optimization.

Using tf.compat for Migration

The tf.compat module supports all TensorFlow 1.x functions while facilitating their compatibility with TensorFlow 2.x. Below are some general steps to migrate a 1.x model:

1. Enable Compatibility Mode

Begin by importing the necessary TensorFlow libraries and enabling compatibility mode as shown below:

import tensorflow as tf

tf.compat.v1.disable_eager_execution()

Disabling eager execution temporarily allows you to migrate components incrementally.

2. Adjust Overview Callbacks

Update deprecated functions using their alternatives in tf.compat.v1. For instance:

graph = tf.compat.v1.Graph()
sess = tf.compat.v1.Session(graph=graph)

This step maintains existing models until manually upgraded to 2.x Graph APIs.

3. Update Model Constructs

Align model layers and functions with 2.x counterparts. Migrate each layer using tf.keras where applicable:

with graph.as_default():
    # Construct design##based on existing model
    model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(64, activation='relu', input_shape=(input_tree,)),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(10)
    ])

This update guides the conversion to TensorFlow’s Keras-centric interface.

4. Leverage tf.function for Decoration

Finally, wrap targeting functions with tf.function to improve function execution speed:

@tf.function
def train_step(inputs):
    # codes for each train step execution
    pass

Using tf.function allows you to benefit from graph execution and optimization resources within TensorFlow 2.x.

A Complete Example of Migration

Consider a TensorFlow 1.x code snippet:

import tensorflow as tf

x = tf.placeholder(tf.float32, shape=[None, 784])
W = tf.Variable(tf.zeros([784, 10]))
y = tf.matmul(x, W)

Now, the equivalent TensorFlow 2.x equivalent after using tf.compat might look like:

import tensorflow as tf

x = tf.keras.Input(shape=(784,))
W = tf.Variable(tf.zeros([784, 10]))
y = tf.linalg.matmul(x, W)

We’ve changed the tf.placeholder to tf.keras.Input and also promoted compatibility without a Session using tf.linalg.matmul.

Conclusion

Migrating from TensorFlow 1.x to 2.x requires attention to both minor and major API changes. However, using the tf.compat module permits a smooth transition, allowing legacy code to gradually adapt to new practices and standards imposed by TensorFlow 2.x. We hope these guidelines enable developers to efficiently transition their projects, leveraging the innovations and optimizations of the latest TensorFlow capabilities.