What is the difference between static and dynamic computation graphs in TensorFlow?

Step 1: Understanding the Key Differences

The main difference between TensorFlow 1.x and 2.x is the execution model.

Step 2: Migrating from TensorFlow 1.x to 2.x

Here is a migration path for the existing TensorFlow 1.x code:

1. Enable eager execution:

The first step is to enable eager execution. This can be done by adding the following line to the top of your script:

import tensorflow as tf
tf.compat.v1.enable_eager_execution()

2. Replace tf.placeholder and tf.Session.run:

In TensorFlow 2.x, you don’t need to use tf.placeholder or tf.Session.run. You can just pass the data directly to the model as a regular Python function.

TensorFlow 1.x:

x = tf.placeholder(tf.float32, shape=[None, 784])
y = model(x)

with tf.Session() as sess:
    result = sess.run(y, feed_dict={x: data})

TensorFlow 2.x:

y = model(data)

3. Use tf.function to optimize performance:

To get the performance benefits of static graphs, you can use the tf.function decorator to convert your Python functions into TensorFlow graphs.

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images, training=True)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

Step 3: The Best of Both Worlds with tf.function

The tf.function decorator allows you to combine the ease of use of dynamic graphs with the performance of static graphs. It does this by “tracing” your Python code to create a static graph that can be optimized and executed in parallel.

This means that you can write your code in a natural, Pythonic way, and then use tf.function to automatically optimize it for performance.