TensorFlow `case`: Implementing Conditional Execution with `case`

TensorFlow, a robust framework for building machine learning models, provides a variety of control flow operations that allow for more dynamic model behavior. One such operation is the tf.case function, which is essential for creating imperative machine learning models where different branches can be executed based on specific conditions. In this article, we will explore how to leverage the tf.case function for conditional execution in TensorFlow.

Understanding tf.case

The tf.case function can be compared to the switch-case construct found in many programming languages but is designed to handle TensorFlow's symbolic graph execution style. It takes a list of predicate-function pairs and an optional default function.

The signature of the tf.case function is:

tf.case(pred_fn_pairs, default=None, exclusive=False, strict=False, name='case')

• pred_fn_pairs: A list of tuples where each tuple is a pair consisting of a callable predicate and a function. The first predicate that returns True triggers its associated function.
• default: A callable that serves as the default branch if no predicates are satisfied.
• exclusive: If True, only one function will be executed. If multiple predicates return True, a runtime error is triggered.
• strict: If True, implies that all functions and the default must return the same types and shapes.

Basic Example of tf.case

Let’s illustrate a simple example where tf.case is used to decide an outcome based on input values.

import tensorflow as tf

a = tf.constant(5)
b = tf.constant(10)

# Define the predicates and functions
pred_fn_pairs = {
    tf.equal(a, b): lambda: tf.add(a, b),
    tf.less(a, b): lambda: tf.subtract(b, a)
}

default_fn = lambda: tf.constant(0)

# Use tf.case
result = tf.case(pred_fn_pairs, default=default_fn, strict=True)

# Add a session for fetching the output
with tf.Session() as sess:
    print(sess.run(result))  # Output will be 5

Use Case in Model Scenarios

The ability to conditionally execute different portions of the computation graph using tf.case is particularly powerful for dynamic neural network architectures. For example, consider a model that processes two different types of inputs depending on an external condition.

# Setup a placeholder that defines the type of input
input_type = tf.placeholder(dtype=tf.bool)

# Define branches for the model
branch1 = lambda: tf.multiply(a, b)
branch2 = lambda: tf.multiply(b, a)

# Conditional execution
result = tf.case([(tf.equal(input_type, True), branch1)], default=branch2, exclusive=True)

# Placeholder value
with tf.Session() as sess:
    result_val = sess.run(result, feed_dict={input_type: True})
    print("Result with input_type True: ", result_val)

In practice, the decision-making process inside a neural network could dictate that different sets of layers or different model paths are executed thanks to this conditional execution pattern.

Advanced Use: Tensor Shapes and Types

Combining tensors of varying shapes and types in conditional execution can be a bit more intricate where strict would require all branches to align. Let’s expand on our initial example to include varying shapes.

c = tf.constant([3, 7])
d = tf.constant([5, 2])

# Mix shapes in predicates
pred_fn_pairs = {
    tf.reduce_sum(c) > tf.reduce_sum(d): lambda: tf.add(c, d),
    tf.reduce_max(c) < tf.reduce_max(d): lambda: tf.subtract(c, d)
}

# Define a default function
default_fn = lambda: tf.constant([1, 1])

# Tensor conditional execution
result = tf.case(pred_fn_pairs, default=default_fn)

with tf.Session() as sess:
    print("Conditional execution result:", sess.run(result))

Conclusion

The tf.case operation provides incredible flexibility for implementing logic into TensorFlow models, allowing conditional execution of segments of your computation graph. Whether developing models that need different behaviors based on input conditions or structuring custom control flow, leveraging tf.case can lead to more dynamic and effective machine learning models.