TensorFlow `numpy_function`: Using Python Functions as TensorFlow Ops

Tensors and operations define the computational graph in TensorFlow. There are times, however, when you might want to leverage regular Python functions within a TensorFlow session. This is exactly what the numpy_function API allows you to do. It enables the wrapping of arbitrary Python functions as tensor ops, though with some caveats related to data type and batch handling that we'll explore below.

Understanding numpy_function

The numpy_function serves as a bridge for using Python functions that act primarily on NumPy arrays directly in TensorFlow's graph environment. If you've ever written a function that heavily relies on NumPy operations, this API will enable you to incorporate that functionality without having to rewrite any TensorFlow-specific operations.

Key Features and Limitations:

• Inputs and outputs of your Python function must be convertible to NumPy arrays.
• The function must return and accept data in NumPy array format.
• The output shape cannot be dynamically determined. TensorFlow must know the shape of the data beforehand.
• numpy_function constructs have no direct GPU compatibility.

Basic Example

Let's walk through a basic example that uses TensorFlow's numpy_function:

import tensorflow as tf
import numpy as np

def my_func(x):
    # This function calculates the square minus the original value
    return np.square(x) - x

x = tf.constant([[2.0, 3.0], [5.0, 7.0]], dtype=tf.float32)
result = tf.numpy_function(my_func, [x], tf.float32)

# Evaluate the tensor
with tf.Session() as sess:
    print(sess.run(result))

In this snippet, my_func defines a NumPy-based operation that can be wrapped into a TensorFlow op. Notice how we only needed to define the input list and the output type for numpy_function. The output type must be specified to help TensorFlow understand how to treat the results within the graph.

Handling Output Shapes

As previously mentioned, numpy_function cannot infer the output shape automatically. This requires you, as the developer, to explicitly declare the shapes using TensorFlow operations along with numpy_function. Here’s how you can specify shapes:

def my_func_with_shape(x):
    return np.sqrt(x)

# Input tensor
x = tf.constant([[4.0, 9.0], [16.0, 25.0]], dtype=tf.float32)

# Define the shape explicitly with tf.Tensor.set_shape()
output = tf.numpy_function(my_func_with_shape, [x], tf.float32)
output.set_shape(x.shape)

# Evaluate the tensor
with tf.Session() as sess:
    shaped_result = sess.run(output)
    print(shaped_result)  # Expected [[2.0, 3.0], [4.0, 5.0]]

Considerations and Best Practices

numpy_function demonstrates impressive flexibility. Nevertheless, like every tool, it should be used judiciously:

• Use for initial prototyping. For efficiency and potentially adding support for Graphics Processing Units (GPUs), consider porting to native TensorFlow ops later.
• Ideal for computationally lightweight operations, as its use may lead to performance bottlenecks in computation-heavy tasks.
• When dealing with large datasets, beware of data copying overheads as NumPy operates in different memory space than TensorFlow by default.
• Always test thoroughly since numpy_function could behave unexpectedly with partial gradient definitions in certain complex graphs.

Conclusion

The numpy_function API empowers you to integrate existing Python code and leverage NumPy operations within a TensorFlow training loop. While it boasts significant flexibility and charm during experimentation phases, performance and compatibility with TensorFlow's advanced functions warrant eventual conversion into TensorFlow native functions for production readiness.