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tf.py_function

Wraps a python function into a TensorFlow op that executes it eagerly.

Used in the notebooks

Used in the guide Used in the tutorials

This function allows expressing computations in a TensorFlow graph as Python functions. In particular, it wraps a Python function func in a once-differentiable TensorFlow operation that executes it with eager execution enabled. As a consequence, tf.py_function makes it possible to express control flow using Python constructs (if, while, for, etc.), instead of TensorFlow control flow constructs (tf.cond, tf.while_loop). For example, you might use tf.py_function to implement the log huber function:

def log_huber(x, m):
  if tf.abs(x) <= m:
    return x**2
  else:
    return m**2 * (1 - 2 * tf.math.log(m) + tf.math.log(x**2))

x = tf.compat.v1.placeholder(tf.float32)
m = tf.compat.v1.placeholder(tf.float32)

y = tf.py_function(func=log_huber, inp=[x, m], Tout=tf.float32)
dy_dx = tf.gradients(y, x)[0]

with tf.compat.v1.Session() as sess:
  # The session executes `log_huber` eagerly. Given the feed values below,
  # it will take the first branch, so `y` evaluates to 1.0 and
  # `dy_dx` evaluates to 2.0.
  y, dy_dx = sess.run([y, dy_dx], feed_dict={x: 1.0, m: 2.0})

You can also use tf.py_function to debug your models at runtime using Python tools, i.e., you can isolate portions of your code that you want to debug, wrap them in Python functions and insert pdb tracepoints or print statements as desired, and wrap those functions in tf.py_function.

For more information on eager execution, see the Eager guide.

tf.py_function is similar in spirit to tf.compat.v1.py_func, but unlike the latter, the former lets you use TensorFlow operations in the wrapped Python function. In particular, while tf.compat.v1.py_func only runs on CPUs and wraps functions that take NumPy arrays as inputs and return NumPy