tf.py_func(func, inp, Tout, stateful=True, name=None)
See the guide: Wraps python functions > Script Language Operators
Wraps a python function and uses it as a TensorFlow op.
Given a python function
func, which takes numpy arrays as its
inputs and returns numpy arrays as its outputs, wrap this function as an
operation in a TensorFlow graph. The following snippet constructs a simple
TensorFlow graph that invokes the
np.sinh() NumPy function as a operation
in the graph:
def my_func(x): # x will be a numpy array with the contents of the placeholder below return np.sinh(x) inp = tf.placeholder(tf.float32) y = tf.py_func(my_func, [inp], tf.float32)
tf.py_func() operation has the following known limitations:
The body of the function (i.e.
func) will not be serialized in a
GraphDef. Therefore, you should not use this function if you need to serialize your model and restore it in a different environment.
The operation must run in the same address space as the Python program that calls
tf.py_func(). If you are using distributed TensorFlow, you must run a
tf.train.Serverin the same process as the program that calls
tf.py_func()and you must pin the created operation to a device in that server (e.g. using
func: A Python function, which accepts a list of NumPy
ndarrayobjects having element types that match the corresponding
inp, and returns a list of
ndarrayobjects (or a single
ndarray) having element types that match the corresponding values in
inp: A list of
Tout: A list or tuple of tensorflow data types or a single tensorflow data type if there is only one, indicating what
stateful: (Boolean.) If True, the function should be considered stateful. If a function is stateless, when given the same input it will return the same output and have no observable side effects. Optimizations such as common subexpression elimination are only performed on stateless operations.
name: A name for the operation (optional).
A list of
Tensor or a single