TensorFlow provides allows you to wrap python/numpy functions as TensorFlow operators.
tf.py_func(func, inp, Tout, stateful=True, name=None)
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. E.g.,
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 = py_func(my_func, [inp], [tf.float32])
The above snippet constructs a tf graph which invokes a numpy sinh(x) as an op in the graph.
func: A python function.
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: A boolean indicating whether the function should be considered stateful or stateless. I.e. whether it, given the same input, will return the same output and at the same time does not change state in an observable way. Optimizations such as common subexpression elimination are only possible when operations are stateless.
name: A name for the operation (optional).
A list of
Tensor or a single