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Wraps a python function and uses it as a TensorFlow op.
tf.contrib.framework.py_func( func, args=(), kwargs=None, output_types=None, output_shapes=None, stateful=True, name=None )
This function is a wrapper around
tf.compat.v1.py_func and improve it with
and output_shapes. Further it changed some argument names.
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.compat.v1.placeholder(tf.float32) y = tf.compat.v1.py_func(my_func, [inp], tf.float32)
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.compat.v1.py_func(). If you are using distributed TensorFlow, you must run a
tf.distribute.Serverin the same process as the program that calls
tf.compat.v1.py_func()and you must pin the created operation to a device in that server (e.g. using
A Python function, which accepts a list of NumPy
A list of
A dict with
A nested structure of tensorflow data types or a single
tensorflow data type if there is only one, indicating what
||Same as output_types, except the types are replaces with shapes (optional).|
||(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.|
||A name for the operation (optional).|
|Tensorflow op that wraps the input python function.|