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Wraps a python function and uses it as a TensorFlow op.
tf.compat.v1.py_func(
func, inp, Tout, stateful=True, name=None
)
Migrate to TF2
This name was deprecated and removed in TF2, but tf.numpy_function
is a
nearexact replacement, just drop the stateful
argument (all
tf.numpy_function
calls are considered stateful). It is compatible with
eager execution and tf.function
.
tf.py_function
is a close but not an exact replacement, passing TensorFlow
tensors to the wrapped function instead of NumPy arrays, which provides
gradients and can take advantage of accelerators.
Before:
def fn_using_numpy(x):
x[0] = 0.
return x
tf.compat.v1.py_func(fn_using_numpy, inp=[tf.constant([1., 2.])],
Tout=tf.float32, stateful=False)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0., 2.], dtype=float32)>
After:
tf.numpy_function(fn_using_numpy, inp=[tf.constant([1., 2.])],
Tout=tf.float32)
<tf.Tensor: shape=(2,), dtype=float32, numpy=array([0., 2.], dtype=float32)>
Description
Given a python function func
, which takes numpy arrays as its
arguments 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)
input = tf.compat.v1.placeholder(tf.float32)
y = tf.compat.v1.py_func(my_func, [input], tf.float32)
The body of the function (i.e.
func
) will not be serialized in aGraphDef
. 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 atf.distribute.Server
in the same process as the program that callstf.compat.v1.py_func()
and you must pin the created operation to a device in that server (e.g. usingwith tf.device():
).
E.g.
import tensorflow as tf
import numpy as np
def make_synthetic_data(i):
return np.cast[np.uint8](i) * np.ones([20,256,256,3],
dtype=np.float32) / 10.
def preprocess_fn(i):
ones = tf.py_function(make_synthetic_data,[i],tf.float32)
ones.set_shape(tf.TensorShape([None, None, None, None]))
ones = tf.image.resize(ones, [224,224])
return ones
ds = tf.data.Dataset.range(10)
ds = ds.map(preprocess_fn)
Args  

func

A Python function, which accepts ndarray objects as arguments and
returns a list of ndarray objects (or a single ndarray ). This function
must accept as many arguments as there are tensors in inp , and these
argument types will match the corresponding tf.Tensor objects in inp .
The returns ndarray s must match the number and types defined Tout .
Important Note: Input and output numpy ndarray s of func are not
guaranteed to be copies. In some cases their underlying memory will be
shared with the corresponding TensorFlow tensors. Inplace modification
or storing func input or return values in python datastructures
without explicit (np.)copy can have nondeterministic consequences.

inp

A list of Tensor objects.

Tout

A list or tuple of tensorflow data types or a single tensorflow data
type if there is only one, indicating what func returns.

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). 
Returns  

A list of Tensor or a single Tensor which func computes.
