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Wraps a python function and uses it as a TensorFlow op.
tf.numpy_function(
func, inp, Tout, name=None
)
Given a python function func
wrap this function as an operation in a
TensorFlow function. func
must take numpy arrays as its arguments and
return numpy arrays as its outputs.
The following example creates a TensorFlow graph with np.sinh()
as an
operation in the graph:
def my_numpy_func(x):
# x will be a numpy array with the contents of the input to the
# tf.function
return np.sinh(x)
@tf.function(input_signature=[tf.TensorSpec(None, tf.float32)])
def tf_function(input):
y = tf.numpy_function(my_numpy_func, [input], tf.float32)
return y * y
tf_function(tf.constant(1.))
<tf.Tensor: shape=(), dtype=float32, numpy=1.3810978>
Comparison to tf.py_function
:
tf.py_function
and tf.numpy_function
are very similar, except that
tf.numpy_function
takes numpy arrays, and not tf.Tensor
s. If you want the
function to contain tf.Tensors
, and have any TensorFlow operations executed
in the function be differentiable, please use tf.py_function
.
The body of the function (i.e.
func
) will not be serialized in atf.SavedModel
. 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.numpy_function()
. If you are using distributed TensorFlow, you must run atf.distribute.Server
in the same process as the program that callstf.numpy_function
you must pin the created operation to a device in that server (e.g. usingwith tf.device():
).Since the function takes numpy arrays, you cannot take gradients through a numpy_function. If you require something that is differentiable, please consider using tf.py_function.
The resulting function is assumed stateful and will never be optimized.
Args | |
---|---|
func
|
A Python function, which accepts numpy.ndarray objects as arguments
and returns a list of numpy.ndarray objects (or a single
numpy.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 numpy.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. In-place modification
or storing func input or return values in python datastructures
without explicit (np.)copy can have non-deterministic consequences.
|
inp
|
A list of tf.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.
|
name
|
(Optional) A name for the operation. |
Returns | |
---|---|
Single or list of tf.Tensor which func computes.
|