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Wraps a python function and uses it as a TensorFlow op.
tf.numpy_function(
func, inp, Tout, stateful=True, 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
.
Calling
tf.numpy_function
will acquire the Python Global Interpreter Lock (GIL) that allows only one thread to run at any point in time. This will preclude efficient parallelization and distribution of the execution of the program. Therefore, you are discouraged to usetf.numpy_function
outside of prototyping and experimentation.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():
).Currently
tf.numpy_function
is not compatible with XLA. Callingtf.numpy_function
insidetf.function(jit_compile=True)
will raise an error.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.
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.
|
stateful
|
(Boolean.) Setting this argument to False tells the runtime to
treat the function as stateless, which enables certain optimizations.
A function is stateless when given the same input it will return the
same output and have no side effects; its only purpose is to have a
return value.
The behavior for a stateful function with the stateful argument False
is undefined. In particular, caution should be taken when
mutating the input arguments as this is a stateful operation.
|
name
|
(Optional) A name for the operation. |
Returns | |
---|---|
Single or list of tf.Tensor which func computes.
|