tf.numpy_function

TensorFlow 1 version

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Wraps a python function and uses it as a TensorFlow op.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.numpy_function

tf.numpy_function(
    func, inp, Tout, name=None
)

Used in the notebooks

Used in the tutorials
Playing CartPole with the Actor-Critic Method Image captioning with visual attention

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.Tensors. 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 a tf.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 a tf.distribute.Server in the same process as the program that calls tf.numpy_function you must pin the created operation to a device in that server (e.g. using with 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.

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tf / numpy_function

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Used in the notebooks

Args

Returns