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tft.apply_pyfunc

tft.apply_pyfunc(
    func,
    Tout,
    stateful=True,
    name=None,
    *args
)

Applies a python function to some Tensors.

Applies a python function to some Tensors given by the argument list. The number of arguments should match the number of inputs to the function.

This function is for using inside a preprocessing_fn. It is a wrapper around tf.py_func. A function added this way can run in Transform, and during training when the graph is imported using the transform_raw_features method of the TFTransformOutput class. However if the resulting training graph is serialized and deserialized, then the tf.py_func op will not work and will cause an error. This means that TensorFlow Serving will not be able to serve this graph.

The underlying reason for this limited support is that tf.py_func ops were not designed to be serialized since they contain a reference to arbitrary Python functions. This function pickles those functions and including them in the graph, and transform_raw_features similarly unpickles the functions. But unpickling requires a Python environment, so there it's not possible to provide support in non-Python languages for loading such ops. Therefore loading these ops in libraries such as TensorFlow Serving is not supported.

Args:

  • func: A Python function, which accepts a list of NumPy ndarray objects having element types that match the corresponding tf.Tensor objects in *args, and returns a list of ndarray objects (or a single ndarray) having element types that match the corresponding values in Tout.
  • 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).
  • *args: The list of Tensors to apply the arguments to.

Returns:

A Tensor representing the application of the function.