Decorator to compile func into graph_mode.
defun converts a function that constructs a TensorFlow graph into a function
that executes the graph. TensorFlow graphs typically execute faster and with a
lower memory-footprint than executing each of the operations that make up the
function individually as the TensorFlow runtime can optimize the graph and
execute sub-operations in parallel.
func must be a Python function that constructs a TensorFlow graph, typically using functions in the tensorflow module.
Arguments to func can be either Tensor objects or Python objects. Non-Tensor python objects are treated as constants, and new function definitions are created internally based on their values.
func must return a tf.Tensor (NOT a Tensor) or a list of tf.Tensor (NOT a Tensor).
Control flow constructs (e.g.,
while) are not yet compatible with
def f(x, y): return tf.reduce_mean(tf.multiply(x ** 2, 3) + y) @tfe.defun def g(x, y): return tf.reduce_mean(tf.multiply(x ** 2, 3) + y) x = tf.constant([[2.0, 3.0]]) y = tf.constant([[3.0, -2.0]]) .html# The plain function and defun-compiled function should return the same value. assert f(x, y).numpy() == g(x, y).numpy() .html# After the first invocation, the defun-compiled (graph) function runs faster .html# than the plain function because the defun-compiled function does not involve .html# Python interpreter overhead during the execution. %time print(f(x, y)) %time print(g(x, y))
func: function to be compiled.
A callable that will execute the compiled function (and return zero or more Tensor objects).