tf.function     View source on GitHub    Compiles a function into a callable TensorFlow graph.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.function  
tf.function(
    func=None, input_signature=None, autograph=True, experimental_implements=None,
    experimental_autograph_options=None, experimental_relax_shapes=False,
    experimental_compile=None, experimental_follow_type_hints=None
)
 tf.function constructs a callable that executes a TensorFlow graph (tf.Graph) created by trace-compiling the TensorFlow operations in func, effectively executing func as a TensorFlow graph. Example usage: 
@tf.function
def f(x, y):
  return x ** 2 + y
x = tf.constant([2, 3])
y = tf.constant([3, -2])
f(x, y)
<tf.Tensor: ... numpy=array([7, 7], ...)>
 Features func may use data-dependent control flow, including if, for, while break, continue and return statements: 
@tf.function
def f(x):
  if tf.reduce_sum(x) > 0:
    return x * x
  else:
    return -x // 2
f(tf.constant(-2))
<tf.Tensor: ... numpy=1>
 func's closure may include tf.Tensor and tf.Variable objects: 
@tf.function
def f():
  return x ** 2 + y
x = tf.constant([-2, -3])
y = tf.Variable([3, -2])
f()
<tf.Tensor: ... numpy=array([7, 7], ...)>
 func may also use ops with side effects, such as tf.print, tf.Variable and others: 
v = tf.Variable(1)
@tf.function
def f(x):
  for i in tf.range(x):
    v.assign_add(i)
f(3)
v
<tf.Variable ... numpy=4>
 Key Point: Any Python side-effects (appending to a list, printing with print, etc) will only happen once, when func is traced. To have side-effects executed into your tf.function they need to be written as TF ops: 
l = []
@tf.function
def f(x):
  for i in x:
    l.append(i + 1)    # Caution! Will only happen once when tracing
f(tf.constant([1, 2, 3]))
l
[<tf.Tensor ...>]
 Instead, use TensorFlow collections like tf.TensorArray: 
@tf.function
def f(x):
  ta = tf.TensorArray(dtype=tf.int32, size=0, dynamic_size=True)
  for i in range(len(x)):
    ta = ta.write(i, x[i] + 1)
  return ta.stack()
f(tf.constant([1, 2, 3]))
<tf.Tensor: ..., numpy=array([2, 3, 4], ...)>
 tf.function is polymorphic Internally, tf.function can build more than one graph, to support arguments with different data types or shapes, since TensorFlow can build more efficient graphs that are specialized on shapes and dtypes. tf.function also treats any pure Python value as opaque objects, and builds a separate graph for each set of Python arguments that it encounters. To obtain an individual graph, use the get_concrete_function method of the callable created by tf.function. It can be called with the same arguments as func and returns a special tf.Graph object: 
@tf.function
def f(x):
  return x + 1
isinstance(f.get_concrete_function(1).graph, tf.Graph)
True
 Caution: Passing python scalars or lists as arguments to tf.function will always build a new graph. To avoid this, pass numeric arguments as Tensors whenever possible: 
@tf.function
def f(x):
  return tf.abs(x)
f1 = f.get_concrete_function(1)
f2 = f.get_concrete_function(2)  # Slow - builds new graph
f1 is f2
False
f1 = f.get_concrete_function(tf.constant(1))
f2 = f.get_concrete_function(tf.constant(2))  # Fast - reuses f1
f1 is f2
True
 Python numerical arguments should only be used when they take few distinct values, such as hyperparameters like the number of layers in a neural network. Input signatures For Tensor arguments, tf.function instantiates a separate graph for every unique set of input shapes and datatypes. The example below creates two separate graphs, each specialized to a different shape: 
@tf.function
def f(x):
  return x + 1
vector = tf.constant([1.0, 1.0])
matrix = tf.constant([[3.0]])
f.get_concrete_function(vector) is f.get_concrete_function(matrix)
False
 An "input signature" can be optionally provided to tf.function to control the graphs traced. The input signature specifies the shape and type of each Tensor argument to the function using a tf.TensorSpec object. More general shapes can be used. This is useful to avoid creating multiple graphs when Tensors have dynamic shapes. It also restricts the shape and datatype of Tensors that can be used: 
@tf.function(
    input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])
def f(x):
  return x + 1
vector = tf.constant([1.0, 1.0])
matrix = tf.constant([[3.0]])
f.get_concrete_function(vector) is f.get_concrete_function(matrix)
True
 Variables may only be created once tf.function only allows creating new tf.Variable objects when it is called for the first time: 
class MyModule(tf.Module):
  def __init__(self):
    self.v = None

  @tf.function
  def __call__(self, x):
    if self.v is None:
      self.v = tf.Variable(tf.ones_like(x))
    return self.v * x
 In general, it is recommended to create stateful objects like tf.Variable outside of tf.function and passing them as arguments. Using type annotations to improve performance 'experimental_follow_type_hints` can be used along with type annotations to improve performance by reducing the number of expensive graph retracings. For example, an argument annotated with tf.Tensor is converted to Tensor even when the input is a non-Tensor value. 
@tf.function(experimental_follow_type_hints=True)
def f_with_hints(x: tf.Tensor):
  print('Tracing')
  return x
@tf.function(experimental_follow_type_hints=False)
def f_no_hints(x: tf.Tensor):
  print('Tracing')
  return x
f_no_hints(1)
Tracing
<tf.Tensor: shape=(), dtype=int32, numpy=1>
f_no_hints(2)
Tracing
<tf.Tensor: shape=(), dtype=int32, numpy=2>
f_with_hints(1)
Tracing
<tf.Tensor: shape=(), dtype=int32, numpy=1>
f_with_hints(2)
<tf.Tensor: shape=(), dtype=int32, numpy=2>

 


 Args
  func   the function to be compiled. If func is None, tf.function returns a decorator that can be invoked with a single argument - func. In other words, tf.function(input_signature=...)(func) is equivalent to tf.function(func, input_signature=...). The former can be used as decorator.  
  input_signature   A possibly nested sequence of tf.TensorSpec objects specifying the shapes and dtypes of the Tensors that will be supplied to this function. If None, a separate function is instantiated for each inferred input signature. If input_signature is specified, every input to func must be a Tensor, and func cannot accept **kwargs.  
  autograph   Whether autograph should be applied on func before tracing a graph. Data-dependent control flow requires autograph=True. For more information, see the tf.function and AutoGraph guide.  
  experimental_implements   If provided, contains a name of a "known" function this implements. For example "mycompany.my_recurrent_cell". This is stored as an attribute in inference function, which can then be detected when processing serialized function. See standardizing composite ops for details. For an example of utilizing this attribute see this example The code above automatically detects and substitutes function that implements "embedded_matmul" and allows TFLite to substitute its own implementations. For instance, a tensorflow user can use this attribute to mark that their function also implements embedded_matmul (perhaps more efficiently!) by specifying it using this parameter: @tf.function(experimental_implements="embedded_matmul") This can either be specified as just the string name of the function or a NameAttrList corresponding to a list of key-value attributes associated with the function name. The name of the function will be in the 'name' field of the NameAttrList.  
  experimental_autograph_options   Optional tuple of tf.autograph.experimental.Feature values.  
  experimental_relax_shapes   When True, tf.function may generate fewer, graphs that are less specialized on input shapes.  
  experimental_compile   If True, the function is always compiled by XLA. XLA may be more efficient in some cases (e.g. TPU, XLA_GPU, dense tensor computations).  
  experimental_follow_type_hints   When True, the function may use type annotations from func to optimize the tracing performance. For example, arguments annotated with tf.Tensor will automatically be converted to a Tensor.   
 


 Returns   If func is not None, returns a callable that will execute the compiled function (and return zero or more tf.Tensor objects). If func is None, returns a decorator that, when invoked with a single func argument, returns a callable equivalent to the case above.  

 


 Raises   ValueError when attempting to use experimental_compile, but XLA support is not enabled.