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## Overview

This guide goes beneath the surface of TensorFlow and Keras to see how TensorFlow works. If you instead want to immediately get started with Keras, please see our collection of Keras guides.

In this guide you'll see the core of how TensorFlow allows you to make simple changes to your code to get graphs, how graphs are stored and represented, and how you can use them to accelerate your models.

**This is a big-picture overview that covers how tf.function allows you to switch from eager execution to graph execution.** For a more complete specification of

`tf.function`

, see the `tf.function`

guide.### What are graphs?

In the previous three guides, you have seen TensorFlow running **eagerly**. This means TensorFlow operations are executed by Python, operation by operation, and returning results back to Python.

While eager execution has several unique advantages, graph execution enables portability outside Python and tends to offer better performance. **Graph execution** means that tensor computations are executed as a *TensorFlow graph*, sometimes referred to as a `tf.Graph`

or simply a "graph."

**Graphs are data structures that contain a set of tf.Operation objects, which represent units of computation; and tf.Tensor objects, which represent the units of data that flow between operations.** They are defined in a

`tf.Graph`

context. Since these graphs are data structures, they can be saved, run, and restored all without the original Python code.This is what a TensorFlow graph representing a two-layer neural network looks like when visualized in TensorBoard.

### The benefits of graphs

With a graph, you have a great deal of flexibility. You can use your TensorFlow graph in environments that don't have a Python interpreter, like mobile applications, embedded devices, and backend servers. TensorFlow uses graphs as the format for saved models when it exports them from Python.

Graphs are also easily optimized, allowing the compiler to do transformations like:

- Statically infer the value of tensors by folding constant nodes in your computation
*("constant folding")*. - Separate sub-parts of a computation that are independent and split them between threads or devices.
- Simplify arithmetic operations by eliminating common subexpressions.

There is an entire optimization system, Grappler, to perform this and other speedups.

In short, graphs are extremely useful and let your TensorFlow run **fast**, run **in parallel**, and run efficiently **on multiple devices**.

However, you still want to define our machine learning models (or other computations) in Python for convenience, and then automatically construct graphs when you need them.

## Taking advantage of graphs

You create and run a graph in TensorFlow by using `tf.function`

, either as a direct call or as a decorator. `tf.function`

takes a regular function as input and returns a `Function`

. **A Function is a Python callable that builds TensorFlow graphs from the Python function. You use a Function in the same way as its Python equivalent.**

```
import tensorflow as tf
import timeit
from datetime import datetime
```

```
# Define a Python function.
def a_regular_function(x, y, b):
x = tf.matmul(x, y)
x = x + b
return x
# `a_function_that_uses_a_graph` is a TensorFlow `Function`.
a_function_that_uses_a_graph = tf.function(a_regular_function)
# Make some tensors.
x1 = tf.constant([[1.0, 2.0]])
y1 = tf.constant([[2.0], [3.0]])
b1 = tf.constant(4.0)
orig_value = a_regular_function(x1, y1, b1).numpy()
# Call a `Function` like a Python function.
tf_function_value = a_function_that_uses_a_graph(x1, y1, b1).numpy()
assert(orig_value == tf_function_value)
```

On the outside, a `Function`

looks like a regular function you write using TensorFlow operations. Underneath, however, it is *very different*. A `Function`

**encapsulates several tf.Graphs behind one API.** That is how

`Function`

is able to give you the benefits of graph execution, like speed and deployability.`tf.function`

applies to a function *and all other functions it calls*:

```
def inner_function(x, y, b):
x = tf.matmul(x, y)
x = x + b
return x
# Use the decorator to make `outer_function` a `Function`.
@tf.function
def outer_function(x):
y = tf.constant([[2.0], [3.0]])
b = tf.constant(4.0)
return inner_function(x, y, b)
# Note that the callable will create a graph that
# includes `inner_function` as well as `outer_function`.
outer_function(tf.constant([[1.0, 2.0]])).numpy()
```

array([[12.]], dtype=float32)

If you have used TensorFlow 1.x, you will notice that at no time did you need to define a `Placeholder`

or `tf.Session`

.

### Converting Python functions to graphs

Any function you write with TensorFlow will contain a mixture of built-in TF operations and Python logic, such as `if-then`

clauses, loops, `break`

, `return`

, `continue`

, and more. While TensorFlow operations are easily captured by a `tf.Graph`

, Python-specific logic needs to undergo an extra step in order to become part of the graph. `tf.function`

uses a library called AutoGraph (`tf.autograph`

) to convert Python code into graph-generating code.

```
def simple_relu(x):
if tf.greater(x, 0):
return x
else:
return 0
# `tf_simple_relu` is a TensorFlow `Function` that wraps `simple_relu`.
tf_simple_relu = tf.function(simple_relu)
print("First branch, with graph:", tf_simple_relu(tf.constant(1)).numpy())
print("Second branch, with graph:", tf_simple_relu(tf.constant(-1)).numpy())
```

First branch, with graph: 1 Second branch, with graph: 0

Though it is unlikely that you will need to view graphs directly, you can inspect the outputs to see the exact results. These are not easy to read, so no need to look too carefully!

```
# This is the graph-generating output of AutoGraph.
print(tf.autograph.to_code(simple_relu))
```

def tf__simple_relu(x): with ag__.FunctionScope('simple_relu', 'fscope', ag__.ConversionOptions(recursive=True, user_requested=True, optional_features=(), internal_convert_user_code=True)) as fscope: do_return = False retval_ = ag__.UndefinedReturnValue() def get_state(): return (do_return, retval_) def set_state(vars_): nonlocal do_return, retval_ (do_return, retval_) = vars_ def if_body(): nonlocal do_return, retval_ try: do_return = True retval_ = ag__.ld(x) except: do_return = False raise def else_body(): nonlocal do_return, retval_ try: do_return = True retval_ = 0 except: do_return = False raise ag__.if_stmt(ag__.converted_call(ag__.ld(tf).greater, (ag__.ld(x), 0), None, fscope), if_body, else_body, get_state, set_state, ('do_return', 'retval_'), 2) return fscope.ret(retval_, do_return)

```
# This is the graph itself.
print(tf_simple_relu.get_concrete_function(tf.constant(1)).graph.as_graph_def())
```

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Most of the time, `tf.function`

will work without special considerations. However, there are some caveats, and the tf.function guide can help here, as well as the complete AutoGraph reference

### Polymorphism: one `Function`

, many graphs

A `tf.Graph`

is specialized to a specific type of inputs (for example, tensors with a specific `dtype`

or objects with the same `id()`

).

Each time you invoke a `Function`

with new `dtypes`

and shapes in its arguments, `Function`

creates a new `tf.Graph`

for the new arguments. The `dtypes`

and shapes of a `tf.Graph`

's inputs are known as an **input signature** or just a **signature**.

The `Function`

stores the `tf.Graph`

corresponding to that signature in a `ConcreteFunction`

. **A ConcreteFunction is a wrapper around a tf.Graph.**

```
@tf.function
def my_relu(x):
return tf.maximum(0., x)
# `my_relu` creates new graphs as it sees more signatures.
print(my_relu(tf.constant(5.5)))
print(my_relu([1, -1]))
print(my_relu(tf.constant([3., -3.])))
```

tf.Tensor(5.5, shape=(), dtype=float32) tf.Tensor([1. 0.], shape=(2,), dtype=float32) tf.Tensor([3. 0.], shape=(2,), dtype=float32)

If the `Function`

has already been called with that signature, `Function`

does not create a new `tf.Graph`

.

```
# These two calls do *not* create new graphs.
print(my_relu(tf.constant(-2.5))) # Signature matches `tf.constant(5.5)`.
print(my_relu(tf.constant([-1., 1.]))) # Signature matches `tf.constant([3., -3.])`.
```

tf.Tensor(0.0, shape=(), dtype=float32) tf.Tensor([0. 1.], shape=(2,), dtype=float32)

Because it's backed by multiple graphs, a `Function`

is **polymorphic**. That enables it to support more input types than a single `tf.Graph`

could represent, as well as to optimize each `tf.Graph`

for better performance.

```
# There are three `ConcreteFunction`s (one for each graph) in `my_relu`.
# The `ConcreteFunction` also knows the return type and shape!
print(my_relu.pretty_printed_concrete_signatures())
```

my_relu(x) Args: x: float32 Tensor, shape=() Returns: float32 Tensor, shape=() my_relu(x) Args: x: float32 Tensor, shape=(2,) Returns: float32 Tensor, shape=(2,) my_relu(x=[1, -1]) Returns: float32 Tensor, shape=(2,)

## Using `tf.function`

So far, you've seen how you can convert a Python function into a graph simply by using `tf.function`

as a decorator or wrapper. But in practice, getting `tf.function`

to work correctly can be tricky! In the following sections, you'll learn how you can make your code work as expected with `tf.function`

.

### Graph execution vs. eager execution

The code in a `Function`

can be executed both eagerly and as a graph. By default, `Function`

executes its code as a graph:

```
@tf.function
def get_MSE(y_true, y_pred):
sq_diff = tf.pow(y_true - y_pred, 2)
return tf.reduce_mean(sq_diff)
```

```
y_true = tf.random.uniform([5], maxval=10, dtype=tf.int32)
y_pred = tf.random.uniform([5], maxval=10, dtype=tf.int32)
print(y_true)
print(y_pred)
```

tf.Tensor([1 2 3 6 8], shape=(5,), dtype=int32) tf.Tensor([0 1 8 7 5], shape=(5,), dtype=int32)

```
get_MSE(y_true, y_pred)
```

<tf.Tensor: shape=(), dtype=int32, numpy=7>

To verify that your `Function`

's graph is doing the same computation as its equivalent Python function, you can make it execute eagerly with `tf.config.run_functions_eagerly(True)`

. This is a switch that **turns off Function's ability to create and run graphs**, instead executing the code normally.

```
tf.config.run_functions_eagerly(True)
```

```
get_MSE(y_true, y_pred)
```

<tf.Tensor: shape=(), dtype=int32, numpy=7>

```
# Don't forget to set it back when you are done.
tf.config.run_functions_eagerly(False)
```

However, `Function`

can behave differently under graph and eager execution. The Python `print`

function is one example of how these two modes differ. Let's see what happens when you insert a `print`

statement to our function and call it repeatedly.

```
@tf.function
def get_MSE(y_true, y_pred):
print("Calculating MSE!")
sq_diff = tf.pow(y_true - y_pred, 2)
return tf.reduce_mean(sq_diff)
```

Observe what is printed:

```
error = get_MSE(y_true, y_pred)
error = get_MSE(y_true, y_pred)
error = get_MSE(y_true, y_pred)
```

Calculating MSE!

Is the output surprising? `get_MSE`

only printed once even though it was called *three* times.

To explain, the `print`

statement is executed when `Function`

runs the original code in order to create the graph in a process known as "tracing". **Tracing captures the TensorFlow operations into a graph, and print is not captured in the graph.** That graph is then executed for all three calls

**without ever running the Python code again**.

As a sanity check, let's turn off graph execution to compare:

```
# Now, globally set everything to run eagerly to force eager execution.
tf.config.run_functions_eagerly(True)
```

```
# Observe what is printed below.
error = get_MSE(y_true, y_pred)
error = get_MSE(y_true, y_pred)
error = get_MSE(y_true, y_pred)
```

Calculating MSE! Calculating MSE! Calculating MSE!

```
tf.config.run_functions_eagerly(False)
```

`print`

is a *Python side effect*, and there are other differences that you should be aware of when converting a function into a `Function`

.

`tf.function`

best practices

It may take some time to get used to the behavior of `Function`

. To get started quickly, first-time users should play around with decorating toy functions with `@tf.function`

to get experience with going from eager to graph execution.

*Designing for tf.function* may be your best bet for writing graph-compatible TensorFlow programs. Here are some tips:

- Toggle between eager and graph execution early and often with
`tf.config.run_functions_eagerly`

to pinpoint if/ when the two modes diverge. - Create
`tf.Variable`

s outside the Python function and modify them on the inside. The same goes for objects that use`tf.Variable`

, like`keras.layers`

,`keras.Model`

s and`tf.optimizers`

. - Avoid writing functions that depend on outer Python variables, excluding
`tf.Variables`

and Keras objects. - Prefer to write functions which take tensors and other TensorFlow types as input. You can pass in other object types but be careful!
- Include as much computation as possible under a
`tf.function`

to maximize the performance gain. For example, decorate a whole training step or the entire training loop.

## Seeing the speed-up

`tf.function`

usually improves the performance of your code, but the amount of speed-up depends on the kind of computation you run. Small computations can be dominated by the overhead of calling a graph. You can measure the difference in performance like so:

```
x = tf.random.uniform(shape=[10, 10], minval=-1, maxval=2, dtype=tf.dtypes.int32)
def power(x, y):
result = tf.eye(10, dtype=tf.dtypes.int32)
for _ in range(y):
result = tf.matmul(x, result)
return result
```

```
print("Eager execution:", timeit.timeit(lambda: power(x, 100), number=1000))
```

Eager execution: 1.777665522999996

```
power_as_graph = tf.function(power)
print("Graph execution:", timeit.timeit(lambda: power_as_graph(x, 100), number=1000))
```

Graph execution: 0.5308018169999968

`tf.function`

is commonly used to speed up training loops, as you can see here with Keras.

### Performance and trade-offs

Graphs can speed up your code, but the process of creating them has some overhead. For some functions, the creation of the graph takes more time than the execution of the graph. **This investment is usually quickly paid back with with the performance boost of subsequent executions, but it's important to be aware that the first few steps of any large model training can be slower due to tracing.**

No matter how large your model, you want to avoid tracing frequently. The `tf.function`

guide discusses how to set input specifications and use tensor arguments to avoid retracing. If you find you are getting unusually poor performance, it's a good idea to check if you are retracing accidentally.

## When is a `Function`

tracing?

To figure out when your `Function`

is tracing, add a `print`

statement to its code. As a rule of thumb, `Function`

will execute the `print`

statement every time it traces.

```
@tf.function
def a_function_with_python_side_effect(x):
print("Tracing!") # An eager-only side effect.
return x * x + tf.constant(2)
# This is traced the first time.
print(a_function_with_python_side_effect(tf.constant(2)))
# The second time through, you won't see the side effect.
print(a_function_with_python_side_effect(tf.constant(3)))
```

Tracing! tf.Tensor(6, shape=(), dtype=int32) tf.Tensor(11, shape=(), dtype=int32)

```
# This retraces each time the Python argument changes,
# as a Python argument could be an epoch count or other
# hyperparameter.
print(a_function_with_python_side_effect(2))
print(a_function_with_python_side_effect(3))
```

Tracing! tf.Tensor(6, shape=(), dtype=int32) Tracing! tf.Tensor(11, shape=(), dtype=int32)

Here, you see extra tracing because new Python arguments always trigger the creation of a new graph.

## Next steps

You can read a more in-depth discussion at both the `tf.function`

API reference page and at the guide.