SPEAKER: In this video, I'll show you

how you can use Autograph to write complex, high performance

TensorFlow code using normal Python.

Autograph is available in the new TF2 function API,

makes it easy to run TensorFlow computations in a way

that's efficient and portable.

When you annotate a Python function with tf.function,

Autograph will automatically convert its Python code

to TensorFlow graph code.

The code is then compiled into a graph

and executed when you call the function.

Let's look at an example.

This simple function calculates the square of a scalar input

if it's positive.

In TensorFlow 2.0, you don't have to use tf.cond anymore.

You can just write a normal if statement,

and Autograph will generate a tf.cond operation

so that the entire computation runs as a graph.

This is the generated code that Autograph writes for you.

Notice that we're writing true and false functions that

would normally be fed into a tf.cond statement.

Instead of writing these, you can simply

use Python if statements.

Let's take a look at a more complicated example.

This is a bare bones RNN cell.

Note that it contains a data dependent for loop,

and it also contains a data independent if statement.

Autograph will only run the data dependent loop in the graph

and leave the data independent if statement untouched.

Simply adding a tf.function as a decorator

still lets you call the function directly and get results

immediately.

But the function runs in graph mode.

It prints results.

And we can also time it.

Now, if we remove the tf.function decorator, which

I've preemptively done here, and run the function in eager mode,

we get the same results out.

However, it's going to be a little bit slower because we

won't have coalesced the entire function into a single tf.graph

op.

We can time both options with tf.function in Autograph

and without.

You'll note that using tf.function, which

requires only a single function decorator,

is significantly faster than the eager mode version

without tf.function.

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