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TensorFlow 1.x and 2.x
>>> simple_function
<function simple_function at 0xb26c3e510>
Note that with tf.function you need to annotate only one main function,
so all other functions called from there will be automatically and transparently
transformed into an optimized computational graph. For example, in the preceding
code, there is no need to annotate linear_layer. Think about tf.function as
marking code for Just In Time (JIT) compilation. Normally, you do not need to
see the code automatically generated by AutoGraph, but in case you are curious,
then you can investigate with the following code fragment:
# internal look at the auto-generated code
print(tf.autograph.to_code(simple_nn.python_function, experimental_
optional_features=None))
This code fragment will print the following automatically generated piece of code:
from __future__ import print_function
def tf__simple_nn(x):
do_return = False
retval_ = None
do_return = True
retval_ = ag__.converted_call('relu', tf.nn, ag__.ConversionOptions
(recursive=True, verbose=0, strip_decorators=(ag__.convert, ag__.do_
not_convert, ag__.converted_call), force_conversion=False, optional_
features=(), internal_convert_user_code=True), (linear_layer(x),), {})
return retval_
tf__simple_nn.autograph_info__ = {}
Let's see an example of the difference in speed between code annotated with the
tf.function() decorator and the same code with no annotation. Here we use
a layer LSTMCell() which will be discussed in Chapter 8, Recurrent Neural Networks,
but for now consider it as a black box doing some form of learning:
import tensorflow as tf
import timeit
cell = tf.keras.layers.LSTMCell(100)
@tf.function
def fn(input, state):
return cell(input, state)
input = tf.zeros([100, 100])
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