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Chapter 2
state = [tf.zeros([100, 100])] * 2
# warmup
cell(input, state)
fn(input, state)
graph_time = timeit.timeit(lambda: cell(input, state), number=100)
auto_graph_time = timeit.timeit(lambda: fn(input, state), number=100)
print('graph_time:', graph_time)
print('auto_graph_time:', auto_graph_time)
When the code fragment is executed, you see a reduction in time of one order of
magnitude if tf.function() is used:
graph_time: 0.4504085020016646
auto_graph_time: 0.07892408400221029
In short, you can decorate Python functions and methods with tf.function, which
converts them to the equivalent of a static graph, with all the optimization that comes
with it.
Keras APIs – three programming models
TensorFlow 1.x provides a lower-level API. You build models by first creating a
graph of ops, which you then compile and execute. tf.keras offers a higher API
level, with three different programming models: Sequential API, Functional API, and
Model Subclassing. Learning models are created as easily as "putting LEGO ® bricks
together," where each "lego brick" is a specific Keras.layer. Let's see when it is best
to use Sequential, Functional, and Subclassing, and note that you can mix-and-match
the three styles according to your specific needs.
Sequential API
The Sequential API is a very elegant, intuitive, and concise model that is appropriate
in 90% of cases. In the previous chapter, we covered an example of using the
Sequential API when we discussed the MNIST code, so let's create the brick with:
tf.keras.utils.plot_model(model, to_file="model.png")
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