# Structuring Your TensorFlow Models

Defining your models in TensorFlow can easily result in one huge wall of code. How to structure your code in a readable and reusable way? For the impatient of you, here is the link to a working example gist. You might also want to take a look at my new post on fast prototyping in TensorFlow, that builds on the idea described here.

## Defining the Compute Graph

It’s sensible to start with one class per model. What is the interface of that
class? Usually, your model connects to some input *data* and *target*
placeholders and provides operations for *training*, *evaluation*, and
*inference*.

```
class Model:
def __init__(self, data, target):
data_size = int(data.get_shape()[1])
target_size = int(target.get_shape()[1])
weight = tf.Variable(tf.truncated_normal([data_size, target_size]))
bias = tf.Variable(tf.constant(0.1, shape=[target_size]))
incoming = tf.matmul(data, weight) + bias
self._prediction = tf.nn.softmax(incoming)
cross_entropy = -tf.reduce_sum(target, tf.log(self._prediction))
self._optimize = tf.train.RMSPropOptimizer(0.03).minimize(cross_entropy)
mistakes = tf.not_equal(
tf.argmax(target, 1), tf.argmax(self._prediction, 1))
self._error = tf.reduce_mean(tf.cast(mistakes, tf.float32))
@property
def prediction(self):
return self._prediction
@property
def optimize(self):
return self._optimize
@property
def error(self):
return self._error
```

This is basically, how models are defined in the TensorFlow codebase. However, there are some problems with it. Most notably, the whole graph is define in a single function, the constructor. This is neither particularly readable nor reusable.

## Using Properties

Just splitting the code into functions doesn’t work, since every time the functions are called, the graph would be extended by new code. Therefore, we have to ensure that the operations are added to the graph only when the function is called for the first time. This is basically lazy-loading.

```
class Model:
def __init__(self, data, target):
self.data = data
self.target = target
self._prediction = None
self._optimize = None
self._error = None
@property
def prediction(self):
if not self._prediction:
data_size = int(self.data.get_shape()[1])
target_size = int(self.target.get_shape()[1])
weight = tf.Variable(tf.truncated_normal([data_size, target_size]))
bias = tf.Variable(tf.constant(0.1, shape=[target_size]))
incoming = tf.matmul(self.data, weight) + bias
self._prediction = tf.nn.softmax(incoming)
return self._prediction
@property
def optimize(self):
if not self._optimize:
cross_entropy = -tf.reduce_sum(self.target, tf.log(self.prediction))
optimizer = tf.train.RMSPropOptimizer(0.03)
self._optimize = optimizer.minimize(cross_entropy)
return self._optimize
@property
def error(self):
if not self._error:
mistakes = tf.not_equal(
tf.argmax(self.target, 1), tf.argmax(self.prediction, 1))
self._error = tf.reduce_mean(tf.cast(mistakes, tf.float32))
return self._error
```

This is much better than the first example. Your code now is structured into functions that you can focus on individually. However, the code is still a bit bloated due to the lazy-loading logic. Let’s see how we can improve on that.

## Lazy Property Decorator

Python is a quite flexible language. So let me show you how to strip out the
redundant code from the last example. We will use a decorator that behaves like
`@property`

but only evaluates the function once. It stores the result in a
member named after the decorated function (prepended with a prefix) and returns
this value on any subsequent calls. If you haven’t used custom decorators yet,
you might also want to take a look at this guide.

```
import functools
def lazy_property(function):
attribute = '_cache_' + function.__name__
@property
@functools.wraps(function)
def decorator(self):
if not hasattr(self, attribute):
setattr(self, attribute, function(self))
return getattr(self, attribute)
return decorator
```

Using this decorator, our example simplifies to the code below.

```
class Model:
def __init__(self, data, target):
self.data = data
self.target = target
self.prediction
self.optimize
self.error
@lazy_property
def prediction(self):
data_size = int(self.data.get_shape()[1])
target_size = int(self.target.get_shape()[1])
weight = tf.Variable(tf.truncated_normal([data_size, target_size]))
bias = tf.Variable(tf.constant(0.1, shape=[target_size]))
incoming = tf.matmul(self.data, weight) + bias
return tf.nn.softmax(incoming)
@lazy_property
def optimize(self):
cross_entropy = -tf.reduce_sum(self.target, tf.log(self.prediction))
optimizer = tf.train.RMSPropOptimizer(0.03)
return optimizer.minimize(cross_entropy)
@lazy_property
def error(self):
mistakes = tf.not_equal(
tf.argmax(self.target, 1), tf.argmax(self.prediction, 1))
return tf.reduce_mean(tf.cast(mistakes, tf.float32))
```

Note that we mention the properties in the constructor. This way the full graph
is ensured to be defined by the time we run `tf.initialize_variables()`

.

## Organizing the Graph with Scopes

We now have a clean way to define model in code, but the resulting computations
graphs are still crowded. If you would visualize the graph,
it would contain a lot of interconnected small nodes. The solution would be to
wrap the content of each function by a `with tf.name_scope('name')`

or ```
with
tf.variable_scope('name')
```

. Nodes would then be grouped together in the graph.
But we adjust our previous decorator to do that automatically:

```
import functools
def define_scope(function):
attribute = '_cache_' + function.__name__
@property
@functools.wraps(function)
def decorator(self):
if not hasattr(self, attribute):
with tf.variable_scope(function.__name):
setattr(self, attribute, function(self))
return getattr(self, attribute)
return decorator
```

I gave the decorator a new name since it has functionality specific to TensorFlow in addition to the lazy caching. Other than that, the model looks identical to the previous one.

We could go even further an enable the `@define_scope`

decorator to forward
arguments to the `tf.variable_scope()`

, for example to define a default
initializer for the scope. If you are interested in this check out the full
example I put together.

We can now define models in a structured and compact way that result in organized computation graphs. This works well for me. If you have any suggestions or questions, feel free to use the comment section.

**Updated 2018-06-26:** Added link to my post on prototyping in
TensorFlow, that introduces an improved version of the
decorator idea introduced here.

You can use this post under the open CC BY-SA 3.0 license and cite it as:

@misc{hafner2016scopedecorator, author = {Hafner, Danijar}, title = {Structuring Your TensorFlow Models}, year = {2016}, howpublished = {Blog post}, url = {https://danijar.com/structuring-your-tensorflow-models/} }