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 inpacient of you, here is the link to a working example gist.

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__, *args, **kwargs):
                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.