Neural Network Programming - Deep Learning with PyTorch

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Training Loop Run Builder - Neural Network Experimentation Code

July 24, 2019 by

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Training Loop Run Builder - Neural Network Experimentation

Welcome to this neural network programming series. In this episode, we’ll code a RunBuilder class that will allow us to generate multiple runs with varying parameters.

Without further ado, let’s get started.

Using the RunBuilder Class

The purpose of this episode and the last couple of episodes of this series is to get ourselves into a position to be able to efficiently experiment with the training process that we’ve constructed. For this reason, we’re going to expand on something we touched on in the episode on hyperparameter experimentation. We’re going to make what we saw there a bit cleaner.

We’re going to build a class called RunBuilder. but, before we look at how to build the class. Let’s see what it will allow us to do. We’ll start with our imports.

from collections import OrderedDict
from collections import namedtuple
from itertools import product

We’re importing OrderedDict and namedtuple from collections and we’re importing a function called product from itertools. This product() function is the one we saw last time that computes a Cartesian product given multiple list inputs.

Alright. This is the RunBuilder class that will build sets of parameters that define our runs. We’ll see how it works after we see how to use it.

class RunBuilder():
    @staticmethod
    def get_runs(params):

        Run = namedtuple('Run', params.keys())

        runs = []
        for v in product(*params.values()):
            runs.append(Run(*v))

        return runs

The main thing to note about using this class is that it has a static method called get_runs(). This method will get the runs for us that it builds based on the parameters we pass in.

Let’s define some parameters now.

params = OrderedDict(
    lr = [.01, .001]
    ,batch_size = [1000, 10000]
)

Here, we’ve defined a set of parameters and values inside an dictionary. We have a set of learning rates and a set of batch sizes we want to try out. When we say try out, we mean that we want to do a training run for each learning rate and each batch size in the dictionary.

To get these runs, we just call the get_runs() function of the RunBuilder class, passing in the parameters we’d like to use.

> runs = RunBuilder.get_runs(params)
> runs

[
    Run(lr=0.01, batch_size=1000),
    Run(lr=0.01, batch_size=10000),
    Run(lr=0.001, batch_size=1000),
    Run(lr=0.001, batch_size=10000)
]

Great, we can see that the RunBuilder class has built and returned a list of four runs. Each of these runs has a learning rate and a batch size that defines the run.

We can access an individual run by indexing into the list like so:

> run = runs[0]
> run

Run(lr=0.01, batch_size=1000)

Notice the string representation of the run output. This string representation was automatically generated for us by the Run tuple class, and this string can be used to uniquely identify the run if we want to write out run statistics to disk for TensorBoard or any other visualization program.

Additionally, because the run is object is a tuple with named attributes, we can access the values using dot notation like so:

> print(run.lr, run.batch_size)

0.01 1000

Finally, since the list of runs is a Python iterable, we can iterate over the runs cleanly like so:

for run in runs:
    print(run, run.lr, run.batch_size)

Output:

Run(lr=0.01, batch_size=1000) 0.01 1000
Run(lr=0.01, batch_size=10000) 0.01 10000
Run(lr=0.001, batch_size=1000) 0.001 1000
Run(lr=0.001, batch_size=10000) 0.001 10000

All we have to do to add additional values is to add them to the original parameter list, and if we want to add an additional type of parameter, all we have to do is add it. The new parameter and its values will automatically become available to be consumed inside the run. The string output for the run also updates as well.

Two parameters:

params = OrderedDict(
    lr = [.01, .001]
    ,batch_size = [1000, 10000]
)

runs = RunBuilder.get_runs(params)
runs

Output:

[
    Run(lr=0.01, batch_size=1000),
    Run(lr=0.01, batch_size=10000),
    Run(lr=0.001, batch_size=1000),
    Run(lr=0.001, batch_size=10000)
]

Three parameters:

params = OrderedDict(
    lr = [.01, .001]
    ,batch_size = [1000, 10000]
    ,device = ["cuda", "cpu"]
)

runs = RunBuilder.get_runs(params)
runs

Output:

[
    Run(lr=0.01, batch_size=1000, device='cuda'),
    Run(lr=0.01, batch_size=1000, device='cpu'),
    Run(lr=0.01, batch_size=10000, device='cuda'),
    Run(lr=0.01, batch_size=10000, device='cpu'),
    Run(lr=0.001, batch_size=1000, device='cuda'),
    Run(lr=0.001, batch_size=1000, device='cpu'),
    Run(lr=0.001, batch_size=10000, device='cuda'),
    Run(lr=0.001, batch_size=10000, device='cpu')
]

This functionality will allow us to have greater control as we experiment with different values during training.

Let’s sees how to build this RunBuilder class.

Coding the RunBuilder Class

The first thing we need to have is a dictionary of parameters and values we’d like to try.

params = OrderedDict(
    lr = [.01, .001]
    ,batch_size = [1000, 10000]
)

Next, we get a list of keys from the dictionary.

> params.keys()
odict_keys(['lr', 'batch_size'])

Then, we get a list of values from the dictionary.

> params.values()
odict_values([[0.01, 0.001], [1000, 10000]])

Once we have both of these, we just make sure we understand both of them by inspecting their output. Once we do, we use these keys and values for what comes next. We’ll start with the keys.

Run = namedtuple('Run', params.keys())

This line creates a new tuple subclass called Run that has named fields. This Run class is used to encapsulate the data for each of our runs. The field names of this class are set by the list of names passed to the constructor. First, we are passing the class name. Then, we are passing the field names, and in our case, we are passing the list of keys from our dictionary.

Now that we have a class for our runs, we are ready to create some.

runs = []
for v in product(*params.values()):
    runs.append(Run(*v))

First we create a list called runs. Then, we use the product() function from itertools to create the Cartesian product using the values for each parameter inside our dictionary. This gives us a set of ordered pairs that define our runs. We iterate over these adding a run to the runs list for each one.

For each value in the Cartesian product we have an ordered tuples. The Cartesian product gives us every ordered pair so we have all possible order pairs of learning rates and batch sizes. When we pass the tuple to the Run constructor, we use the * operator to tell the constructor to accept the tuple values as arguments opposed to the tuple itself.

Finally, we wrap this code in our RunBuilder class.

class RunBuilder():
    @staticmethod
    def get_runs(params):

        Run = namedtuple('Run', params.keys())

        runs = []
        for v in product(*params.values()):
        runs.append(Run(*v))

        return runs

Since the get_runs() method is static, we can call it using the class itself. We don’t need an instance of the class.

Now, this allow us to update our training code in the following way:

Before:

for lr, batch_size, shuffle in product(*param_values):
    comment = f' batch_size={batch_size} lr={lr} shuffle={shuffle}'

    # Training process given the set of parameters

After:

for run in RunBuilder.get_runs(params):
    comment = f'-{run}'
    
    # Training process given the set of parameters

What is a Cartesian Product?

Do you know about the Cartesian product? Like many things in life, the Cartesian product is a mathematical concept. The Cartesian product is a binary operation. The operation takes two sets as arguments and returns a third set as an output. Let's look at a general mathematical example.

Suppose that \(X\) is a set.

Suppose that \(Y\) is a set.

The Cartesian product between two sets is denoted as, \(X \times Y\). The Cartesian product between the sets \(X\) and the set \(Y\) is defined to be the set of all ordered pairs \((x,y)\) such that little \(x \in X\) and \(y \in Y\). This can be expressed in the following way:

\[X \times Y = \bigg\{ (x,y) \mid x \in X \text{ and } y \in Y \bigg\} \]

This way of expressing the output of the Cartesian product is called set builder notation. It is cool. So \(X \times Y\) is the set of all ordered pairs \((x,y)\) such that little \(x \in X\) and \(y \in Y\).

To compute \(X \times Y\) we do the following:

For every \(x \in X\) and for every \(y \in Y\), we collect the corresponding pair \((x,y)\). The resulting collection gives us the set of all ordered pairs little \((x,y)\) such that \(x \in X\) and \(y \in Y\).

Here is a concrete example expressed in Python:

X = {1,2,3}
Y = {1,2,3}

{ (x,y) for x in X for y in Y }

Output:

{(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)}

Notice how powerful the mathematical code is. It covers all cases. Maybe you noticed that this can be achieved using for-loop iteration like so:

X = {1,2,3}
Y = {1,2,3}
cartesian_product = set()
for x in X:
    for y in Y:
        cartesian_product.add((x,y))
cartesian_product

Output:

{(1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (3, 3)}

Wrapping Up

Alright, now we know how this works and we can use it going forward.

Description

Welcome to this neural network programming series. In this episode, we’ll code a training loop run builder class that will allow us to generate multiple runs with varying parameters. This will aid us with experimentation of the neural network training process. At the end of the video, we cover the Cartesian product. 💥🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎💥 👀 OUR VLOG: 🔗 https://www.youtube.com/channel/UC9cBIteC3u7Ee6bzeOcl_Og 👉 Check out the blog post and other resources for this video: 🔗 https://deeplizard.com/learn/video/NSKghk0pcco 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 https://www.patreon.com/posts/27743395 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 https://deeplizard.com/hivemind 🤜 Support collective intelligence, create a quiz question for this video: 🔗 https://deeplizard.com/create-quiz-question 🚀 Boost collective intelligence by sharing this video on social media! ❤️🦎 Special thanks to the following polymaths of the deeplizard hivemind: yasser Prash 👀 Follow deeplizard: Our vlog: https://www.youtube.com/channel/UC9cBIteC3u7Ee6bzeOcl_Og Twitter: https://twitter.com/deeplizard Facebook: https://www.facebook.com/Deeplizard-145413762948316 Patreon: https://www.patreon.com/deeplizard YouTube: https://www.youtube.com/deeplizard Instagram: https://www.instagram.com/deeplizard/ 🎓 Deep Learning with deeplizard: Fundamental Concepts - https://deeplizard.com/learn/video/gZmobeGL0Yg Beginner Code - https://deeplizard.com/learn/video/RznKVRTFkBY Advanced Code - https://deeplizard.com/learn/video/v5cngxo4mIg Advanced Deep RL - https://deeplizard.com/learn/video/nyjbcRQ-uQ8 🎓 Other Courses: Data Science - https://deeplizard.com/learn/video/d11chG7Z-xk Trading - https://deeplizard.com/learn/video/ZpfCK_uHL9Y 🛒 Check out products deeplizard recommends on Amazon: 🔗 https://www.amazon.com/shop/deeplizard 📕 Get a FREE 30-day Audible trial and 2 FREE audio books using deeplizard’s link: 🔗 https://amzn.to/2yoqWRn 🎵 deeplizard uses music by Kevin MacLeod 🔗 https://www.youtube.com/channel/UCSZXFhRIx6b0dFX3xS8L1yQ 🔗 http://incompetech.com/ ❤️ Please use the knowledge gained from deeplizard content for good, not evil.