Building a Neural Network with PyTorch in 15 Minutes | Coding Challenge

Summary

In this fast-paced video, the host challenges himself to code a PyTorch deep learning model within 15 minutes without referring to any documentation or pre-existing code. He introduces PyTorch, an open-source machine learning library, and proceeds to build a neural network for the MNIST dataset. The video covers model creation, training, and prediction, showcasing the host's ability to meet the time constraint and successfully train a model to recognize handwritten digits.

Takeaways

• 🕒 The video is a challenge to code a PyTorch deep learning model within 15 minutes.
• 🚫 The presenter sets a rule to avoid looking at documentation or using pre-existing code, with a 1-minute penalty for violations.
• 🎁 There's a stake involved: if the time limit is not met, the presenter will give a $50 Amazon gift card to the viewers.
• 🛠️ PyTorch is an open-source deep learning framework developed by Facebook, used for building deep learning models quickly.
• 💾 The video demonstrates how to import necessary dependencies from PyTorch and TorchVision for model building.
• 📈 The presenter guides through creating a neural network class, setting up the model architecture with convolutional layers, and defining a forward method.
• 🔄 The training process is explained, including data loading, setting up the optimizer, defining a loss function, and writing a training loop.
• 💾 The video includes a practical example of training a model on the MNIST dataset, which consists of images of handwritten digits.
• 🏁 The presenter successfully trains the model and saves the weights within the time limit, demonstrating the efficiency of PyTorch.
• 🔮 After training, the video shows how to load the saved model and make predictions on new data, showcasing the model's ability to classify images.

Q & A

What is the main challenge the video aims to address?

The video aims to challenge the presenter to code a PyTorch deep learning model within a strict time limit of 15 minutes without referring to any documentation or pre-existing code.

What are the consequences if the presenter fails to meet the 15-minute time limit?

If the presenter fails to meet the time limit, they will give away a $50 Amazon gift card to the viewers.

What is the purpose of using the MNIST dataset in the video?

The MNIST dataset is used for training the deep neural network model as it is an image classification dataset with 10 classes (0-9), which is suitable for demonstrating the capabilities of PyTorch in a short timeframe.

What is the role of the 'transforms' module from 'torchvision' in the script?

The 'transforms' module is used to convert images into tensors, which are the data format that PyTorch works with for building and training neural networks.

What is the significance of the 'nn.Sequential' API used in the video?

The 'nn.Sequential' API in PyTorch is used to create a sequence of neural network layers, making it easier to stack layers and build the model architecture.

How does the video demonstrate the process of creating a convolutional neural network (CNN)?

The video demonstrates creating a CNN by stacking multiple 'nn.Conv2d' layers with specific parameters, such as the number of input channels, output channels, and kernel size.

What is the purpose of the 'nn.Flatten' layer in the neural network architecture presented?

The 'nn.Flatten' layer is used to flatten the output of the convolutional layers so that it can be fed into the fully connected 'nn.Linear' layers for classification.

How is the training process of the neural network described in the video?

The training process involves iterating over batches of data, making predictions, calculating loss using 'nn.CrossEntropyLoss', and applying backpropagation to update the model's weights using the 'optim.Adam' optimizer.

What is the significance of the 'cuda' keyword in the script?

The 'cuda' keyword is used to specify that the model and data should be sent to the GPU for faster computation. If a GPU is not available, it defaults to using the CPU.

How does the presenter ensure the model's performance is evaluated during the training?

The presenter prints out the loss for each batch after each epoch to monitor the model's performance and ensure it is learning effectively.

What is the final step demonstrated in the video after training the model?

The final step demonstrated is saving the trained model's state to a file using 'torch.save', which allows the model to be reloaded and used for making predictions without retraining.

Outlines

00:00

🕒 Introduction to a PyTorch Deep Learning Challenge

The speaker introduces a challenge to code a PyTorch deep learning model within a 15-minute time limit. They outline the rules: no prior code or documentation can be referenced, and using copilot is not allowed due to an expired subscription. A one-minute penalty is applied for breaking these rules. If the time limit is not met, a 50 Amazon gift card is promised to the audience. The speaker briefly explains PyTorch as an open-source deep learning framework developed by Facebook, used for building deep learning models quickly and efficiently.

05:02

💻 Setting Up the PyTorch Neural Network

The speaker begins coding by creating a Python file and importing necessary dependencies from PyTorch, including the neural network class, Adam optimizer, and data loader. They also import datasets and transforms from torchvision to handle image data. The speaker then proceeds to download the MNIST dataset, a collection of images for handwritten digit classification, and sets up data transformations to convert images into tensors. A data loader is created to batch the data into sets of 32 images. The speaker then defines an 'image classifier' class, inheriting from PyTorch's neural network module, and sets up the model architecture using convolutional layers and a linear layer for classification.

10:03

🚀 Rapid Development of the Image Classifier

The speaker continues to develop the 'image classifier' by defining the model's layers, including convolutional layers with varying numbers of filters and kernel sizes. They calculate the output size after applying the convolutional layers and adjust the input size for the linear layer accordingly. The speaker also sets up the forward method for the model, which passes input data through the defined layers. They create an instance of the classifier, move it to a GPU if available, and define the Adam optimizer with a specified learning rate. A cross-entropy loss function is also instantiated. The speaker then outlines the training loop, which includes unpacking data batches, sending data to the GPU, making predictions, calculating loss, and performing backpropagation.

15:05

🏁 Completing the Training and Saving the Model

The speaker wraps up the training process by running the training loop for 10 epochs, printing the loss for each batch, and saving the trained model weights to a file. They express excitement as the training completes without errors and within the time limit. The speaker then demonstrates how to load the saved model and make predictions on new images, showcasing the model's ability to classify handwritten digits correctly. They conclude by thanking the audience and encouraging feedback, promising to share the code on GitHub for those interested in trying the challenge themselves.

20:07

🎉 Conclusion and Engagement

In the final paragraph, the speaker invites viewers to like, subscribe, and turn on notifications for more content. They express gratitude for the viewers' engagement and ask for feedback or suggestions for future challenges. The speaker also inquires if the audience enjoyed the PyTorch deep learning challenge and if they would like to see more of this type of content. The video ends with a positive note, encouraging viewers to participate and learn from the shared experience.

Keywords

💡PyTorch

PyTorch is an open-source machine learning library for Python, used for applications such as computer vision and natural language processing. In the video, PyTorch is the primary framework utilized to build a deep learning model. The script mentions installing PyTorch and using its various modules to create, train, and save a neural network, highlighting its versatility and efficiency in deep learning tasks.

💡Deep Learning

Deep learning is a subset of machine learning that focuses on artificial neural networks with many layers, allowing the model to learn complex patterns in data. The video's theme revolves around constructing a deep learning model using PyTorch, showcasing the process from data loading to model training and prediction, which is central to the script's educational purpose.

💡Neural Network

A neural network is a series of algorithms modeled loosely after the human brain that are designed to recognize patterns. In the script, the creator builds a 'deep neural network' using PyTorch, which involves stacking layers of neural network classes to process and learn from data, exemplified by the 'image classifier' class defined in the video.

💡Convolutional Neural Network (CNN)

A CNN is a type of deep learning model commonly used for image recognition tasks. The script describes adding convolutional layers to the neural network, which are essential for feature extraction in image data. The use of 'nn.conv2d' in the script to create these layers illustrates the application of CNNs in the model.

💡Optimizer

In machine learning, an optimizer is an algorithm that helps in adjusting the model's parameters to minimize the loss function. The script mentions using the 'Adam' optimizer from PyTorch, which is a method to update the model's weights during training, as indicated by the line 'from torch.optim import adam'.

💡Loss Function

A loss function is a measure of how far the model's predictions are from the actual outcome. The video script includes the use of 'nn.cross_entropy_loss' as the loss function, which is crucial for training the neural network by providing a way to quantify and minimize the error during the learning process.

💡Backpropagation

Backpropagation is a method used to calculate the gradient of the loss function with respect to the model's parameters, which is essential for learning. The script refers to applying backpropagation by using 'loss.backward()' to compute the gradient and then updating the model's weights.

💡Epoch

An epoch in machine learning refers to a full pass through the entire dataset. The script mentions training the model for '10 epochs', which means the entire dataset is used for training the model 10 times, a common practice to improve the model's performance.

💡Batch

A batch is a subset of the dataset used in training. The video script describes batching the data into sets of 32 images, which is a common practice to manage memory usage and computational efficiency during training, as shown by the 'batch_size=32' parameter in the DataLoader.

💡Tensor

In the context of PyTorch, a tensor is a generalization of vectors and matrices and is the primary data structure for neural networks. The script includes converting images into tensors using 'transforms.ToTensor()', which is necessary because PyTorch models operate on tensors rather than raw image data.

💡GPU

A GPU (Graphics Processing Unit) is used for accelerating the computation in deep learning tasks. The script mentions sending data to the GPU using 'cuda', which is important for speeding up the training process by leveraging the parallel processing capabilities of GPUs, as indicated by 'x, y = x.cuda(), y.cuda()'.

Highlights

Introduction to a challenge of coding a PyTorch deep learning model in 15 minutes.

Setting a 15-minute time limit for building the model without referencing documentation or pre-existing code.

A one-minute penalty is applied for using documentation or pre-existing code.

The presenter is not using Copilot due to an expired subscription.

Failure to meet the time limit results in a 50 Amazon gift card giveaway.

PyTorch is an open-source deep learning framework developed by Facebook.

Importing necessary dependencies for building the neural network.

Downloading the MNIST dataset for image classification.

Creating a neural network class called 'image classifier'.

Stacking layers for the model using PyTorch's sequential API.

Using convolutional neural network layers with specific parameters.

Adjusting for the reduction in image size after applying convolutional layers.

Creating a linear layer to output predictions for the 10 classes of the MNIST dataset.

Instantiating the neural network, optimizer, and loss function.

Setting up a training loop with backpropagation.

Printing out the loss for each batch during training.

Saving the trained model to the environment.

Successfully training the model and making predictions within the time limit.

Loading the trained model and making predictions on new images.

The presenter expresses happiness and satisfaction with the successful training and prediction.

Encouragement for viewers to try the challenge and provide feedback.

Transcripts

so i looked at the pie torch

documentation for the very first time

yesterday and in this video we're going

to be trying to code a pie torch deep

learning model in 15 minutes

[Music]

this is gonna be interesting what's

happening guys welcome back to another

episode of

code that where i try to build stuff in

a ridiculously short time frame in this

episode as i mentioned we are going to

be building a deep neural network using

pie torch

very quickly so what are the rules well

first and foremost we're going to have a

time limit as per usual the time limit

is going to be 15 minutes now we need

some constraints in this particular case

i'm not going to be allowed to look at

any documentation or pre-existing code

also i'm not gonna be able to use

copilot to be honest my subscriptions

expired anyway so that doesn't really

matter if i do go and use existing code

or documentation it is a one minute time

penalty off our total time limit we're

also going to need

some steaks if i don't make that time

limit it is going to be a 50 amazon gift

card to you guys now the last two

episodes we haven't quite hit that time

limit so who knows we're gonna have to

try to make a bit of an effort in this

one before we jump right into it we

should probably take a little bit of a

look as to what is pie torch pie torch

is an open source deep learning

framework which is predominantly used in

python it's developed by the team at

facebook and it accelerates the speed at

which you can go and build deep learning

models it is used quite heavily in

state-of-the-art models as well as in

deep learning research ready to do it

let's get to it

alrighty guys let's get into it 15

minutes on the clock

let's go

okay so the first thing that we need to

do is create a new python file so i'm

just going to call it uh

torch and then dot pi

okay then we need to import a bunch of

dependencies so i've already gone

and installed pi torch on this

particular machine but we need to import

some pie torch dependencies so let's go

for it so uh from

i torch actually from torch well i'm

already screwing up from torch import

and then

from

porch.optimum import adam

from torch.utils.data

import what is it uh data loader

all right cool so those are so torch so

this most of our neural network classes

are going to be inside of the neural

network class atom is going to be our

optimizer and data loader is needed to

load a data set from pytorch then we

need a bunch of dependencies from torch

vision so from torch vision

watch oh my gosh

everything torch vision uh import data

sets and then from torch

vision so we need this next dependency

to convert our images into tensors which

are going to work with pytorch so from

torch vision dot

transforms

import uh what do we need

through tensor

boom okay let's close this for now all

right so those are our dependencies

imported all we now need to do is import

our data set so we don't have any data

sets in our folders but some images that

have already gone and downloaded which

we'll be able to test out later on so

let's download our data set so we're

going to use the mnist data set which is

an image classification data set there

are 10 classes so 0 to 9 so we're going

to be able to download that using the

data sets

class over here so train equals

datasets.mnist

and then we need to specify where we

want to download it so root equals and

then the folder so data

and then we're going to specify download

equals true because we need to download

it and then we also need to download we

want the train partition

set that equal to true

and then we need to specify any data

transformations that we want to go ahead

and apply so we're going to specify

transform

and we want to transform it to a tensor

and then we are going to create our data

set so data set equals

data loader so we need to pass our train

partition so this over here to this data

load us to train and then specify how we

want to batch up our data set so we're

going to

convert it into batches of 32 images

cool so this is

that's right two comments so import

dependencies

get data

all right now we actually need to create

our neural network class so

the first thing that we're going to do

is create a class and we're going to

call it image classifier

and we are going to subclass it from the

neural network.module class from pi

torch then we need two functions or two

methods so definite

and we also need a forward

a forward method this is akin to

the call method inside of

tensorflow

all right so we've initialized our first

method so now what we need to do is

create a model so this is where we

actually stack all our layers so we are

going to call self.models we'll create a

new attribute equals nn.sequential so

we're going to be using the sequential

api from pytorch so it's coming from up

here

and then what we want to do is we're

going to use convolutional neural

network layers so uh so nn.conf 2d

and then we need to specify what this is

intense all right so the input channel

is going to be one because our images

are black and white then we want 32

filters of shape 3x3 or 32 kernels

all right so that's our one

convolutional neural network layer then

we need some activations

to handle non-lyrics yeah

non-linearities

and then uh what are we doing so we're

going to add in a bunch more

convolutional neural network layers so

we now need 32 as our input channels and

then we're going to output 64.

and then let's do one more set of

convolutional neural network layer or

conv conv layers and then we are going

to be taking 64 from up here

now without getting too deep into the

math by applying each one of these

layers with these specific parameters

we're going to be shaving off two pixels

off the height and width of each image

so we need to adjust for that in our

output when we apply a linear layer so

first up we need to flatten these layers

and then we're going to apply uh

creating linear layers so nn dot linear

and our shape so we need to pass through

the input shape into the linear layer so

it's going to be 64 channels because

that's the final channel output from our

last convolutional neural network layer

multiplied by our images from mnist are

in the shape of one comma 28 comma 28.

so if we're applying

three convolutional neural network

layers with these specific parameters

this specific stride and whatnot we're

gonna be shaving off two pixels each

time so we're effectively going to be

shaving off two

two and then two so it's minus six so

it's going to be 68 multiplied or minus

6

sorry 28

minus 6 then 28 minus 6 over here and

then our output shape needs to be the

number of classes so out we've got 10

different classes so 0 to 9

classes 0 to 9 so we need 10 outputs

beautiful okay i think that's our neural

network layer let me just quickly take a

look at that um all right wait we need

to subclass this model

so we are going to call super

dot

init

and that looks good and then our forward

function or method is pretty simple so

it's just going to

take in our current instance and we're

going to take in our x data and we are

going to return

self.model and then we're going to pass

through our x value into that all right

how we're looking how are we looking for

time all right six minutes of we need to

accelerate okay so that's our image

classifier class

uh neural network

now what we need to do is set up a a

couple of things so we need to create an

instance

of the neural network

uh loss and our optimizer

okay so instance of our neural network

so we're going to call it classifiers to

clf equals image classifier

and then we need to say that we want to

use our gpu so i've installed the gpu

edition of torch on this so it's the

kudo equivalent so we're going to send

it to that device so we can type in dot

2

and then say cuda if you don't have a

gpu then you can set this to cpu

really need to stop talking and stop

writing okay so that's our classifier

cool i think that is good then we need

to instantiate our optimizer

and we uh imported our optimizer from up

here i don't know why this turtle thing

imports so our optimizer is atom so

we're going to be using atom

and then to that we need to pass our

classifier and the parameters

and then we need to specify our learning

rate learning rate adjusts how fast or

slow our neural network is going to

learn so we're going to specify lr

equals 1 e negative 3.

cool all right so that's our optimizer

then we need to create a loss function

so loss function equals

uh nn dot cross entropy

loss

beautiful okay so that's our

neural network done that's our optimizer

done that's our loss function done all

right now what we need is a training

function we are we're gonna make this

seven minutes oh yeah we're gonna make

this all right um

okay so training

uh flow so we're just gonna do it inside

of like our typical function or like

python function here

that is still recording cool all right

um

so if

breathe breathe

remember your pressure point

remember your pressure points okay if

name

equals equals

main i finally want to make this

hit the code that time limit all right

so if name let's get rid of that equals

main then what do we want to do so we

want to run our deep learning training

for a number of epochs that's

effectively going through all of our

batches so

if actually four epoch in range

range 10

so this means we're going to be training

for 10 epochs

train for 10 epochs

then for batch in

data set so we're going to be looping

through

this over here

then what we need to do is unpack the

data that we're getting there so x and y

are going to equal batch so we're going

to unpack that data and then we need to

send the x and y values to our gpu again

so it's going to be x comma y equals

x dot 2.

cuda again if you've got a cpu only it's

going to be true cpu

y dot 2

cuda

so this is a little different compared

to when you're working in tensorflow um

all right then we need to make a

prediction so y hat equals

clf and then pass through x

to generate a prediction yes that makes

sense then we need to calculate

loss so this is how you typically go

through and actually build or build a

flow input intense flow you just

effectively go compile and then dot fit

all right so we need to calculate our

loss so our loss is going to be our loss

function

plus function and then we are passing

through y hat and then y so that

calculates our loss and then we actually

need to go and apply backdrop apply

back prop

so first what we need to do is we need

to zero out any existing gradients so we

go opt.zero grad

then we need to take or we need to

calculate those gradients so loss got

backward

and then we need to go and take a step

and effectively uh

apply gradient descent so opt dot

visit op dot step

cool i think that's okay guys all right

so then what we want to do is we want to

print out our loss for every batch so

print

we are getting close print

f and then we are going to

the epoch

so i'm just going to print out the epoch

so we're going to print out what epoch

we're currently up to and then we're

just going to print out loss

loss is

and then we'll push loss dot item

here

then close that close that all right

that looks good

i think we're okay so what are we doing

so let's just quickly check so we've got

our dependencies

utensil that's fine data loader

we've gone and subclassed our model yes

that's fine

we've got our forward

method

instantiated all of this stuff

gone and sent our data to the gpu gone

and applied back prop we're then going

to print out our epoch print out our

lost oh last thing so we want to save

down this model to our

environment right so we actually want to

go and save it here so let's go and save

that so um with open we're going to call

it uh

model state

dot pt

and then we're going to write binary

as f and then we need to oh actually we

need to import some more stuff so we

need to download we need to import save

and then load from torch

so we are going to

save

uh

what's the format now

it's model it'll be clf dot state it

and then we are saving it as f i think

that's right

okay let's give it a crack all right so

then we can run to run this we're just

going to run python and then torch n.pie

so python

and if we successfully kick off training

we'll pause the timer because come on

guys you got to give me that at least

all right uh so let's run this

[Music]

okay no errors so far this is looking

promising

all right so it's downloading the data

set

so we should have our data set popping

up here you can see inside of data

if we see a lost metric i'll pause it

come on

this is killing me

yes all right

guys

we started trading

oh man

yes i think we're going to finally make

this first coat that so you can see

fewer that we are successfully printing

out at epoch loss so epoch zero loss is

0.02 then we're going to 0.01 then

0.0002

and once we finish training you should

effectively see that we get our

porch weight saved

the intensity

guys

you can see that our neural network is

currently training right

oh i'm so happy and then we'll make a

prediction i'm i'm counting this as a

dub we hit it in what

30 less than 13 minutes

so what 12 minutes and 54 seconds that's

got to be a new record

got all the the gangster deep learning

practitioners out there gonna be like

nick you skipped over a ton of details i

was like come on i had 15 minutes anyway

we are chugging along

so i effectively once our model gets to

epoch

what is that epoch 9 we should

have our weight saved out because we

went and wrote this

and i'm going to share all this code as

usual code should be in the description

and you can give this a crack yourself

all right cool we didn't get any errors

so boom

we've got to play some angel music in

here

oh guys your boy is happy

so that is successfully how to go and

build a neural network with pytorch so

we did it

i'm happy now so if we wanted to we

could actually go and load up

this um neural network and make a

prediction which is why i've got these

images here so

should we try to get this done in two

minutes

i'm a little nervous all right let's do

it so we've got two minutes left on the

clock

let's try to get the predictions done

okay so we've successfully loaded

downloaded or trained our model so now

what we can do let me just minimize this

so now what we can do is comment this

stuff out so we don't need to go and

train again

and then what we'll do is we'll load up

so we've got a classifier there so we'll

then go with

open

and then we want to load up this so

model state dot pt

so with open model state dot pt

read binary

as f and then we are going to go load

f and then we want to go clf dot load

i think it's load state dick yep cool so

that's going to load the weights into

our classifier then in order to make

predictions we need to import

torch

torch up here and we also need to import

uh pillow to load our image so then what

we can go ahead and do

is load in our image so image equals

kill i'll actually wait at some point so

from

kill import

image

so then we're going to import our image

to image

dot open

and we're just going to open up image

one right now which is a number two so

uh image underscore one

dot jpg

and then what do we need to do so we

need to convert that to a tensor so

we're going to go uh image answer

equals

true tensor

pass through our image and then we need

to unsqueeze it because we want to pass

through a single sample so we're gonna

unsqueeze and then send it to our gpu so

cuda

and then we should be able to print

classifier and then image tensor

i think that might be okay

all right let's run it

and this should print out our prediction

oh we need to uh so torch dot ugg max

i'm gonna take this as a win guys

right that's the timer up though

so if i now go and run this

boom it's predicting two

how good is that

i'm gonna take that as dub guys because

we've managed to train that model and

make predictions so if you take a look

now right i can then go and load up

image two

all i need to do is change the image

here

and it should print out the class

the tens of zero so image if we go and

open up image two

boom that's tense to zero so it's

predicting the the class is zero

we go and run it for let's go and do it

for image three

which is a number nine so if we go and

pass through image three here

boom number nine

thank you so much for tuning in guys

hopefully you've enjoyed this speed

running building pie torch neural

networks code's going to be in the

description via my github if you guys

want to take a crack i'll catch you in

the next one peace thanks so much for

tuning in guys hopefully you enjoyed

this video if you did be sure to give it

a big thumbs up hit subscribe and tick

that bell it really does help and i

really do appreciate you checking out

this video if there's any feedback any

challenges or anything you'd like to

hear a little bit more about do let me

know in the comments below thanks again

for tuning in guys also let me know if

you like this challenge and uh if we

could do better next time thanks again

for tuning in peace

[Music]