Tutorial: Linear Regression
Flux is a pure Julia ML stack that allows you to build predictive models. Here are the steps for a typical Flux program:
 Provide training and test data
 Build a model with configurable parameters to make predictions
 Iteratively train the model by tweaking the parameters to improve predictions
 Verify your model
Under the hood, Flux uses a technique called automatic differentiation to take gradients that help improve predictions. Flux is also fully written in Julia so you can easily replace any layer of Flux with your own code to improve your understanding or satisfy special requirements.
The following page contains a stepbystep walkthrough of the linear regression algorithm in Julia
using Flux
! We will start by creating a simple linear regression model for dummy data and then move on to a real dataset. The first part would involve writing some parts of the model on our own, which will later be replaced by Flux
.
Let us start by building a simple linear regression model. This model would be trained on the data points of the form (xโ, yโ), (xโ, yโ), ... , (xโ, yโ)
. In the real world, these x
s can have multiple features, and the y
s denote a label. In our example, each x
has a single feature; hence, our data would have n
data points, each point mapping a single feature to a single label.
Importing the required Julia
packages 
julia> using Flux, Plots
Generating a dataset
The data usually comes from the real world, which we will be exploring in the last part of this tutorial, but we don't want to jump straight to the relatively harder part. Here we will generate the x
s of our data points and map them to the respective y
s using a simple function. Remember, here each x
is equivalent to a feature, and each y
is the corresponding label. Combining all the x
s and y
s would create the complete dataset.
julia> x = hcat(collect(Float32, 3:0.1:3)...)
1ร61 Matrix{Float32}:
3.0 2.9 2.8 2.7 2.6 2.5 โฆ 2.4 2.5 2.6 2.7 2.8 2.9 3.0
The hcat
call generates a Matrix
with numbers ranging from 3.0
to 3.0
with a gap of 0.1
between them. Each column of this matrix holds a single x
, a total of 61 x
s. The next step would be to generate the corresponding labels or the y
s.
julia> f(x) = @. 3x + 2;
julia> y = f(x)
1ร61 Matrix{Float32}:
7.0 6.7 6.4 6.1 5.8 5.5 โฆ 9.5 9.8 10.1 10.4 10.7 11.0
The function f
maps each x
to a y
, and as x
is a Matrix
, the expression broadcasts the scalar values using @.
macro. Our data points are ready, but they are too perfect. In a realworld scenario, we will not have an f
function to generate y
values, but instead, the labels would be manually added.
julia> x = x .* reshape(rand(Float32, 61), (1, 61));
Visualizing the final data 
julia> plot(vec(x), vec(y), lw = 3, seriestype = :scatter, label = "", title = "Generated data", xlabel = "x", ylabel= "y");
The data looks random enough now! The x
and y
values are still somewhat correlated; hence, the linear regression algorithm should work fine on our dataset.
We can now proceed ahead and build a model for our dataset!
Building a model
A linear regression model is defined mathematically as 
\[model(W, b, x) = Wx + b\]
where W
is the weight matrix and b
is the bias. For our case, the weight matrix (W
) would constitute only a single element, as we have only a single feature. We can define our model in Julia
using the exact same notation!
julia> custom_model(W, b, x) = @. W*x + b
custom_model (generic function with 1 method)
The @.
macro allows you to perform the calculations by broadcasting the scalar quantities (for example  the bias).
The next step would be to initialize the model parameters, which are the weight and the bias. There are a lot of initialization techniques available for different machine learning models, but for the sake of this example, let's pull out the weight from a uniform distribution and initialize the bias as 0
.
julia> W = rand(Float32, 1, 1)
1ร1 Matrix{Float32}:
0.99285793
julia> b = [0.0f0]
1element Vector{Float32}:
0.0
Time to test if our model works!
julia> custom_model(W, b, x) > size
(1, 61)
julia> custom_model(W, b, x)[1], y[1]
(1.6116865f0, 7.0f0)
It does! But the predictions are way off. We need to train the model to improve the predictions, but before training the model we need to define the loss function. The loss function would ideally output a quantity that we will try to minimize during the entire training process. Here we will use the mean sum squared error loss function.
julia> function custom_loss(W, b, x, y)
ลท = custom_model(W, b, x)
sum((y . ลท).^2) / length(x)
end;
julia> custom_loss(W, b, x, y)
23.772217f0
Calling the loss function on our x
s and y
s shows how far our predictions (ลท
) are from the real labels. More precisely, it calculates the sum of the squares of residuals and divides it by the total number of data points.
We have successfully defined our model and the loss function, but surprisingly, we haven't used Flux
anywhere till now. Let's see how we can write the same code using Flux
.
julia> flux_model = Dense(1 => 1)
Dense(1 => 1) # 2 parameters
A Dense(1 => 1)
layer denotes a layer of one neuron with one input (one feature) and one output. This layer is exactly same as the mathematical model defined by us above! Under the hood, Flux
too calculates the output using the same expression! But, we don't have to initialize the parameters ourselves this time, instead Flux
does it for us.
julia> flux_model.weight, flux_model.bias
(Float32[1.1412252], Float32[0.0])
Now we can check if our model is acting right. We can pass the complete data in one go, with each x
having exactly one feature (one input) 
julia> flux_model(x) > size
(1, 61)
julia> flux_model(x)[1], y[1]
(1.8525281f0, 7.0f0)
It is! The next step would be defining the loss function using Flux
's functions 
julia> function flux_loss(flux_model, x, y)
ลท = flux_model(x)
Flux.mse(ลท, y)
end;
julia> flux_loss(flux_model, x, y)
22.74856f0
Everything works as before! It almost feels like Flux
provides us with smart wrappers for the functions we could have written on our own. Now, as the last step of this section, let's see how different the flux_model
is from our custom_model
. A good way to go about this would be to fix the parameters of both models to be the same. Let's change the parameters of our custom_model
to match that of the flux_model

julia> W = Float32[1.1412252]
1element Vector{Float32}:
1.1412252
To check how both the models are performing on the data, let's find out the losses using the loss
and flux_loss
functions 
julia> custom_loss(W, b, x, y), flux_loss(flux_model, x, y)
(22.74856f0, 22.74856f0)
The losses are identical! This means that our model
and the flux_model
are identical on some level, and the loss functions are completely identical! The difference in models would be that Flux
's Dense
layer supports many other arguments that can be used to customize the layer further. But, for this tutorial, let us stick to our simple custom_model
.
Training the model
Let's train our model using the classic Gradient Descent algorithm. According to the gradient descent algorithm, the weights and biases should be iteratively updated using the following mathematical equations 
\[\begin{aligned} W &= W  \eta * \frac{dL}{dW} \\ b &= b  \eta * \frac{dL}{db} \end{aligned}\]
Here, W
is the weight matrix, b
is the bias vector, $\eta$ is the learning rate, $\frac{dL}{dW}$ is the derivative of the loss function with respect to the weight, and $\frac{dL}{db}$ is the derivative of the loss function with respect to the bias.
The derivatives are calculated using an Automatic Differentiation tool, and Flux
uses Zygote.jl
for the same. Since Zygote.jl
is an independent Julia package, it can be used outside of Flux as well! Refer to the documentation of Zygote.jl
for more information on the same.
Our first step would be to obtain the gradient of the loss function with respect to the weights and the biases. Flux
reexports Zygote
's gradient
function; hence, we don't need to import Zygote
explicitly to use the functionality.
julia> dLdW, dLdb, _, _ = gradient(custom_loss, W, b, x, y);
We can now update the parameters, following the gradient descent algorithm 
julia> W .= W . 0.1 .* dLdW
1element Vector{Float32}:
1.8144473
julia> b .= b . 0.1 .* dLdb
1element Vector{Float32}:
0.41325632
The parameters have been updated! We can now check the value of the loss function 
julia> custom_loss(W, b, x, y)
17.157953f0
The loss went down! This means that we successfully trained our model for one epoch. We can plug the training code written above into a loop and train the model for a higher number of epochs. It can be customized either to have a fixed number of epochs or to stop when certain conditions are met, for example, change in loss < 0.1
. The loop can be tailored to suit the user's needs, and the conditions can be specified in plain Julia
!
Let's plug our super training logic inside a function and test it again 
julia> function train_custom_model()
dLdW, dLdb, _, _ = gradient(custom_loss, W, b, x, y)
@. W = W  0.1 * dLdW
@. b = b  0.1 * dLdb
end;
julia> train_custom_model();
julia> W, b, custom_loss(W, b, x, y)
(Float32[2.340657], Float32[0.7516814], 13.64972f0)
It works, and the loss went down again! This was the second epoch of our training procedure. Let's plug this in a for loop and train the model for 30 epochs.
julia> for i = 1:40
train_custom_model()
end
julia> W, b, custom_loss(W, b, x, y)
(Float32[4.2422233], Float32[2.2460847], 7.6680417f0)
There was a significant reduction in loss, and the parameters were updated!
We can train the model even more or tweak the hyperparameters to achieve the desired result faster, but let's stop here. We trained our model for 42 epochs, and loss went down from 22.74856
to 7.6680417f
. Time for some visualization!
Results
The main objective of this tutorial was to fit a line to our dataset using the linear regression algorithm. The training procedure went well, and the loss went down significantly! Let's see what the fitted line looks like. Remember, Wx + b
is nothing more than a line's equation, with slope = W[1]
and yintercept = b[1]
(indexing at 1
as W
and b
are iterable).
Plotting the line and the data points using Plot.jl

julia> plot(reshape(x, (61, 1)), reshape(y, (61, 1)), lw = 3, seriestype = :scatter, label = "", title = "Simple Linear Regression", xlabel = "x", ylabel= "y");
julia> plot!((x) > b[1] + W[1] * x, 3, 3, label="Custom model", lw=2);
The line fits well! There is room for improvement, but we leave that up to you! You can play with the optimisers, the number of epochs, learning rate, etc. to improve the fitting and reduce the loss!
Linear regression model on a real dataset
We now move on to a relatively complex linear regression model. Here we will use a real dataset from MLDatasets.jl
, which will not confine our data points to have only one feature. Let's start by importing the required packages 
julia> using Flux, Statistics, MLDatasets, DataFrames
Gathering real data
Let's start by initializing our dataset. We will be using the BostonHousing
dataset consisting of 506
data points. Each of these data points has 13
features and a corresponding label, the house's price. The x
s are still mapped to a single y
, but now, a single x
data point has 13 features.
julia> dataset = BostonHousing();
julia> x, y = BostonHousing(as_df=false)[:];
We can now split the obtained data into training and testing data 
julia> x_train, x_test, y_train, y_test = x[:, 1:400], x[:, 401:end], y[:, 1:400], y[:, 401:end];
julia> x_train > size, x_test > size, y_train > size, y_test > size
((13, 400), (13, 106), (1, 400), (1, 106))
This data contains a diverse number of features, which means that the features have different scales. A wise option here would be to normalise
the data, making the training process more efficient and fast. Let's check the standard deviation of the training data before normalising it.
julia> std(x_train)
134.06784844377117
The data is indeed not normalised. We can use the Flux.normalise
function to normalise the training data.
julia> x_train_n = Flux.normalise(x_train);
julia> std(x_train_n)
1.0000843694328236
The standard deviation is now close to one! Our data is ready!
Building a Flux model
We can now directly use Flux
and let it do all the work internally! Let's define a model that takes in 13 inputs (13 features) and gives us a single output (the label). We will then pass our entire data through this model in one go, and Flux
will handle everything for us! Remember, we could have declared a model in plain Julia
as well. The model will have 14 parameters: 13 weights and 1 bias.
julia> model = Dense(13 => 1)
Dense(13 => 1) # 14 parameters
Same as before, our next step would be to define a loss function to quantify our accuracy somehow. The lower the loss, the better the model!
julia> function loss(model, x, y)
ลท = model(x)
Flux.mse(ลท, y)
end;
julia> loss(model, x_train_n, y_train)
676.165591625047
We can now proceed to the training phase!
Training the Flux model
The training procedure would make use of the same mathematics, but now we can pass in the model inside the gradient
call and let Flux
and Zygote
handle the derivatives!
julia> function train_model()
dLdm, _, _ = gradient(loss, model, x_train_n, y_train)
@. model.weight = model.weight  0.000001 * dLdm.weight
@. model.bias = model.bias  0.000001 * dLdm.bias
end;
Contrary to our last training procedure, let's say that this time we don't want to hardcode the number of epochs. We want the training procedure to stop when the loss converges, that is, when change in loss < ฮด
. The quantity ฮด
can be altered according to a user's need, but let's fix it to 10โปยณ
for this tutorial.
We can write such custom training loops effortlessly using Flux
and plain Julia
!
julia> loss_init = Inf;
julia> while true
train_model()
if loss_init == Inf
loss_init = loss(model, x_train_n, y_train)
continue
end
if abs(loss_init  loss(model, x_train_n, y_train)) < 1e4
break
else
loss_init = loss(model, x_train_n, y_train)
end
end;
The code starts by initializing an initial value for the loss, infinity
. Next, it runs an infinite loop that breaks if change in loss < 10โปยณ
, or the code changes the value of loss_init
to the current loss and moves on to the next iteration.
This custom loop works! This shows how easily a user can write down any custom training routine using Flux and Julia!
Let's have a look at the loss 
julia> loss(model, x_train_n, y_train)
27.127200028562164
The loss went down significantly! It can be minimized further by choosing an even smaller ฮด
.
Testing the Flux model
The last step of this tutorial would be to test our model using the testing data. We will first normalise the testing data and then calculate the corresponding loss.
julia> x_test_n = Flux.normalise(x_test);
julia> loss(model, x_test_n, y_test)
66.91014769713368
The loss is not as small as the loss of the training data, but it looks good! This also shows that our model is not overfitting!
Summarising this tutorial, we started by generating a random yet correlated dataset for our custom model
. We then saw how a simple linear regression model could be built with and without Flux
, and how they were almost identical.
Next, we trained the model by manually writing down the Gradient Descent algorithm and optimising the loss. We also saw how Flux
provides various wrapper functionalities and keeps the API extremely intuitive and simple for the users.
After getting familiar with the basics of Flux
and Julia
, we moved ahead to build a machine learning model for a real dataset. We repeated the exact same steps, but this time with a lot more features and data points, and by harnessing Flux
's full capabilities. In the end, we developed a training loop that was smarter than the hardcoded one and ran the model on our normalised dataset to conclude the tutorial.
Originally published on 21 November 2022, by Saransh Chopra.