# 3.2. Linear Regression Implementation from Scratch¶ Open the notebook in Colab

Now that you understand the key ideas behind linear regression, we can
begin to work through a hands-on implementation in code. In this
section, we will implement the entire method from scratch, including the
data pipeline, the model, the loss function, and the gradient descent
optimizer. While modern deep learning frameworks can automate nearly all
of this work, implementing things from scratch is the only to make sure
that you really know what you are doing. Moreover, when it comes time to
customize models, defining our own layers, loss functions, etc.,
understanding how things work under the hood will prove handy. In this
section, we will rely only on `ndarray`

and `autograd`

. Afterwards,
we will introduce a more compact implementation, taking advantage of
Gluon’s bells and whistles. To start off, we import the few required
packages.

```
%matplotlib inline
import d2l
from mxnet import autograd, np, npx
import random
npx.set_np()
```

## 3.2.1. Generating the Dataset¶

To keep things simple, we will construct an artificial dataset according to a linear model with additive noise. Out task will be to recover this model’s parameters using the finite set of examples contained in our dataset. We will keep the data low-dimensional so we can visualize it easily. In the following code snippet, we generated a dataset containing \(1000\) examples, each consisting of \(2\) features sampled from a standard normal distribution. Thus our synthetic dataset will be an object \(\mathbf{X}\in \mathbb{R}^{1000 \times 2}\).

The true parameters generating our data will be \(\mathbf{w} = [2, -3.4]^\top\) and \(b = 4.2\) and our synthetic labels will be assigned according to the following linear model with noise term \(\epsilon\):

You could think of \(\epsilon\) as capturing potential measurement errors on the features and labels. We will assume that the standard assumptions hold and thus that \(\epsilon\) obeys a normal distribution with mean of \(0\). To make our problem easy, we will set its standard deviation to \(0.01\). The following code generates our synthetic dataset:

```
# Saved in the d2l package for later use
def synthetic_data(w, b, num_examples):
"""Generate y = X w + b + noise."""
X = np.random.normal(0, 1, (num_examples, len(w)))
y = np.dot(X, w) + b
y += np.random.normal(0, 0.01, y.shape)
return X, y
true_w = np.array([2, -3.4])
true_b = 4.2
features, labels = synthetic_data(true_w, true_b, 1000)
```

Note that each row in `features`

consists of a 2-dimensional data
point and that each row in `labels`

consists of a 1-dimensional target
value (a scalar).

```
print('features:', features[0],'\nlabel:', labels[0])
```

```
features: [2.2122064 1.1630787]
label: 4.662078
```

By generating a scatter plot using the second `features[:, 1]`

and
`labels`

, we can clearly observe the linear correlation between the
two.

```
d2l.set_figsize((3.5, 2.5))
d2l.plt.scatter(features[:, 1].asnumpy(), labels.asnumpy(), 1);
```

## 3.2.2. Reading the Dataset¶

Recall that training models consists of making multiple passes over the dataset, grabbing one minibatch of examples at a time, and using them to update our model. Since this process is so fundamental to training machine learning algorithms, its worth defining a utility function to shuffle the data and access it in minibatches.

In the following code, we define a `data_iter`

function to demonstrate
one possible implementation of this functionality. The function takes a
batch size, a design matrix, and a vector of labels, yielding
minibatches of size `batch_size`

. Each minibatch consists of an tuple
of features and labels.

```
def data_iter(batch_size, features, labels):
num_examples = len(features)
indices = list(range(num_examples))
# The examples are read at random, in no particular order
random.shuffle(indices)
for i in range(0, num_examples, batch_size):
batch_indices = np.array(
indices[i: min(i + batch_size, num_examples)])
yield features[batch_indices], labels[batch_indices]
```

In general, note that we want to use reasonably sized minibatches to take advantage of the GPU hardware, which excels at parallelizing operations. Because each example can be fed through our models in parallel and the gradient of the loss function for each example can also be taken in parallel, GPUs allow us to process hundreds of examples in scarcely more time than it might take to process just a single example.

To build some intuition, let us read and print the first small batch of
data examples. The shape of the features in each minibatch tells us both
the minibatch size and the number of input features. Likewise, our
minibatch of labels will have a shape given by `batch_size`

.

```
batch_size = 10
for X, y in data_iter(batch_size, features, labels):
print(X, '\n', y)
break
```

```
[[-0.96688974 0.6003209 ]
[-1.0497183 0.03132189]
[ 0.09625402 -0.9130886 ]
[-2.298264 0.10911477]
[ 2.248859 -0.15570128]
[ 1.3597034 0.7995023 ]
[-0.5381857 -1.6348621 ]
[-0.30227563 0.1624491 ]
[ 1.1049992 1.3410575 ]
[-2.2465067 -1.3058136 ]]
[ 0.2236586 1.9896969 7.49567 -0.75900215 9.2266245 4.201482
8.690356 3.044561 1.8435274 4.140484 ]
```

As we run the iterator, we obtain distinct minibatches successively until all the data has been exhausted (try this). While the iterator implemented above is good for didactic purposes, it is inefficient in ways that might get us in trouble on real problems. For example, it requires that we load all data in memory and that we perform lots of random memory access. The built-in iterators implemented in Apache MXNet are considerably more efficient and they can deal both with data stored in file and data fed via a data stream.

## 3.2.3. Initializing Model Parameters¶

Before we can begin optimizing our model’s parameters by gradient descent, we need to have some parameters in the first place. In the following code, we initialize weights by sampling random numbers from a normal distribution with mean 0 and a standard deviation of \(0.01\), setting the bias \(b\) to \(0\).

```
w = np.random.normal(0, 0.01, (2, 1))
b = np.zeros(1)
```

Now that we have initialized our parameters, our next task is to update them until they fit our data sufficiently well. Each update requires taking the gradient (a multi-dimensional derivative) of our loss function with respect to the parameters. Given this gradient, we can update each parameter in the direction that reduces the loss.

Since nobody wants to compute gradients explicitly (this is tedious and
error prone), we use automatic differentiation to compute the gradient.
See Section 2.5 for more details. Recall from the autograd
chapter that in order for `autograd`

to know that it should store a
gradient for our parameters, we need to invoke the `attach_grad`

function, allocating memory to store the gradients that we plan to take.

```
w.attach_grad()
b.attach_grad()
```

## 3.2.4. Defining the Model¶

Next, we must define our model, relating its inputs and parameters to
its outputs. Recall that to calculate the output of the linear model, we
simply take the matrix-vector dot product of the examples
\(\mathbf{X}\) and the models weights \(w\), and add the offset
\(b\) to each example. Note that below `np.dot(X, w)`

is a vector
and `b`

is a scalar. Recall that when we add a vector and a scalar,
the scalar is added to each component of the vector.

```
# Saved in the d2l package for later use
def linreg(X, w, b):
return np.dot(X, w) + b
```

## 3.2.5. Defining the Loss Function¶

Since updating our model requires taking the gradient of our loss
function, we ought to define the loss function first. Here we will use
the squared loss function as described in the previous section. In the
implementation, we need to transform the true value `y`

into the
predicted value’s shape `y_hat`

. The result returned by the following
function will also be the same as the `y_hat`

shape.

```
# Saved in the d2l package for later use
def squared_loss(y_hat, y):
return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2
```

## 3.2.6. Defining the Optimization Algorithm¶

As we discussed in the previous section, linear regression has a closed-form solution. However, this is not a book about linear regression, it is a book about deep learning. Since none of the other models that this book introduces can be solved analytically, we will take this opportunity to introduce your first working example of stochastic gradient descent (SGD).

At each step, using one batch randomly drawn from our dataset, we will
estimate the gradient of the loss with respect to our parameters. Next,
we will update our parameters (a small amount) in the direction that
reduces the loss. Recall from Section 2.5 that after we
call `backward`

each parameter (`param`

) will have its gradient
stored in `param.grad`

. The following code applies the SGD update,
given a set of parameters, a learning rate, and a batch size. The size
of the update step is determined by the learning rate `lr`

. Because
our loss is calculated as a sum over the batch of examples, we normalize
our step size by the batch size (`batch_size`

), so that the magnitude
of a typical step size does not depend heavily on our choice of the
batch size.

```
# Saved in the d2l package for later use
def sgd(params, lr, batch_size):
for param in params:
param[:] = param - lr * param.grad / batch_size
```

## 3.2.7. Training¶

Now that we have all of the parts in place, we are ready to implement the main training loop. It is crucial that you understand this code because you will see nearly identical training loops over and over again throughout your career in deep learning.

In each iteration, we will grab minibatches of models, first passing
them through our model to obtain a set of predictions. After calculating
the loss, we call the `backward`

function to initiate the backwards
pass through the network, storing the gradients with respect to each
parameter in its corresponding `.grad`

attribute. Finally, we will
call the optimization algorithm `sgd`

to update the model parameters.
Since we previously set the batch size `batch_size`

to \(10\), the
loss shape `l`

for each minibatch is (\(10\), \(1\)).

In summary, we will execute the following loop:

Initialize parameters \((\mathbf{w}, b)\)

Repeat until done

Compute gradient \(\mathbf{g} \leftarrow \partial_{(\mathbf{w},b)} \frac{1}{\mathcal{B}} \sum_{i \in \mathcal{B}} l(\mathbf{x}^i, y^i, \mathbf{w}, b)\)

Update parameters \((\mathbf{w}, b) \leftarrow (\mathbf{w}, b) - \eta \mathbf{g}\)

In the code below, `l`

is a vector of the losses for each example in
the minibatch. Because `l`

is not a scalar variable, running
`l.backward()`

adds together the elements in `l`

to obtain the new
variable and then calculates the gradient.

In each epoch (a pass through the data), we will iterate through the
entire dataset (using the `data_iter`

function) once passing through
every examples in the training dataset (assuming the number of examples
is divisible by the batch size). The number of epochs `num_epochs`

and
the learning rate `lr`

are both hyper-parameters, which we set here to
\(3\) and \(0.03\), respectively. Unfortunately, setting
hyper-parameters is tricky and requires some adjustment by trial and
error. We elide these details for now but revise them later in
Section 11.

```
lr = 0.03 # Learning rate
num_epochs = 3 # Number of iterations
net = linreg # Our fancy linear model
loss = squared_loss # 0.5 (y-y')^2
for epoch in range(num_epochs):
# Assuming the number of examples can be divided by the batch size, all
# the examples in the training dataset are used once in one epoch
# iteration. The features and tags of minibatch examples are given by X
# and y respectively
for X, y in data_iter(batch_size, features, labels):
with autograd.record():
l = loss(net(X, w, b), y) # Minibatch loss in X and y
l.backward() # Compute gradient on l with respect to [w, b]
sgd([w, b], lr, batch_size) # Update parameters using their gradient
train_l = loss(net(features, w, b), labels)
print('epoch %d, loss %f' % (epoch + 1, train_l.mean().asnumpy()))
```

```
epoch 1, loss 0.025142
epoch 2, loss 0.000087
epoch 3, loss 0.000051
```

In this case, because we synthesized the data ourselves, we know precisely what the true parameters are. Thus, we can evaluate our success in training by comparing the true parameters with those that we learned through our training loop. Indeed they turn out to be very close to each other.

```
print('Error in estimating w', true_w - w.reshape(true_w.shape))
print('Error in estimating b', true_b - b)
```

```
Error in estimating w [-0.0001018 0.00017595]
Error in estimating b [0.00030088]
```

Note that we should not take it for granted that we are able to recover
the parameters accurately. This only happens for a special category
problems: strongly convex optimization problems with “enough” data to
ensure that the noisy samples allow us to recover the underlying
dependency. In most cases this is *not* the case. In fact, the
parameters of a deep network are rarely the same (or even close) between
two different runs, unless all conditions are identical, including the
order in which the data is traversed. However, in machine learning, we
are typically less concerned with recovering true underlying parameters,
and more concerned with parameters that lead to accurate prediction.
Fortunately, even on difficult optimization problems, stochastic
gradient descent can often find remarkably good solutions, owing partly
to the fact that, for deep networks, there exist many configurations of
the parameters that lead to accurate prediction.

## 3.2.8. Summary¶

We saw how a deep network can be implemented and optimized from scratch,
using just `ndarray`

and `autograd`

, without any need for defining
layers, fancy optimizers, etc. This only scratches the surface of what
is possible. In the following sections, we will describe additional
models based on the concepts that we have just introduced and learn how
to implement them more concisely.

## 3.2.9. Exercises¶

What would happen if we were to initialize the weights \(\mathbf{w} = 0\). Would the algorithm still work?

Assume that you are Georg Simon Ohm trying to come up with a model between voltage and current. Can you use

`autograd`

to learn the parameters of your model.Can you use Planck’s Law to determine the temperature of an object using spectral energy density?

What are the problems you might encounter if you wanted to extend

`autograd`

to second derivatives? How would you fix them?Why is the

`reshape`

function needed in the`squared_loss`

function?Experiment using different learning rates to find out how fast the loss function value drops.

If the number of examples cannot be divided by the batch size, what happens to the

`data_iter`

function’s behavior?