# 3.3. Concise Implementation of Linear Regression¶

Broad and intense interest in deep learning for the past several years has inspired both companies, academics, and hobbyists to develop a variety of mature open source frameworks for automating the repetitive work of implementing gradient-based learning algorithms. In the previous section, we relied only on (i) ndarray for data storage and linear algebra; and (ii) autograd for calculating derivatives. In practice, because data iterators, loss functions, optimizers, and neural network layers (and some whole architectures) are so common, modern libraries implement these components for us as well.

In this section, we will show you how to implement the linear regression model from sec_linear_scratch concisely by using Gluon.

## 3.3.1. Generating Data Sets¶

To start, we will generate the same data set as in the previous section.

import d2l
from mxnet import autograd, np, npx, gluon
npx.set_np()

true_w = np.array([2, -3.4])
true_b = 4.2
features, labels = d2l.synthetic_data(true_w, true_b, 1000)


Rather than rolling our own iterator, we can call upon Gluon’s data module to read data. The first step will be to instantiate an ArrayDataset. This object’s constructor takes one or more ndarrays as arguments. Here, we pass in features and labels as arguments. Next, we will use the ArrayDataset to instantiate a DataLoader, which also requires that we specify a batch_size and specify a Boolean value shuffle indicating whether or not we want the DataLoader to shuffle the data on each epoch (pass through the dataset).

# Save to the d2l package.
dataset = gluon.data.ArrayDataset(*data_arrays)

batch_size = 10


Now we can use data_iter in much the same way as we called the data_iter function in the previous section. To verify that it is working, we can read and print the first minibatch of instances.

for X, y in data_iter:
print(X, '\n', y)
break

[[-1.122059    2.8835263 ]
[-0.9910388  -0.19236775]
[-0.19262609 -0.94700885]
[ 0.74844444 -2.7521858 ]
[-1.0766882  -0.7581972 ]
[-0.6305844   2.207319  ]
[ 1.6114203   0.59097415]
[ 2.0085082  -1.3542701 ]
[ 1.0637976  -1.2535169 ]
[-1.5056137   0.25672248]]
[-7.828709   2.870505   7.03164   15.052703   4.634861  -4.565893
5.4101954 12.808898  10.58337    0.3373743]


## 3.3.3. Define the Model¶

When we implemented linear regression from scratch (in :num_refsec_linear_scratch), we defined our model parameters explicitly and coded up the calculations to produce output using basic linear algebra operations. You should know how to do this. But once your models get more complex, and once you have to do this nearly every day, you will be glad for the assistance. The situation is similar to coding up your own blog from scratch. Doing it once or twice is rewarding and instructive, but you would be a lousy web developer if every time you needed a blog you spent a month reinventing the weel.

For standard operations, we can use Gluon’s predefined layers, which allow us to focus especially on the layers used to construct the model rather than having to focus on the implementation. To define a linear model, we first import the nn module, which defines a large number of neural network layers (note that “nn” is an abbreviation for neural networks). We will first define a model variable net, which will refer to an instance of the Sequential class. In Gluon, Sequential defines a container for several layers that will be chained together. Given input data, a Sequential passes it through the first layer, in turn passing the output as the second layer’s input and so forth. In the following example, our model consists of only one layer, so we do not really need Sequential. But since nearly all of our future models will involve multiple layers, we will use it anyway just to familiarize you with the most standard workflow.

from mxnet.gluon import nn
net = nn.Sequential()


Recall the architecture of a single-layer network. The layer is said to be fully-connected because each of its inputs are connected to each of its outputs by means of a matrix-vector multiplication. In Gluon, the fully-connected layer is defined in the Dense class. Since we only want to generate a single scalar output, we set that number to $$1$$.

net.add(nn.Dense(1))


It is worth noting that, for convenience, Gluon does not require us to specify the input shape for each layer. So here, we don’t need to tell Gluon how many inputs go into this linear layer. When we first try to pass data through our model, e.g., when we execute net(X) later, Gluon will automatically infer the number of inputs to each layer. We will describe how this works in more detail in the chapter “Deep Learning Computation”.

## 3.3.4. Initialize Model Parameters¶

Before using net, we need to initialize the model parameters, such as the weights and biases in the linear regression model. We will import the initializer module from MXNet. This module provides various methods for model parameter initialization. Gluon makes init available as a shortcut (abbreviation) to access the initializer package. By calling init.Normal(sigma=0.01), we specify that each weight parameter should be randomly sampled from a normal distribution with mean $$0$$ and standard deviation $$0.01$$. The bias parameter will be initialized to zero by default. Both the weight vector and bias will have attached gradients.

from mxnet import init
net.initialize(init.Normal(sigma=0.01))


The code above may look straightforward but you should note that something strange is happening here. We are initializing parameters for a network even though Gluon does not yet know how many dimensions the input will have! It might be $$2$$ as in our example or it might be $$2000$$. Gluon lets us get away with this because behind the scenes, the initialization is actually deferred. The real initialization will take place only when we for the first time attempt to pass data through the network. Just be careful to remember that since the parameters have not been initialized yet, we cannot access or manipulate them.

## 3.3.5. Define the Loss Function¶

In Gluon, the loss module defines various loss functions. We will the imported module loss with the pseudonym gloss, to avoid confusing it for the variable holding our chosen loss function. In this example, we will use the Gluon implementation of squared loss (L2Loss).

from mxnet.gluon import loss as gloss
loss = gloss.L2Loss()  # The squared loss is also known as the L2 norm loss


## 3.3.6. Define the Optimization Algorithm¶

Minibatch SGD and related variants are standard tools for optimizing neural networks and thus Gluon supports SGD alongside a number of variations on this algorithm through its Trainer class. When we instantiate the Trainer, we will specify the parameters to optimize over (obtainable from our net via net.collect_params()), the optimization algortihm we wish to use (sgd), and a dictionary of hyper-parameters required by our optimization algorithm. SGD just requires that we set the value learning_rate, (here we set it to 0.03).

from mxnet import gluon
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.03})


## 3.3.7. Training¶

You might have noticed that expressing our model through Gluon requires comparatively few lines of code. We didn’t have to individually allocate parameters, define our loss function, or implement stochastic gradient descent. Once we start working with much more complex models, Gluon’s advantages will grow considerably. However, once we have all the basic pieces in place, the training loop itself is strikingly similar to what we did when implementing everything from scratch.

To refresh your memory: for some number of epochs, we’ll make a complete pass over the dataset (train_data), iteratively grabbing one minibatch of inputs and the corresponding ground-truth labels. For each minibatch, we go through the following ritual:

• Generate predictions by calling net(X) and calculate the loss l (the forward pass).
• Calculate gradients by calling l.backward() (the backward pass).
• Update the model parameters by invoking our SGD optimizer (note that trainer already knows which parameters to optimize over, so we just need to pass in the minibatch size.

For good measure, we compute the loss after each epoch and print it to monitor progress.

num_epochs = 3
for epoch in range(1, num_epochs + 1):
for X, y in data_iter:
l = loss(net(X), y)
l.backward()
trainer.step(batch_size)
l = loss(net(features), labels)
print('epoch %d, loss: %f' % (epoch, l.mean().asnumpy()))

epoch 1, loss: 0.040539
epoch 2, loss: 0.000160
epoch 3, loss: 0.000051


Below, we compare the model parameters learned by training on finite data and the actual parameters that generated our dataset. To access parameters with Gluon, we first access the layer that we need from net and then access that layer’s weight (weight) and bias (bias). To access each parameter’s values as an ndarray, we invoke its data() method. As in our from-scratch implementation, note that our estimated parameters are close to their ground truth counterparts.

w = net[0].weight.data()
print('Error in estimating w', true_w.reshape(w.shape) - w)
b = net[0].bias.data()
print('Error in estimating b', true_b - b)

Error in estimating w [[ 0.0003159  -0.00055933]]
Error in estimating b [0.00083876]


## 3.3.8. Summary¶

• Using Gluon, we can implement models much more succinctly.
• In Gluon, the data module provides tools for data processing, the nn module defines a large number of neural network layers, and the loss module defines many common loss functions.
• MXNet’s module initializer provides various methods for model parameter initialization.
• Dimensionality and storage are automatically inferred (but be careful not to attempt to access parameters before they have been initialized).

## 3.3.9. Exercises¶

1. If we replace l = loss(output, y) with l = loss(output, y).mean(), we need to change trainer.step(batch_size) to trainer.step(1) for the code to behave identically. Why?
2. Review the MXNet documentation to see what loss functions and initialization methods are provided in the modules gluon.loss and init. Replace the loss by Huber’s loss.
3. How do you access the gradient of dense.weight?