# 3.3. Synthetic Regression Data¶ Open the notebook in Colab Open the notebook in Colab Open the notebook in Colab Open the notebook in SageMaker Studio Lab

Machine learning is all about extracting information from data. So you might wonder, what could we possibly learn from synthetic data? While we might not care intrinsically about the patterns that we ourselves baked into an artificial data generating model, such datasets are nevertheless useful for didactic purposes, helping us to evaluate the properties of our learning algorithms and to confirm that our implementations work as expected. For example, if we create data for which the correct parameters are known a priori, then we can verify that our model can in fact recover them.

%matplotlib inline
import random
import torch
from d2l import torch as d2l

%matplotlib inline
import random
from mxnet import gluon, np, npx
from d2l import mxnet as d2l

npx.set_np()

%matplotlib inline
import random
import tensorflow as tf
from d2l import tensorflow as d2l


## 3.3.1. Generating the Dataset¶

For this example, we will work low-dimensional for succinctness. The following code snippet generates 1000 examples with 2-dimensional features drawn from a standard normal distribution. The resulting design matrix $$\mathbf{X}$$ belongs to $$\mathbb{R}^{1000 \times 2}$$. We generate each label by applying a ground truth linear function, corrupted them via additive noise $$\epsilon$$, drawn independently and identically for each example:

(3.3.1)$\mathbf{y}= \mathbf{X} \mathbf{w} + b + \mathbf\epsilon.$

For convenience we assume that $$\epsilon$$ is drawn from a normal distribution with mean $$\mu= 0$$ and standard deviation $$\sigma = 0.01$$. Note that for object-oriented design we add the code to the __init__ method of a subclass of d2l.DataModule (introduced in Section 3.2.3). It’s good practice to allow setting any additional hyperparameters. We accomplish this with save_hyperparameters(). The batch_size will be determined later on.

class SyntheticRegressionData(d2l.DataModule):  #@save
def __init__(self, w, b, noise=0.01, num_train=1000, num_val=1000,
batch_size=32):
super().__init__()
self.save_hyperparameters()
n = num_train + num_val
self.X = torch.randn(n, len(w))
noise = torch.randn(n, 1) * noise
self.y = torch.matmul(self.X, w.reshape((-1, 1))) + b + noise

class SyntheticRegressionData(d2l.DataModule):  #@save
def __init__(self, w, b, noise=0.01, num_train=1000, num_val=1000,
batch_size=32):
super().__init__()
self.save_hyperparameters()
n = num_train + num_val
self.X = np.random.randn(n, len(w))
noise = np.random.randn(n, 1) * noise
self.y = np.dot(self.X, w.reshape((-1, 1))) + b + noise

class SyntheticRegressionData(d2l.DataModule):  #@save
def __init__(self, w, b, noise=0.01, num_train=1000, num_val=1000,
batch_size=32):
super().__init__()
self.save_hyperparameters()
n = num_train + num_val
self.X = tf.random.normal((n, w.shape[0]))
noise = tf.random.normal((n, 1)) * noise
self.y = tf.matmul(self.X, tf.reshape(w, (-1, 1))) + b + noise


Below, we set the true parameters to $$\mathbf{w} = [2, -3.4]^\top$$ and $$b = 4.2$$. Later, we can check our estimated parameters against these ground truth values.

data = SyntheticRegressionData(w=torch.tensor([2, -3.4]), b=4.2)

data = SyntheticRegressionData(w=np.array([2, -3.4]), b=4.2)

data = SyntheticRegressionData(w=tf.constant([2, -3.4]), b=4.2)


Each row in features consists of a vector in $$\mathbb{R}^2$$ and each row in labels is a scalar. Let’s have a look at the first entry.

print('features:', data.X[0],'\nlabel:', data.y[0])

features: tensor([-1.0052, -0.1289])
label: tensor([2.6433])

print('features:', data.X[0],'\nlabel:', data.y[0])

features: [2.2122064 1.1630787]
label: [4.684836]

print('features:', data.X[0],'\nlabel:', data.y[0])

features: tf.Tensor([ 0.26570445 -1.9220122 ], shape=(2,), dtype=float32)
label: tf.Tensor([11.265601], shape=(1,), dtype=float32)


## 3.3.2. Reading the Dataset¶

Training machine learning models often requires multiple passes over a dataset, grabbing one minibatch of examples at a time. This data is then used to update the model. To illustrate how this works, we implement the get_dataloader function, registering it as a method in the SyntheticRegressionData class via add_to_class (introduced in Section 3.2.1). It takes a batch size, a matrix of features, and a vector of labels, and generates minibatches of size batch_size. As such, each minibatch consists of a tuple of features and labels. Note that we need to be mindful of whether we’re in training or validation mode: in the former, we will want to read the data in random order, whereas for the latter, being able to read data in a pre-defined order may be important for debugging purposes.

@d2l.add_to_class(SyntheticRegressionData)
if train:
indices = list(range(0, self.num_train))
# The examples are read in random order
random.shuffle(indices)
else:
indices = list(range(self.num_train, self.num_train+self.num_val))
for i in range(0, len(indices), self.batch_size):
batch_indices = torch.tensor(indices[i: i+self.batch_size])
yield self.X[batch_indices], self.y[batch_indices]

@d2l.add_to_class(SyntheticRegressionData)
if train:
indices = list(range(0, self.num_train))
# The examples are read in random order
random.shuffle(indices)
else:
indices = list(range(self.num_train, self.num_train+self.num_val))
for i in range(0, len(indices), self.batch_size):
batch_indices = np.array(indices[i: i+self.batch_size])
yield self.X[batch_indices], self.y[batch_indices]

@d2l.add_to_class(SyntheticRegressionData)
if train:
indices = list(range(0, self.num_train))
# The examples are read in random order
random.shuffle(indices)
else:
indices = list(range(self.num_train, self.num_train+self.num_val))
for i in range(0, len(indices), self.batch_size):
j = tf.constant(indices[i : i+self.batch_size])
yield tf.gather(self.X, j), tf.gather(self.y, j)


To build some intuition, let’s inspect the first minibatch of data. Each minibatch of features provides us with both its size and the dimensionality of input features. Likewise, our minibatch of labels will have a matching shape given by batch_size.

X, y = next(iter(data.train_dataloader()))
print('X shape:', X.shape, '\ny shape:', y.shape)

X shape: torch.Size([32, 2])
y shape: torch.Size([32, 1])

X, y = next(iter(data.train_dataloader()))
print('X shape:', X.shape, '\ny shape:', y.shape)

X shape: (32, 2)
y shape: (32, 1)

X, y = next(iter(data.train_dataloader()))
print('X shape:', X.shape, '\ny shape:', y.shape)

X shape: (32, 2)
y shape: (32, 1)


While seemingly innocuous, the invocation of iter(data.train_dataloader()) illustrates the power of Python’s object-oriented design. Note that we added a method to the SyntheticRegressionData class after creating the data object. Nonetheless, the object benefits from the ex post facto addition of functionality to the class.

Throughout the iteration we obtain distinct minibatches until the entire dataset has been exhausted (try this). While the iteration 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 the data in memory and that we perform lots of random memory access. The built-in iterators implemented in a deep learning framework are considerably more efficient and they can deal with sources such as data stored in files, data received via a stream, and data generated or processed on the fly. Next let’s try to implement the same function using built-in iterators.

## 3.3.3. Concise Implementation of the Data Loader¶

Rather than writing our own iterator, we can call the existing API in a framework to load data. As before, we need a dataset with features X and labels y. Beyond that, we set batch_size in the built-in data loader and let it take care of shuffling examples efficiently.

@d2l.add_to_class(d2l.DataModule)  #@save
def get_tensorloader(self, tensors, train, indices=slice(0, None)):
tensors = tuple(a[indices] for a in tensors)
dataset = torch.utils.data.TensorDataset(*tensors)
shuffle=train)
i = slice(0, self.num_train) if train else slice(self.num_train, None)
return self.get_tensorloader((self.X, self.y), train, i)

@d2l.add_to_class(d2l.DataModule)  #@save
def get_tensorloader(self, tensors, train, indices=slice(0, None)):
tensors = tuple(a[indices] for a in tensors)
dataset = gluon.data.ArrayDataset(*tensors)
shuffle=train)
i = slice(0, self.num_train) if train else slice(self.num_train, None)
return self.get_tensorloader((self.X, self.y), train, i)

@d2l.add_to_class(d2l.DataModule)  #@save
def get_tensorloader(self, tensors, train, indices=slice(0, None)):
tensors = tuple(a[indices] for a in tensors)
shuffle_buffer = tensors[0].shape[0] if train else 1
return tf.data.Dataset.from_tensor_slices(tensors).shuffle(
buffer_size=shuffle_buffer).batch(self.batch_size)

i = slice(0, self.num_train) if train else slice(self.num_train, None)
return self.get_tensorloader((self.X, self.y), train, i)


The new data loader behaves just as the previous one, except that it is more efficient and has some added functionality.

X, y = next(iter(data.train_dataloader()))
print('X shape:', X.shape, '\ny shape:', y.shape)

X shape: torch.Size([32, 2])
y shape: torch.Size([32, 1])

X, y = next(iter(data.train_dataloader()))
print('X shape:', X.shape, '\ny shape:', y.shape)

X shape: (32, 2)
y shape: (32, 1)

X, y = next(iter(data.train_dataloader()))
print('X shape:', X.shape, '\ny shape:', y.shape)

X shape: (32, 2)
y shape: (32, 1)


For instance, the data loader provided by the framework API supports the built-in __len__ method, so we can query its length, i.e., the number of batches.

len(data.train_dataloader())

32

len(data.train_dataloader())

32

len(data.train_dataloader())

32


## 3.3.4. Summary¶

Data loaders are a convenient way of abstracting out the process of loading and manipulating data. This way the same machine learning algorithm is capable of processing many different types and sources of data without the need for modification. One of the nice things about data loaders is that they can be composed. For instance, we might be loading images and then have a post-processing filter that crops them or modifies them otherwise. As such, data loaders can be used to describe an entire data processing pipeline.

As for the model itself, the two-dimensional linear model is about as simple a model as we might encounter. It lets us test out the accuracy of regression models without worry about having insufficient amounts of data or an underdetermined system of equations. We will put this to good use in the next section.

## 3.3.5. Exercises¶

1. What will happen if the number of examples cannot be divided by the batch size. How to change this behavior by specifying a different argument by using framework’s API?

2. What if we want to generate a huge dataset, where both the size of the parameter vector w and the number of examples num_examples are large?

1. What happens if we cannot hold all data in memory?

2. How would you shuffle the data if data is held on disk? Your task is to design an efficient algorithm that does not require too many random reads or writes. Hint: pseudorandom permutation generators allow you to design a reshuffle without the need to store the permutation table explicitly .

3. Implement a data generator that produces new data on the fly, every time the iterator is called.

4. How would you design a random data generator that generates the same data each time it’s called?