# 11.4. Stochastic Gradient Descent¶ Open the notebook in SageMaker Studio Lab

In earlier chapters we kept using stochastic gradient descent in our
training procedure, however, without explaining why it works. To shed
some light on it, we just described the basic principles of gradient
descent in Section 11.3. In this section, we go on to discuss
*stochastic gradient descent* in greater detail.

```
%matplotlib inline
import math
from mxnet import np, npx
from d2l import mxnet as d2l
npx.set_np()
```

```
%matplotlib inline
import math
import torch
from d2l import torch as d2l
```

```
%matplotlib inline
import math
import tensorflow as tf
from d2l import tensorflow as d2l
```

## 11.4.1. Stochastic Gradient Updates¶

In deep learning, the objective function is usually the average of the loss functions for each example in the training dataset. Given a training dataset of \(n\) examples, we assume that \(f_i(\mathbf{x})\) is the loss function with respect to the training example of index \(i\), where \(\mathbf{x}\) is the parameter vector. Then we arrive at the objective function

The gradient of the objective function at \(\mathbf{x}\) is computed as

If gradient descent is used, the computational cost for each independent variable iteration is \(\mathcal{O}(n)\), which grows linearly with \(n\). Therefore, when the training dataset is larger, the cost of gradient descent for each iteration will be higher.

Stochastic gradient descent (SGD) reduces computational cost at each iteration. At each iteration of stochastic gradient descent, we uniformly sample an index \(i\in\{1,\ldots, n\}\) for data examples at random, and compute the gradient \(\nabla f_i(\mathbf{x})\) to update \(\mathbf{x}\):

where \(\eta\) is the learning rate. We can see that the computational cost for each iteration drops from \(\mathcal{O}(n)\) of the gradient descent to the constant \(\mathcal{O}(1)\). Moreover, we want to emphasize that the stochastic gradient \(\nabla f_i(\mathbf{x})\) is an unbiased estimate of the full gradient \(\nabla f(\mathbf{x})\) because

This means that, on average, the stochastic gradient is a good estimate of the gradient.

Now, we will compare it with gradient descent by adding random noise with a mean of 0 and a variance of 1 to the gradient to simulate a stochastic gradient descent.

```
def f(x1, x2): # Objective function
return x1 ** 2 + 2 * x2 ** 2
def f_grad(x1, x2): # Gradient of the objective function
return 2 * x1, 4 * x2
def sgd(x1, x2, s1, s2, f_grad):
g1, g2 = f_grad(x1, x2)
# Simulate noisy gradient
g1 += np.random.normal(0.0, 1, (1,))
g2 += np.random.normal(0.0, 1, (1,))
eta_t = eta * lr()
return (x1 - eta_t * g1, x2 - eta_t * g2, 0, 0)
def constant_lr():
return 1
eta = 0.1
lr = constant_lr # Constant learning rate
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=50, f_grad=f_grad))
```

```
epoch 50, x1: -0.472513, x2: 0.110780
```

```
def f(x1, x2): # Objective function
return x1 ** 2 + 2 * x2 ** 2
def f_grad(x1, x2): # Gradient of the objective function
return 2 * x1, 4 * x2
def sgd(x1, x2, s1, s2, f_grad):
g1, g2 = f_grad(x1, x2)
# Simulate noisy gradient
g1 += torch.normal(0.0, 1, (1,))
g2 += torch.normal(0.0, 1, (1,))
eta_t = eta * lr()
return (x1 - eta_t * g1, x2 - eta_t * g2, 0, 0)
def constant_lr():
return 1
eta = 0.1
lr = constant_lr # Constant learning rate
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=50, f_grad=f_grad))
```

```
epoch 50, x1: -0.027909, x2: -0.175327
```

```
def f(x1, x2): # Objective function
return x1 ** 2 + 2 * x2 ** 2
def f_grad(x1, x2): # Gradient of the objective function
return 2 * x1, 4 * x2
def sgd(x1, x2, s1, s2, f_grad):
g1, g2 = f_grad(x1, x2)
# Simulate noisy gradient
g1 += tf.random.normal([1], 0.0, 1)
g2 += tf.random.normal([1], 0.0, 1)
eta_t = eta * lr()
return (x1 - eta_t * g1, x2 - eta_t * g2, 0, 0)
def constant_lr():
return 1
eta = 0.1
lr = constant_lr # Constant learning rate
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=50, f_grad=f_grad))
```

```
epoch 50, x1: -0.010124, x2: -0.092619
```

As we can see, the trajectory of the variables in the stochastic
gradient descent is much more noisy than the one we observed in gradient
descent in Section 11.3. This is due to the stochastic nature of
the gradient. That is, even when we arrive near the minimum, we are
still subject to the uncertainty injected by the instantaneous gradient
via \(\eta \nabla f_i(\mathbf{x})\). Even after 50 steps the quality
is still not so good. Even worse, it will not improve after additional
steps (we encourage you to experiment with a larger number of steps to
confirm this). This leaves us with the only alternative: change the
learning rate \(\eta\). However, if we pick this too small, we will
not make any meaningful progress initially. On the other hand, if we
pick it too large, we will not get a good solution, as seen above. The
only way to resolve these conflicting goals is to reduce the learning
rate *dynamically* as optimization progresses.

This is also the reason for adding a learning rate function `lr`

into
the `sgd`

step function. In the example above any functionality for
learning rate scheduling lies dormant as we set the associated `lr`

function to be constant.

## 11.4.2. Dynamic Learning Rate¶

Replacing \(\eta\) with a time-dependent learning rate \(\eta(t)\) adds to the complexity of controlling convergence of an optimization algorithm. In particular, we need to figure out how rapidly \(\eta\) should decay. If it is too quick, we will stop optimizing prematurely. If we decrease it too slowly, we waste too much time on optimization. The following are a few basic strategies that are used in adjusting \(\eta\) over time (we will discuss more advanced strategies later):

In the first *piecewise constant* scenario we decrease the learning
rate, e.g., whenever progress in optimization stalls. This is a common
strategy for training deep networks. Alternatively we could decrease it
much more aggressively by an *exponential decay*. Unfortunately this
often leads to premature stopping before the algorithm has converged. A
popular choice is *polynomial decay* with \(\alpha = 0.5\). In the
case of convex optimization there are a number of proofs that show that
this rate is well behaved.

Let us see what the exponential decay looks like in practice.

```
def exponential_lr():
# Global variable that is defined outside this function and updated inside
global t
t += 1
return math.exp(-0.1 * t)
t = 1
lr = exponential_lr
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=1000, f_grad=f_grad))
```

```
epoch 1000, x1: -0.820458, x2: 0.004701
```

```
def exponential_lr():
# Global variable that is defined outside this function and updated inside
global t
t += 1
return math.exp(-0.1 * t)
t = 1
lr = exponential_lr
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=1000, f_grad=f_grad))
```

```
epoch 1000, x1: -0.794224, x2: -0.080204
```

```
def exponential_lr():
# Global variable that is defined outside this function and updated inside
global t
t += 1
return math.exp(-0.1 * t)
t = 1
lr = exponential_lr
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=1000, f_grad=f_grad))
```

```
epoch 1000, x1: -0.785397, x2: 0.002270
```

As expected, the variance in the parameters is significantly reduced. However, this comes at the expense of failing to converge to the optimal solution \(\mathbf{x} = (0, 0)\). Even after 1000 iteration steps are we are still very far away from the optimal solution. Indeed, the algorithm fails to converge at all. On the other hand, if we use a polynomial decay where the learning rate decays with the inverse square root of the number of steps, convergence gets better after only 50 steps.

```
def polynomial_lr():
# Global variable that is defined outside this function and updated inside
global t
t += 1
return (1 + 0.1 * t) ** (-0.5)
t = 1
lr = polynomial_lr
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=50, f_grad=f_grad))
```

```
epoch 50, x1: 0.025029, x2: 0.115820
```

```
def polynomial_lr():
# Global variable that is defined outside this function and updated inside
global t
t += 1
return (1 + 0.1 * t) ** (-0.5)
t = 1
lr = polynomial_lr
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=50, f_grad=f_grad))
```

```
epoch 50, x1: 0.169398, x2: 0.067291
```

```
def polynomial_lr():
# Global variable that is defined outside this function and updated inside
global t
t += 1
return (1 + 0.1 * t) ** (-0.5)
t = 1
lr = polynomial_lr
d2l.show_trace_2d(f, d2l.train_2d(sgd, steps=50, f_grad=f_grad))
```

```
epoch 50, x1: -0.125012, x2: -0.084739
```

There exist many more choices for how to set the learning rate. For instance, we could start with a small rate, then rapidly ramp up and then decrease it again, albeit more slowly. We could even alternate between smaller and larger learning rates. There exists a large variety of such schedules. For now let us focus on learning rate schedules for which a comprehensive theoretical analysis is possible, i.e., on learning rates in a convex setting. For general nonconvex problems it is very difficult to obtain meaningful convergence guarantees, since in general minimizing nonlinear nonconvex problems is NP hard. For a survey see e.g., the excellent lecture notes of Tibshirani 2015.

## 11.4.3. Convergence Analysis for Convex Objectives¶

The following convergence analysis of stochastic gradient descent for convex objective functions is optional and primarily serves to convey more intuition about the problem. We limit ourselves to one of the simplest proofs [Nesterov & Vial, 2000]. Significantly more advanced proof techniques exist, e.g., whenever the objective function is particularly well behaved.

Suppose that the objective function \(f(\boldsymbol{\xi}, \mathbf{x})\) is convex in \(\mathbf{x}\) for all \(\boldsymbol{\xi}\). More concretely, we consider the stochastic gradient descent update:

where \(f(\boldsymbol{\xi}_t, \mathbf{x})\) is the objective function with respect to the training example \(\boldsymbol{\xi}_t\) drawn from some distribution at step \(t\) and \(\mathbf{x}\) is the model parameter. Denote by

the expected risk and by \(R^*\) its minimum with regard to \(\mathbf{x}\). Last let \(\mathbf{x}^*\) be the minimizer (we assume that it exists within the domain where \(\mathbf{x}\) is defined). In this case we can track the distance between the current parameter \(\mathbf{x}_t\) at time \(t\) and the risk minimizer \(\mathbf{x}^*\) and see whether it improves over time:

We assume that the \(L_2\) norm of stochastic gradient \(\partial_\mathbf{x} f(\boldsymbol{\xi}_t, \mathbf{x})\) is bounded by some constant \(L\), hence we have that

We are mostly interested in how the distance between
\(\mathbf{x}_t\) and \(\mathbf{x}^*\) changes *in expectation*.
In fact, for any specific sequence of steps the distance might well
increase, depending on whichever \(\boldsymbol{\xi}_t\) we
encounter. Hence we need to bound the dot product. Since for any convex
function \(f\) it holds that
\(f(\mathbf{y}) \geq f(\mathbf{x}) + \langle f'(\mathbf{x}), \mathbf{y} - \mathbf{x} \rangle\)
for all \(\mathbf{x}\) and \(\mathbf{y}\), by convexity we have

Plugging both inequalities (11.4.9) and (11.4.10) into (11.4.8) we obtain a bound on the distance between parameters at time \(t+1\) as follows:

This means that we make progress as long as the difference between
current loss and the optimal loss outweighs \(\eta_t L^2/2\). Since
this difference is bound to converge to zero it follows that the
learning rate \(\eta_t\) also needs to *vanish*.

Next we take expectations over (11.4.11). This yields

The last step involves summing over the inequalities for \(t \in \{1, \ldots, T\}\). Since the sum telescopes and by dropping the lower term we obtain

Note that we exploited that \(\mathbf{x}_1\) is given and thus the expectation can be dropped. Last define

Since

by Jensen’s inequality (setting \(i=t\), \(\alpha_i = \eta_t/\sum_{t=1}^T \eta_t\) in (11.2.3)) and convexity of \(R\) it follows that \(E[R(\mathbf{x}_t)] \geq E[R(\bar{\mathbf{x}})]\), thus

Plugging this into the inequality (11.4.13) yields the bound

where \(r^2 \stackrel{\mathrm{def}}{=} \|\mathbf{x}_1 - \mathbf{x}^*\|^2\) is a bound on the distance between the initial choice of parameters and the final outcome. In short, the speed of convergence depends on how the norm of stochastic gradient is bounded (\(L\)) and how far away from optimality the initial parameter value is (\(r\)). Note that the bound is in terms of \(\bar{\mathbf{x}}\) rather than \(\mathbf{x}_T\). This is the case since \(\bar{\mathbf{x}}\) is a smoothed version of the optimization path. Whenever \(r, L\), and \(T\) are known we can pick the learning rate \(\eta = r/(L \sqrt{T})\). This yields as upper bound \(rL/\sqrt{T}\). That is, we converge with rate \(\mathcal{O}(1/\sqrt{T})\) to the optimal solution.

## 11.4.4. Stochastic Gradients and Finite Samples¶

So far we have played a bit fast and loose when it comes to talking about stochastic gradient descent. We posited that we draw instances \(x_i\), typically with labels \(y_i\) from some distribution \(p(x, y)\) and that we use this to update the model parameters in some manner. In particular, for a finite sample size we simply argued that the discrete distribution \(p(x, y) = \frac{1}{n} \sum_{i=1}^n \delta_{x_i}(x) \delta_{y_i}(y)\) for some functions \(\delta_{x_i}\) and \(\delta_{y_i}\) allows us to perform stochastic gradient descent over it.

However, this is not really what we did. In the toy examples in the
current section we simply added noise to an otherwise non-stochastic
gradient, i.e., we pretended to have pairs \((x_i, y_i)\). It turns
out that this is justified here (see the exercises for a detailed
discussion). More troubling is that in all previous discussions we
clearly did not do this. Instead we iterated over all instances *exactly
once*. To see why this is preferable consider the converse, namely that
we are sampling \(n\) observations from the discrete distribution
*with replacement*. The probability of choosing an element \(i\) at
random is \(1/n\). Thus to choose it *at least* once is

A similar reasoning shows that the probability of picking some sample
(i.e., training example) *exactly once* is given by

This leads to an increased variance and decreased data efficiency
relative to sampling *without replacement*. Hence, in practice we
perform the latter (and this is the default choice throughout this
book). Last note that repeated passes through the training dataset
traverse it in a *different* random order.

## 11.4.5. Summary¶

For convex problems we can prove that for a wide choice of learning rates stochastic gradient descent will converge to the optimal solution.

For deep learning this is generally not the case. However, the analysis of convex problems gives us useful insight into how to approach optimization, namely to reduce the learning rate progressively, albeit not too quickly.

Problems occur when the learning rate is too small or too large. In practice a suitable learning rate is often found only after multiple experiments.

When there are more examples in the training dataset, it costs more to compute each iteration for gradient descent, so stochastic gradient descent is preferred in these cases.

Optimality guarantees for stochastic gradient descent are in general not available in nonconvex cases since the number of local minima that require checking might well be exponential.

## 11.4.6. Exercises¶

Experiment with different learning rate schedules for stochastic gradient descent and with different numbers of iterations. In particular, plot the distance from the optimal solution \((0, 0)\) as a function of the number of iterations.

Prove that for the function \(f(x_1, x_2) = x_1^2 + 2 x_2^2\) adding normal noise to the gradient is equivalent to minimizing a loss function \(f(\mathbf{x}, \mathbf{w}) = (x_1 - w_1)^2 + 2 (x_2 - w_2)^2\) where \(\mathbf{x}\) is drawn from a normal distribution.

Compare convergence of stochastic gradient descent when you sample from \(\{(x_1, y_1), \ldots, (x_n, y_n)\}\) with replacement and when you sample without replacement.

How would you change the stochastic gradient descent solver if some gradient (or rather some coordinate associated with it) was consistently larger than all the other gradients?

Assume that \(f(x) = x^2 (1 + \sin x)\). How many local minima does \(f\) have? Can you change \(f\) in such a way that to minimize it one needs to evaluate all the local minima?