Tuesday, February 13, 2018

Automatic Learning Rate Scheduling That Really Works

Training deep learning models can be a pain. In particular, there is this perception that one of the reasons it's a pain is because you have to fiddle with learning rates. For example, arguably the most popular strategy for setting learning rates looks like this:
  1. Run vanilla stochastic gradient descent with momentum and a fixed learning rate
  2. Wait for the loss to stop improving
  3. Reduce the learning rate
  4. Go back to step 1 or stop if the learning rate is really small
Many papers reporting state-of-the-art results do this. There have been a lot of other methods proposed, like ADAM, but I've always found the above procedure to work best. This is a common finding. The only fiddly part of this procedure is the "wait for the loss to stop improving" step. A lot of people just eyeball a plot of the loss and manually intervene when it looks like its flattened out. Or worse, they pick a certain number of iterations ahead of time and blindly stop when that limit is reached. Both of these ways of deciding when to reduce the learning rate suck. 

Fortunately, there is a simple method from classical statistics we can use to decide if the loss is still improving, and thus, when to reduce it. With this method it's trivial to fully automate the above procedure. In fact, it's what I've used to train all the public DNN models in dlib over the last few years: e.g. face detection,  face recognition, vehicle detection, and imagenet classification. It's the default solving strategy used by dlib's DNN solver. The rest of this blog post explains how it works.

Fundamentally, what we need is a method that takes a noisy time series of $n$ loss values, $Y=\{y_0,y_1,y_2,\dots,y_{n-1}\}$, and tells us if the time series is trending down or not. To do this, we model the time series as a noisy line corrupted by Gaussian noise:
\newcommand{\N} {\mathcal{N} } y_i = m\times i + b + \epsilon
\] Here, $m$ and $b$ are the unknown true slope and intercept parameters of the line, and $\epsilon$ is a Gaussian noise term with mean 0 and variance $\sigma^2$. Let's also define the function $\text{slope}(Y)$ that takes in a time series, performs OLS, and outputs the OLS estimate of $m$, the slope of the line. You can then ask the following question: what is the probability that a time series sampled from our noisy line model will have a negative slope according to OLS? That is, what is the value of?
P(\text{slope}(Y) < 0)
\]If we could compute an estimate of $P(\text{slope}(Y)<0)$ we could use it to test if the loss is still decreasing. Fortunately, computing the above quantity turns out to be easy. In fact, $\text{slope}(Y)$ is a Gaussian random variable with this distribution:
\text{slope}(Y) \sim \N\left(m, \frac{12 \sigma^2}{n^3-n}\right)
\]We don't know the true values of $m$ and $\sigma^2$, but they are easily estimated from data. We can obviously use $\text{slope}(Y)$ to estimate $m$. As for $\sigma^2$, it's customary to estimate it like this:
\[ \sigma^2 = \frac{1}{n-2} \sum_{i=0}^{n-1} (y_i - \hat y_i)^2 \] which gives an unbiased estimate of the true $\sigma^2$. Here $y_i - \hat y_i$ is the difference between the observed time series value at time $i$ and the value predicted by the OLS fitted line at time $i$. I should point out that none of this is new stuff, in fact, these properties of OLS are discussed in detail on the Wikipedia page about OLS.

So let's recap. We need a method to decide if the loss is trending downward or not. I'm suggesting that you use $P(\text{slope}(Y) < 0)$, the probability that a line fit to your loss curve will have negative slope. Moreover, as discussed above, this probability is easy to compute since it's just a question about a simple Gaussian variable and the two parameters of the Gaussian variable are given by a straightforward application of OLS.

You should also note that the variance of $\text{slope}(Y)$ decays at the very quick rate of $O(1/n^3)$, where $n$ is the number of loss samples. So it becomes very accurate as the length of the time series grows. To illustrate just how accurate this is, let's look at some examples. The figure below shows four different time series plots, each consisting of $n=4000$ points. Each plot is a draw from our noisy line model with parameters: $b=0$, $\sigma^2=1$, and $m \in \{0.001, 0.00004, -0.00004, -0.001\}$. For each of these noisy plots I've computed $P(\text{slope}(Y) < 0)$ and included it in the title.

From looking at these plots it should be obvious that $P(\text{slope}(Y) < 0)$ is quite good at detecting the slope. In particular, I doubt you can tell the difference between the two middle plots (the ones with slopes -0.00004 and 0.00004). But as you can see, the test statistic I'm suggesting, $P(\text{slope}(Y) < 0)$, has no trouble at all correctly identifying one as sloping up and the other as sloping down.

I find that a nice way to parameterize this in actual code is to count the number of mini-batches that executed while $P(\text{slope}(Y) < 0) < 0.51$. That is, find out how many loss values you have to look at before there is evidence the loss has been decreasing. To be very clear, this bit of pseudo-code implements the idea:
    def count_steps_without_decrease(Y):
        steps_without_decrease = 0
        n = len(Y)
        for i in reversed(range(n)):
            if P(slope(Y[i:n]) < 0) < 0.51:
                steps_without_decrease = n-i
        return steps_without_decrease
You can then use a rule like: "if the steps without decrease is 1000 I will lower the learning rate by 10x". However, there is one more issue that needs to be addressed. This is the fact that loss curves sometimes have really large transient spikes, where, for one reason or another (e.g. maybe a bad mini-batch) the loss will suddenly become huge for a moment. Not all models or datasets have this problem during training, but some do. In these cases, count_steps_without_decrease() might erroneously return a very large value. You can deal with this problem by discarding the top 10% of loss values inside count_steps_without_decrease(). This makes the entire procedure robust to these noisy outliers. Note, however, that the final test you would want to use is:
count_steps_without_decrease(Y) > threshold and count_steps_without_decrease_robust(Y) > threshold
That is, perform the check with and without outlier discarding. You need both checks because the 10% largest loss values might have occurred at the very beginning of Y. For example, maybe you are waiting for 1000 (i.e. threshold=1000) mini-batches to execute without showing evidence of the loss going down. And maybe the first 100 all showed a dropping loss while the last 900 were flat. The check that discarded the top 10% would erroneously indicate that the loss was NOT dropping. So you want to perform both checks and if both agree that the loss isn't dropping then you can be confident it's not dropping.

It should be emphasized that this method isn't substantively different from what a whole lot of people already do when training deep neural networks. The only difference here is that the "look at the loss and see if it's decreasing" step is being done by a computer. The point of this blog post is to point out that this check is trivially automatable with boring old simple statistics. There is no reason to do it by hand. Let the computer do it and find something more productive to do with your time than babysitting SGD. The test is simple to implement yourself, but if you want to just call a function you can call dlib's count_steps_without_decrease() and count_steps_without_decrease_robust() routines from C++ or Python.

Finally, one more useful thing you can do is the following: you can periodically check if $P(\text{slope}(Y) > 0) \gt 0.99$, that is, check if we are really certain that the loss is going up, rather than down. This can happen and I've had training runs that were going fine and then suddenly the loss shot up and stayed high for a really long time, basically ruining the training run. This doesn't seem to be too much of an issue with simple losses like the log-loss. However, structured loss functions that perform some kind of hard negative mining inside a mini-batch will sometimes go haywire if they hit a very bad mini-batch. You can fix this problem by simply reloading from an earlier network state before the loss increased. But to do this you need a reliable way to measure "the loss is going up" and $P(\text{slope}(Y) > 0) \gt 0.99$ is excellent for this task. This idea is called backtracking and has a long history in numerical optimization. Backtracking significantly increases solver robustness in many cases and is well worth using.

Sunday, January 14, 2018

Correctly Mirroring Datasets

I get asked a lot of questions about dlib's landmarking tools. Some of the most common questions are about how to prepare a good training dataset. One of the most useful tricks for creating a dataset is to mirror the data, since this effectively doubles the amount of training data. However, if you do this naively you end up with a terrible training dataset that produces really awful landmarking models. Some of the most common questions I get are about why this is happening.

To understand the issue, consider the following image of an annotated face from the iBug W-300 dataset:

Since the mirror image of a face is still a face, we can mirror images like this to get more training data. However, what happens if you simply mirror the annotations? You end up with the wrong annotation labels! To see this, take a look at the figure below. The left image shows what happens if you naively mirror the above image and its landmarks. Note, for instance, that the points along the jawline are now annotated in reverse order. In fact, nearly all the annotations in the left image are wrong. Instead, you want to match the source image's labeling scheme. A mirrored image with the correct annotations is shown on the right.

Dlib's imglab tool has had a --flip option for a long time that would mirror a dataset for you. However, it used naive mirroring and it was left up to the user to adjust any landmark labels appropriately. Many users found this confusing, so in the new version of imglab (v1.13) the --flip command now performs automatic source label matching using a 2D point registration algorithm. That is, it left-right flips the dataset and annotations. Then it registers the mirrored landmarks with the original landmarks and transfers labels appropriately. In fact, the "source label matching" image on the right was created by the new version of imglab.

Finally, just to be clear, the point registration algorithm will work on anything. It doesn't have to be iBug's annotations. It doesn't have to be faces. It's a general point registration method that will work correctly for any kind of landmark annotated data with left-right symmetry. However, if you want the old --flip behavior you can use the new --flip-basic to get a naive mirroring. But most users will want to use the new --flip.