# User Guide¶

You are probably reading this guide because you have a problem.

There are many problems in the world, and many ways of thinking about solving them. Happily, some—many—problems can be described mathematically using a “loss” function, which takes a potential solution for your problem and returns a single number indicating how terrible that solution is.

If you can express your problem using a loss function, then it’s possible—even likely—that you can then use a computer to solve your problem for you. This is what downhill does: given a computational formulation of a loss, the optimization routines in downhill can compute a series of ever-better solutions to your problem.

This guide describes how that works.

## Creating a Loss¶

Many types of problems can be formulated in terms of a scalar “loss” function that ought to be minimized. The “loss” for a problem:

• is computed with respect to a potential solution to a problem, and
• is a scalar quantity—just a single number.

A few examples of problems and their associated losses might include:

• Categorizing pictures into “elephants” versus “acrobats”; the loss might be the number of mistakes that are made on a given set of test pictures.
• Allocating funds to provide a given set of public services; the loss might be the monetary cost of the budget.
• Computing the actions of a robot to achieve a goal; the loss might be the total energy consumed.

This guide will use linear regression as a running example. Suppose you’ve made some measurements of, say, the sizes and prices of various houses for sale where you live. You want to describe the relationship between the size (let’s represent it as $$x_i$$) and the price ($$y_i$$) by fitting a line to the measurements you’ve made.

So you need to take the data points that you collected and somehow use them to compute a slope $$m$$ and an intercept $$b$$ such that the resulting line $$y = m x + b$$ passes as closely as possible to your data points. In this example, the loss $$\mathcal{L}$$ might be expressed as the sum of the differences between the values on the line and the observed data:

$\mathcal{L}(m,b) = \sum_{i=1}^N ( m x_i + b - y_i )^2$

### Using Theano¶

Well, you’ve formulated a loss for this regression problem. Now it’s time to use downhill to minimize it, right?

Not so fast ... the downhill package provides routines for optimizing scalar loss functions, but there’s a catch: the loss functions must be defined using Theano, a Python framework for describing computation graphs. Theano takes a bit of getting used to, but we’ll walk through the linear regression example here; if you’re curious, there are also lots of good tutorials on the Theano site.

To use Theano with downhill, you need to define shared variables for each of the parameters in your model, and symbolic inputs for the data that you’ll use to evaluate your loss. We’ll start with the shared variables:

import downhill
import numpy as np
import theano
import theano.tensor as TT

m = theano.shared(np.ones((1, ), 'f'), name='m')
b = theano.shared(np.zeros((1, ), 'f'), name='b')


This sets up a vector with one 1 for $$m$$, and a vector with one 0 for $$b$$. The values contained inside these shared variables will be adjusted automatically by the optimization algorithms in downhill.

Next, you need to define symbols that represent the data needed to compute the loss:

x = TT.vector('x')
y = TT.vector('y')


These symbolic vectors represent the inputs—the house sizes $$[x_1 \dots x_N]$$ and prices $$[y_1 \dots y_N]$$—needed to compute the loss. Finally, having created all of these symbolic variables, you can define the loss itself:

loss = TT.sqr(m * x + b - y).sum()


This tells Theano to multiply the data vector x by the value stored in the shared m variable, add the value stored in the shared b variable, and then subtract the data vector y. Then that vector gets squared elementwise, and all of the components of the result get summed up to produce the loss.

Note that none of these operations have actually been computed; instead, you’ve instructed Theano how to compute the loss, if you were to give it some values for x and y. This is the bizarre thing about Theano: it looks like you’re computing things, but you’re actually just telling the computer how to compute things in the future.

## Minimizing a Loss¶

The downhill package provides a single high-level function, downhill.minimize(), that can be used as a black-box optimizer for losses. In addition, there are lower-level calls that provide more control over the interaction between your code and downhill. First, we’ll look at the high-level minimize function, then we’ll talk about what happens under the hood.

Once you’ve defined your loss using Theano, you can minimize it with a single function call. Here, we’ll minimize the loss defined above:

downhill.minimize(loss, [sizes, prices], inputs=[x, y])


You just specify the loss to minimize, provide some data to use for computing the loss, and identify the symbolic inputs that the loss requires. The downhill code will select an optimization algorithm (the default is currently RMSProp), identify shared variables in the loss that need optimization, and run the optimization process to completion. After the minimization has finished, the shared variables in your loss will be updated to their optimal values. You can retrieve their values using any of the methods of shared variables:

m_value, b_value = m.get_value(), b.get_value()


There is much to say about providing data—see Providing Data for more information—but briefly, the data you will need to provide is typically a list of numpy arrays of the measurements you’ve made for your problem. For the house price regression example, the arrays for house size and house price might be set up like this:

sizes = np.array([1200, 2013, 8129, 2431, 2211])
prices = np.array([103020, 203310, 3922013, 224321, 449020])


### Training and Validation¶

You might have noticed that the formulation of the loss given at the top of this guide contains a sum over all of the data points that you’ve observed $$(x_i, y_i)$$. (For the house price example, these data are stored in the sizes and prices arrays.) This is a very common state of affairs for many problems: the loss is computed thanks to observed data.

But for a typical regression problem, it’s not feasible or even possible to gather all of the relevant data—either it’s too expensive to do that, or there might be new data created in the future that you don’t have any way of predicting.

Given this paucity of data, you’re running a risk in using a stochastic optimizer to solve your problem: the data that you have collected might not be representative of the data that you haven’t collected! If the data you collected are quite different from the “true” data out there in the world, then when you optimize your loss, the optimal model might be skewed toward your dataset, and your model might not perform well on new, “unseen” data.

This problem is generally referred to as overfitting and is a risk with many types of models. Generally the risk of overfitting increases with the complexity of your model, and also increases when you don’t have a lot of data.

There are many ways to combat overfitting:

• You can tighten your belt and gather more data, which increases the chance that the data you do have will be representative of data you don’t yet have.
• You can regularize your loss; this tends to encourage some solutions to your problem (e.g., solutions with small parameter values) and discourage others (e.g., solutions that “memorize” outliers).
• You can also set aside a bit of the data you’ve collected as a “validation” set. You can use this set to stop the optimization process when the performance of your model on the validation set stops improving—this is known as “early stopping.”

Collecting more data is almost always a good idea, as long as you can afford to do so (whether in terms of time, monetary cost, etc.)—but downhill can’t help you with that. And while it can often be a good idea to incorporate regularizers into your loss, doing so is something of an art and remains outside the scope of downhill.

### Early Stopping¶

The algorithms in downhill implement the “early stopping” regularization method. To take advantage of it, just provide a second set of data when minimizing your loss:

downhill.minimize(loss, [sizes, prices], [valid_sizes, valid_prices])


Here we’ll assume that you’ve gathered another few sizes and prices and put them in a new pair of numpy arrays. In practice, the validation dataset can also just be a small bit (10% or so) of the training data you’ve collected. Either way, it’s important to make sure the validation data is disjoint from the training data, to ensure the most accurate predictions on unseen data. The idea is that you want to use a small part of the data you’ve gathered as a sort of canary to guess when the performance of your model will be good when you actually take it out into the world and use it.

The early stopping method will cause optimization to halt when the loss stops improving on the validation dataset. If you do not specify a validation dataset, the training dataset will also be used for validation, which effectively disables early stopping—that is, optimization will halt whenever the loss computed on the training dataset stops improving.

To understand this better, we’ll take a look at the lower-level API provided by downhill.

### Iterative Optimization¶

The downhill.minimize() function is actually just a wrapper that performs a few common lower-level tasks to optimize your loss. These tasks include:

You can perform these tasks yourself to retain more control over the optimization process, but even if you don’t, it’s useful to follow the process to know how it works. In practice it can often be useful to call the iterate() method yourself, because it gives you access to the state of the optimizer at each step.

opt = downhill.build('rmsprop', loss=loss, inputs=[x, y])
train = downhill.Dataset([sizes, prices])
valid = downhill.Dataset([valid_sizes, valid_prices])
for tm, vm in opt.iterate(train, valid):
print('training loss:', tm['loss'])
print('most recent validation loss:', vm['loss'])


This code constructs an Optimizer object (specifically, an RMSProp optimizer), wraps the input data with a Dataset, and then steps through the optimization process iteratively.

Notice that after each iteration, the optimizer yields a pair of dictionaries to the caller: the first dictionary contains measured values of the loss on the training data during that iteration, and the second contains measured values of the loss on the validation data.

The keys and values in each of these dictionaries give the costs and monitors that are computed during optimization. There will always be a 'loss' key in each dictionary that gives the value of the loss function being optimized. In addition, any monitor values that were defined when creating the optimizer will also be provided in these dictionaries.

### Batches and Epochs¶

During each iteration, the optimizer instance processes training data in small pieces called “mini-batches”; each mini-batch is used to compute a gradient estimate for the loss, and the parameters are updated by a small amount. After a fixed number of mini-batches have been processed, the iterate method yields the loss dictionaries to the caller.

Each group of parameter updates processed during a single iteration is called an “epoch.” After a fixed number of epochs have taken place, the loss is then evaluated using a fixed number of mini-batches from the validation dataset, and this result is saved as the validation dictionary after every epoch until the next validation happens.

Optimization epochs continue to occur, with occasional validations, until the loss on the validation dataset fails to make sufficient progress for long enough. Optimization halts at that point.

There are a number of hyperparameters involved in this process, which can be tuned for the best performance on your problem.

## Tuning¶

The downhill package provides several ways of tuning the optimization process. There are many different settings for mini-batch optimization and validation, many optimization algorithms are available, and there are also several common learning hyperparameters that might require tuning.

### Batch Parameters¶

All algorithms in downhill provide early stopping and use epoch-based optimization as described above. This process is controlled by a number of parameters that can be tweaked for your optimization problem.

The size of a minibatch is controlled using the batch_size parameter when you create a Dataset. To build mini-batches containing 3 pieces of data, for example:

train = downhill.Dataset([sizes, prices], batch_size=3)


If you call the high-level downhill.minimize() method directly, you can pass batch_size to it directly:

downhill.minimize(loss, [sizes, prices], batch_size=3)


The number of mini-batches that are processed during a single training epoch is controlled by the iteration_size parameter when constructing a Dataset:

train = downhill.Dataset([sizes, prices], iteration_size=10)


This will ensure that one iteration loop over the training dataset will produce 10 mini-batches. If you have fewer than batch_size times iteration_size pieces of data, the Dataset class will loop over your data multiple times to ensure that the desired number of batches is processed. (The Dataset class also handles shuffling your data as needed during iteration, to avoid issues that can come up when presenting data to the model in a fixed order.)

If you call the high-level downhill.minimize() method, the numbers of training and validation mini-batches processed per epoch are set using the train_batches and valid_batches parameters, respectively:

downhill.minimize(..., train_batches=10, valid_batches=8)


Finally, a validation takes place after a fixed number of training epochs have happened. This number is set using the validate_every parameter; for example, to validate the loss every 5 training epochs:

downhill.minimize(..., validate_every=5)


If you are processing data using the lower-level API, the validate_every parameter is passed directly to iterate():

for tm, vm in opt.iterate(..., validate_every=5):
# ...


### Patience and Improvement¶

The training process halts if there is “insufficient” progress on the validation loss for “long enough.” The precise meanings of these terms are given by the min_improvement and patience parameters, respectively.

The min_improvement parameter specifies the minimum relative improvement of the validation loss that counts as progress in the optimization. If min_improvement is set to 0, for example, then any positive improvement in the validation loss will count as progress, while if min_improvement is set to 0.1, then the validation loss must improve by 10% relative to the current best validation loss before the validation attempt counts as progress.

The patience parameter specifies the number of failed validation attempts that you are willing to tolerate before seeing any progress. If patience is set to 0, for instance, then optimization will halt as soon as a validation attempt fails to make min_improvement relative loss improvement over the best validation loss so far. If patience is set to 3, then optimization will continue through three failed validation attempts, but if the fourth validation attempt fails, then optimization will halt.

These parameters can be set either on a call to the high-level downhill.minimize() function:

downhill.minimize(..., patience=3, min_improvement=0.1)


or when calling iterate():

for tm, vm in opt.iterate(..., patience=3, min_improvement=0.1):
# ...


### Optimization Algorithms¶

The following algorithms are currently available in downhill:

To select an algorithm, specify its name using the algo keyword argument:

downhill.minimize(..., algo='adadelta')


or pass the algorithm name to build an Optimizer instance:

opt = downhill.build('adadelta', ...)


Different algorithms have different performance characteristics, different numbers of hyperparameters to tune, and different suitability for particular problems. In general, several of the the adaptive procedures seem to work well across different problems, particularly Adam, ADADELTA, and RMSProp. NAG also seems to work quite well, but can sometimes take longer to converge.

Many of these algorithms, being based on stochastic gradient descent, rely on a common set of hyperparameters that control the speed of convergence and the reliability of the optimization process over time; these parameters are discussed next.

### Learning Rate¶

Most stochastic gradient optimization methods make small parameter updates based on the local gradient of the loss at each step in the optimization procedure. Intuitively, parameters in a model are updated by subtracting a small portion of the local derivative from the current parameter value. Mathematically, this is written as:

$\theta_{t+1} = \theta_t - \alpha \left. \frac{\partial\mathcal{L}}{\partial\theta} \right|_{\theta_t}$

where $$\mathcal{L}$$ is the loss function being optimized, $$\theta$$ is the value of a parameter in the model (e.g., $$m$$ or $$b$$ for the regression problem) at optimization step $$t$$, $$\alpha$$ is the learning rate, and $$\frac{\partial\mathcal{L}}{\partial\theta}$$ (also often written $$\nabla_{\theta_t}\mathcal{L}$$) is the partial derivative of the loss with respect to the parameters, evaluated at the current value of those parameters.

The learning rate $$\alpha$$ specifies the scale of these parameter updates with respect to the magnitude of the gradient. Almost all stochastic optimizers use a fixed learning rate parameter.

In downhill, the learning rate is passed as a keyword argument to downhill.minimize():

downhill.minimize(..., learning_rate=0.1)


Often the learning rate is set to a very small value—many approaches seem to start with values around 1e-4. If the learning rate is too large, the optimization procedure might “bounce around” in the loss landscape because the parameter steps are too large. If the learning rate is too small, the optimization procedure might not make progress quickly enough to make optimization practical.

### Momentum¶

Momentum is a common technique in stochastic gradient optimization algorithms that seems to accelerate the optimization process in most cases. Intuitively, momentum avoids “jitter” in the parameters during optimization by smoothing the estimates of the local gradient information over time. In practice a momentum method maintains a “velocity” of the most recent parameter steps and combines these recent individual steps together when making a parameter update. Mathematically, this is written:

$\begin{split}\begin{eqnarray*} \nu_{t+1} &=& \mu \nu_t - \alpha \left. \frac{\partial\mathcal{L}}{\partial\theta} \right|_{\theta_t} \\ \theta_{t+1} &=& \theta_t + \nu_{t+1} \end{eqnarray*}\end{split}$

where the symbols are the same as above, and additionally $$\nu$$ describes the “velocity” of parameter $$\theta$$, and $$\mu$$ is the momentum hyperparameter. The gradient computations using momentum are exactly the same as when not using momentum; the only difference is the accumulation of recent updates in the “velocity.”

In downhill, the momentum value is passed as a keyword argument to downhill.minimize():

downhill.minimize(..., momentum=0.9)


Typically momentum is set to a value in $$[0, 1)$$—when set to 0, momentum is disabled, and when set to values near 1, the momentum is very high, requiring several consecutive parameter updates in the same direction to change the parameter velocity.

In many problems it is useful to set the momentum to a surprisingly large value, sometimes even to values greater than 0.9. Such values can be especially effective with a relatively small learning rate.

If the momentum is set too low, then parameter updates will be more noisy and optimization might take longer to converge, but if the momentum is set too high, the optimization process might diverge entirely.

### Nesterov Momentum¶

More recently, a newer momentum technique has been shown to be even more performant than “traditional” momentum. This technique was originally proposed by Y. Nesterov and effectively amounts to computing the momentum value at a different location in the parameter space, namely the location where the momentum value would have placed the parameter after the current update:

$\begin{split}\begin{eqnarray*} \nu_{t+1} &=& \mu \nu_t - \alpha \left. \frac{\partial\mathcal{L}}{\partial\theta}\right|_{\theta_t + \mu\nu_t} \\ \theta_{t+1} &=& \theta_t + \nu_{t+1} \end{eqnarray*}\end{split}$

Note that the partial derivative is evaluated at $$\theta_t + \mu\nu_t$$ instead of at $$\theta_t$$. The intuitive rationale for this change is that if the momentum would have produced an “overshoot,” then the gradient at this overshot parameter value would point backwards, toward the previous parameter value, which would thus help correct oscillations during optimization.

To use Nesterov-style momentum, use either the NAG optimizer (which uses plain stochastic gradient descent with Nesterov momentum), or specify nesterov=True in addition to providing a nonzero momentum value when minimizing your loss:

downhill.minimize(..., momentum=0.9, nesterov=True)


Sometimes during the execution of a stochastic optimization routine—and particularly at the start of optimization, when the problem parameters are far from their optimal values—the gradient of the loss with respect to the parameters can be extremely large. In these cases, taking a step that is proportional to the magnitude of the gradient can actually be harmful, resulting in an unpredictable parameter change.

To prevent this from happening, but still preserve the iterative loss improvements when parameters are in a region with “more reasonable” gradient magnitudes, downhill implements two forms of “gradient clipping.”

The first gradient truncation method rescales the entire gradient vector if its L2 norm exceeds some threshold. This is accomplished using the max_gradient_norm hyperparameter:

downhill.minimize(..., max_gradient_norm=1)


The second gradient truncation method clips each element of the gradient vector individually. This is accomplished using the max_gradient_elem hyperparameter:

downhill.minimize(..., max_gradient_elem=1)


In both cases, gradients that are extremely large will still point in the correct direction, but their magnitudes will be rescaled to avoid steps that are too large. Gradients with values smaller than the thresholds (presumably, gradients near an optimum will be small) will not be affected. In both cases, the strategy of taking small steps proportional to the gradient seems to work.

## Providing Data¶

As described above, you’ll often need to provide data to downhill so that you can compute the loss and optimize the parameters for your problem. There are two ways of passing data to downhill: using arrays and using callables.

### Using Arrays¶

A fairly typical use case for optimizing a loss for a small-ish problem is to construct a numpy array containing the data you have:

dataset = np.load(filename)
downhill.minimize(..., train=dataset)


Sometimes the data available for optimizing a loss exceeds the available resources (e.g., memory) on the computer at hand. There are several ways of handling this type of situation. If your data are already in a numpy array stored on disk, you might want to try loading the array using mmap:

dataset = np.load(filename, mmap_mode='r')
downhill.minimize(..., train=dataset)


Alternatively, you might want to load just part of the data and train on that, then load another part and train on it:

for filename in filenames:
downhill.minimize(..., train=dataset)


Finally, you can potentially handle large datasets by using a callable to provide data to the optimization algorithm.

### Using Callables¶

Instead of an array of data, you can provide a callable for a Dataset. This callable must take no arguments and must return a list of numpy arrays of the proper shape for your loss.

During minimization, the callable will be invoked every time the optimization algorithm requires a batch of training (or validation) data. Therefore, your callable should return at least one array containing a batch of data; if your model requires multiple arrays per batch (e.g., if you are minimizing a loss that requires some “input” data as well as some “output” data), then your callable should return a list containing the correct number of arrays (e.g., an array of “inputs” and the corresponding “outputs”).

For example, this code defines a batch() helper that could be used for a loss that needs one input. The callable chooses a random dataset and a random offset for each batch:

SOURCES = 'foo.npy', 'bar.npy', 'baz.npy'
BATCH_SIZE = 64

def batch():
i = np.random.randint(len(X))
return X[i:i+BATCH_SIZE]

downhill.minimize(..., train=batch)


If you need to maintain more state than is reasonable from a single closure, you can also encapsulate the callable inside a class. Just make sure instances of the class are callable by defining the __call__ method. For example, this class loads data from a series of numpy arrays on disk, but only loads one of the on-disk arrays into memory at a given time:

class Loader:
def __init__(sources=('foo.npy', 'bar.npy', 'baz.npy'), batch_size=64):
self.sources = sources
self.batch_size = batch_size
self.src = -1
self.idx = 0
self.X = ()

def __call__(self):
if self.idx + self.batch_size > len(self.X):
self.idx = 0
self.src = (self.src + 1) % len(self.sources)
try:
return self.X[self.idx:self.idx+self.batch_size]
finally:
self.idx += self.batch_size



There are almost limitless possibilities for using callables to interface with the optimization process.

## Monitoring¶

Sometimes while optimizing a loss, it can be helpful to “see inside” the model. In a model with a sparsity regularizer, for example, having some idea of the current sparsity of the model can help diagnose when the model is “too sparse.”

In downhill you can provide a series of monitors during optimization that satisfy this need. Monitors must be a series of named Theano expressions that evaluate to scalars; this can be provided as a dictionary that maps names to expressions, or as a list of (name, expression) ordered pairs.

Suppose you want to monitor the slope and intercept values that your model is computing as it works its way through the house price modeling task. You can provide monitors for these quantities as follows:

downhill.minimize(
loss,
[sizes, prices],
inputs=[x, y],
monitors=[
('m', m.sum()),
('b', b.sum()),
])


The Theano expressions here are sums because the m and b shared variables are actually arrays of shared variables. (This also helps generalize the regression loss to situations where you might have multiple independent variables, like house size and number of bedrooms.) If you preferred to provide the monitor values as a dictionary, it would look like:

downhill.minimize(
loss,
[sizes, prices],
inputs=[x, y],
monitors=dict(m=m.sum(), b=b.sum()))


Note that if you construct an Optimizer directly, then you need to pass the monitors when you create your optimizer instance:

opt = downhill.build(
'nag', loss=loss, inputs=[sizes, prices],
monitors=dict(m=m.sum(), b=b.sum()))


Sometimes when setting parameters like learning_rate and max_gradient_norm, it can be quite useful to see how large the gradients of your model are. These quantities can be included in the monitors easily by setting the monitor_gradients flag:

downhill.minimize(
loss,
[sizes, prices],
inputs=[x, y],

This concludes the downhill guide! Have a good time harnessing the power of your GPU to optimize your scalar losses!