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Latent variable models attempt to capture hidden structure in high dimensional data. Examples include principle component analysis (PCA) and factor analysis. Gaussian processes are "non-parametric" models which can flexibly capture local correlation structure and uncertainty. The Gaussian process latent variable model (Lawrence, 2004) combines these concepts.

## Background: Gaussian Processes

A Gaussian process is any collection of random variables such that the marginal distribution over any finite subset is a multivariate normal distribution. For a detailed look at GPs in the context of regression, check out Gaussian Process Regression in TensorFlow Probability.

We use a so-called *index set* to label each of the random variables in the
collection that the GP comprises. In the case of a finite index set, we just
get a multivariate normal. GP's are most interesting, though, when we consider
*infinite* collections. In the case of index sets like \(\mathbb{R}^D\), where we
have a random variable for *every point in \(D\)-dimensional space*, the GP can be
thought of as a distribution over random *functions*. A single draw from such a
GP, if it could be realized, would assign a (jointly normally-distributed) value
to every point in \(\mathbb{R}^D\). In this colab, we'll focus on GP's over some
\(\mathbb{R}^D\).

Normal distributions are completely determined by their first and second order
statistics -- indeed, one way to define the normal distribution is as one whose
higher-order cumulants are all zero. This is the case for GP's, too: we completely
specify a GP by describing the mean and covariance^{*}. Recall that for
finite-dimensional multivariate normals, the mean is a vector and the covariance is a square,
symmetric positive-definite matrix. In the infinite-dimensional GP, these
structures generalize to a mean *function* \(m : \mathbb{R}^D \to \mathbb{R}\),
defined at each point of the index set, and a covariance "*kernel*" function,
\(k : \mathbb{R}^D \times \mathbb{R}^D \to \mathbb{R}\). The kernel
function is required to be positive-definite, which
essentially says that, restricted to a finite set of points, it yields a
postiive-definite matrix.

Most of the structure of a GP derives from its covariance kernel function -- this function describes how the values of sampeld functions vary across nearby (or not-so-nearby) points. Different covariance functions encourage different degrees of smoothness. One commonly used kernel function is the "exponentiated quadratic" (a.k.a., "gaussian", "squared exponential" or "radial basis function"), \(k(x, x') = \sigma^2 e^{(x - x^2) / \lambda^2}\). Other examples are outlined on David Duvenaud's kernel cookbook page, as well as in the canonical text Gaussian Processes for Machine Learning.

_{* With an infinite index set, we also require a consistency condition. Since
the definition of the GP is in terms of finite marginals, we must require that
these marginals are consistent irrespective of the order in which the
marginals are taken. This is a somewhat advanced topic in the theory of
stochastic processes, out of scope for this tutorial; suffice it to say things
work out ok in the end!}

## Applying GPs: Regression and Latent Variable Models

One way we can use GPs is for regression: given a bunch of observed data in the
form of inputs \(\{x_i\}_{i=1}^N\) (elements of the index set) and observations
\(\{y_i\}_{i=1}^N\), we can use these to form a posterior predictive distribution
at a new set of points \(\{x_j^*\}_{j=1}^M\). Since the distributions are all
Gaussian, this boils down to some straightforward linear algebra (but note: the
requisite computations have runtime *cubic* in the number of data points and
require space quadratic in the number of data points -- this is a major limiting
factor in the use of GPs and much current research focuses on computationally
viable alternatives to exact posterior inference). We cover GP regression in more
detail in the GP Regression in TFP colab.

Another way we can use GPs is as a latent variable model: given a collection of high-dimensional observations (e.g., images), we can posit some low-dimensional latent structure. We assume that, conditional on the latent structure, the large number of outputs (pixels in the image) are independent of each other. Training in this model consists of

- optimizing model parameters (kernel function parameters as well as, e.g., observation noise variance), and
- finding, for each training observation (image), a corresponding point location in the index set. All of the optimization can be done by maximizing the marginal log likelihood of the data.

## Imports

```
import numpy as np
import tensorflow as tf
import tf_keras
import tensorflow_probability as tfp
tfd = tfp.distributions
tfk = tfp.math.psd_kernels
%pylab inline
```

Populating the interactive namespace from numpy and matplotlib

## Load MNIST Data

```
# Load the MNIST data set and isolate a subset of it.
(x_train, y_train), (_, _) = tf_keras.datasets.mnist.load_data()
N = 1000
small_x_train = x_train[:N, ...].astype(np.float64) / 256.
small_y_train = y_train[:N]
```

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 11493376/11490434 [==============================] - 0s 0us/step 11501568/11490434 [==============================] - 0s 0us/step

## Prepare trainable variables

We'll be jointly training 3 model parameters as well as the latent inputs.

```
# Create some trainable model parameters. We will constrain them to be strictly
# positive when constructing the kernel and the GP.
unconstrained_amplitude = tf.Variable(np.float64(1.), name='amplitude')
unconstrained_length_scale = tf.Variable(np.float64(1.), name='length_scale')
unconstrained_observation_noise = tf.Variable(np.float64(1.), name='observation_noise')
```

```
# We need to flatten the images and, somewhat unintuitively, transpose from
# shape [100, 784] to [784, 100]. This is because the 784 pixels will be
# treated as *independent* conditioned on the latent inputs, meaning we really
# have a batch of 784 GP's with 100 index_points.
observations_ = small_x_train.reshape(N, -1).transpose()
# Create a collection of N 2-dimensional index points that will represent our
# latent embeddings of the data. (Lawrence, 2004) prescribes initializing these
# with PCA, but a random initialization actually gives not-too-bad results, so
# we use this for simplicity. For a fun exercise, try doing the
# PCA-initialization yourself!
init_ = np.random.normal(size=(N, 2))
latent_index_points = tf.Variable(init_, name='latent_index_points')
```

## Construct model and training ops

```
# Create our kernel and GP distribution
EPS = np.finfo(np.float64).eps
def create_kernel():
amplitude = tf.math.softplus(EPS + unconstrained_amplitude)
length_scale = tf.math.softplus(EPS + unconstrained_length_scale)
kernel = tfk.ExponentiatedQuadratic(amplitude, length_scale)
return kernel
def loss_fn():
observation_noise_variance = tf.math.softplus(
EPS + unconstrained_observation_noise)
gp = tfd.GaussianProcess(
kernel=create_kernel(),
index_points=latent_index_points,
observation_noise_variance=observation_noise_variance)
log_probs = gp.log_prob(observations_, name='log_prob')
return -tf.reduce_mean(log_probs)
trainable_variables = [unconstrained_amplitude,
unconstrained_length_scale,
unconstrained_observation_noise,
latent_index_points]
optimizer = tf_keras.optimizers.Adam(learning_rate=1.0)
@tf.function(autograph=False, jit_compile=True)
def train_model():
with tf.GradientTape() as tape:
loss_value = loss_fn()
grads = tape.gradient(loss_value, trainable_variables)
optimizer.apply_gradients(zip(grads, trainable_variables))
return loss_value
```

## Train and plot the resulting latent embeddings

```
# Initialize variables and train!
num_iters = 100
log_interval = 20
lips = np.zeros((num_iters, N, 2), np.float64)
for i in range(num_iters):
loss = train_model()
lips[i] = latent_index_points.numpy()
if i % log_interval == 0 or i + 1 == num_iters:
print("Loss at step %d: %f" % (i, loss))
```

Loss at step 0: 1108.121688 Loss at step 20: -159.633761 Loss at step 40: -263.014394 Loss at step 60: -283.713056 Loss at step 80: -288.709413 Loss at step 99: -289.662253

## Plot results

```
# Plot the latent locations before and after training
plt.figure(figsize=(7, 7))
plt.title("Before training")
plt.grid(False)
plt.scatter(x=init_[:, 0], y=init_[:, 1],
c=y_train[:N], cmap=plt.get_cmap('Paired'), s=50)
plt.show()
plt.figure(figsize=(7, 7))
plt.title("After training")
plt.grid(False)
plt.scatter(x=lips[-1, :, 0], y=lips[-1, :, 1],
c=y_train[:N], cmap=plt.get_cmap('Paired'), s=50)
plt.show()
```

## Construct predictive model and sampling ops

```
# We'll draw samples at evenly spaced points on a 10x10 grid in the latent
# input space.
sample_grid_points = 10
grid_ = np.linspace(-4, 4, sample_grid_points).astype(np.float64)
# Create a 10x10 grid of 2-vectors, for a total shape [10, 10, 2]
grid_ = np.stack(np.meshgrid(grid_, grid_), axis=-1)
# This part's a bit subtle! What we defined above was a batch of 784 (=28x28)
# independent GP distributions over the input space. Each one corresponds to a
# single pixel of an MNIST image. Now what we'd like to do is draw 100 (=10x10)
# *independent* samples, each one separately conditioned on all the observations
# as well as the learned latent input locations above.
#
# The GP regression model below will define a batch of 784 independent
# posteriors. We'd like to get 100 independent samples each at a different
# latent index point. We could loop over the points in the grid, but that might
# be a bit slow. Instead, we can vectorize the computation by tacking on *even
# more* batch dimensions to our GaussianProcessRegressionModel distribution.
# In the below grid_ shape, we have concatentaed
# 1. batch shape: [sample_grid_points, sample_grid_points, 1]
# 2. number of examples: [1]
# 3. number of latent input dimensions: [2]
# The `1` in the batch shape will broadcast with 784. The final result will be
# samples of shape [10, 10, 784, 1]. The `1` comes from the "number of examples"
# and we can just `np.squeeze` it off.
grid_ = grid_.reshape(sample_grid_points, sample_grid_points, 1, 1, 2)
# Create the GPRegressionModel instance which represents the posterior
# predictive at the grid of new points.
gprm = tfd.GaussianProcessRegressionModel(
kernel=create_kernel(),
# Shape [10, 10, 1, 1, 2]
index_points=grid_,
# Shape [1000, 2]. 1000 2 dimensional vectors.
observation_index_points=latent_index_points,
# Shape [784, 1000]. A batch of 784 1000-dimensional observations.
observations=observations_)
```

## Draw samples conditioned on the data and latent embeddings

We sample at 100 points on a 2-d grid in the latent space.

```
samples = gprm.sample()
# Plot the grid of samples at new points. We do a bit of tweaking of the samples
# first, squeezing off extra 1-shapes and normalizing the values.
samples_ = np.squeeze(samples.numpy())
samples_ = ((samples_ -
samples_.min(-1, keepdims=True)) /
(samples_.max(-1, keepdims=True) -
samples_.min(-1, keepdims=True)))
samples_ = samples_.reshape(sample_grid_points, sample_grid_points, 28, 28)
samples_ = samples_.transpose([0, 2, 1, 3])
samples_ = samples_.reshape(28 * sample_grid_points, 28 * sample_grid_points)
plt.figure(figsize=(7, 7))
ax = plt.subplot()
ax.grid(False)
ax.imshow(-samples_, interpolation='none', cmap='Greys')
plt.show()
```

## Conclusion

We've taken a brief tour of the Gaussian process latent variable model, and shown how we can implement it in just a few lines of TF and TF Probability code.