RStudio AI Weblog: Simple PixelCNN with tfprobability

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We’ve seen fairly a couple of examples of unsupervised studying (or self-supervised studying, to decide on the extra right however much less fashionable time period) on this weblog.

Usually, these concerned Variational Autoencoders (VAEs), whose attraction lies in them permitting to mannequin a latent house of underlying, impartial (ideally) elements that decide the seen options. A potential draw back could be the inferior high quality of generated samples. Generative Adversarial Networks (GANs) are one other fashionable method. Conceptually, these are extremely enticing because of their game-theoretic framing. Nonetheless, they are often tough to coach. PixelCNN variants, then again – we’ll subsume all of them right here underneath PixelCNN – are typically recognized for his or her good outcomes. They appear to contain some extra alchemy although. Below these circumstances, what might be extra welcome than a straightforward manner of experimenting with them? Via TensorFlow Likelihood (TFP) and its R wrapper, tfprobability, we now have such a manner.

This publish first offers an introduction to PixelCNN, concentrating on high-level ideas (leaving the small print for the curious to look them up within the respective papers). We’ll then present an instance of utilizing tfprobability to experiment with the TFP implementation.

PixelCNN ideas

Autoregressivity, or: We’d like (some) order

The essential concept in PixelCNN is autoregressivity. Every pixel is modeled as relying on all prior pixels. Formally:

[p(mathbf{x}) = prod_{i}p(x_i|x_0, x_1, …, x_{i-1})]

Now wait a second – what even are prior pixels? Final I noticed one photographs had been two-dimensional. So this implies we have now to impose an order on the pixels. Generally this can be raster scan order: row after row, from left to proper. However when coping with shade photographs, there’s one thing else: At every place, we even have three depth values, one for every of purple, inexperienced, and blue. The unique PixelCNN paper(Oord, Kalchbrenner, and Kavukcuoglu 2016) carried via autoregressivity right here as properly, with a pixel’s depth for purple relying on simply prior pixels, these for inexperienced relying on these identical prior pixels however moreover, the present worth for purple, and people for blue relying on the prior pixels in addition to the present values for purple and inexperienced.

[p(x_i|mathbf{x}<i) = p(x_{i,R}|mathbf{x}<i) p(x_{i,G}|mathbf{x}<i, x_{i,R}) p(x_{i,B}|mathbf{x}<i, x_{i,R}, x_{i,G})]

Right here, the variant applied in TFP, PixelCNN++(Salimans et al. 2017) , introduces a simplification; it factorizes the joint distribution in a much less compute-intensive manner.

Technically, then, we all know how autoregressivity is realized; intuitively, it might nonetheless appear shocking that imposing a raster scan order “simply works” (to me, at the very least, it’s). Possibly that is a type of factors the place compute energy efficiently compensates for lack of an equal of a cognitive prior.

Masking, or: The place to not look

Now, PixelCNN ends in “CNN” for a motive – as traditional in picture processing, convolutional layers (or blocks thereof) are concerned. However – is it not the very nature of a convolution that it computes a mean of some types, trying, for every output pixel, not simply on the corresponding enter but additionally, at its spatial (or temporal) environment? How does that rhyme with the look-at-just-prior-pixels technique?

Surprisingly, this downside is simpler to resolve than it sounds. When making use of the convolutional kernel, simply multiply with a masks that zeroes out any “forbidden pixels” – like on this instance for a 5×5 kernel, the place we’re about to compute the convolved worth for row 3, column 3:

[left[begin{array}
{rrr}
1 & 1 & 1 & 1 & 1
1 & 1 & 1 & 1 & 1
1 & 1 & 1 & 0 & 0
0 & 0 & 0 & 0 & 0
0 & 0 & 0 & 0 & 0
end{array}right]
]

This makes the algorithm sincere, however introduces a unique downside: With every successive convolutional layer consuming its predecessor’s output, there’s a repeatedly rising blind spot (so-called in analogy to the blind spot on the retina, however positioned within the high proper) of pixels which are by no means seen by the algorithm. Van den Oord et al. (2016)(Oord et al. 2016) repair this by utilizing two completely different convolutional stacks, one continuing from high to backside, the opposite from left to proper.

Fig. 1: Left: Blind spot, growing over layers. Right: Using two different stacks (a vertical and a horizontal one) solves the problem. Source: van den Oord et al., 2016.

Conditioning, or: Present me a kitten

To date, we’ve all the time talked about “producing photographs” in a purely generic manner. However the actual attraction lies in creating samples of some specified kind – one of many courses we’ve been coaching on, or orthogonal data fed into the community. That is the place PixelCNN turns into Conditional PixelCNN(Oord et al. 2016), and it is usually the place that feeling of magic resurfaces. Once more, as “common math” it’s not exhausting to conceive. Right here, (mathbf{h}) is the extra enter we’re conditioning on:

[p(mathbf{x}| mathbf{h}) = prod_{i}p(x_i|x_0, x_1, …, x_{i-1}, mathbf{h})]

However how does this translate into neural community operations? It’s simply one other matrix multiplication ((V^T mathbf{h})) added to the convolutional outputs ((W mathbf{x})).

[mathbf{y} = tanh(W_{k,f} mathbf{x} + V^T_{k,f} mathbf{h}) odot sigma(W_{k,g} mathbf{x} + V^T_{k,g} mathbf{h})]

(In the event you’re questioning in regards to the second half on the suitable, after the Hadamard product signal – we gained’t go into particulars, however in a nutshell, it’s one other modification launched by (Oord et al. 2016), a switch of the “gating” precept from recurrent neural networks, corresponding to GRUs and LSTMs, to the convolutional setting.)

So we see what goes into the choice of a pixel worth to pattern. However how is that call truly made?

Logistic combination probability , or: No pixel is an island

Once more, that is the place the TFP implementation doesn’t comply with the unique paper, however the latter PixelCNN++ one. Initially, pixels had been modeled as discrete values, selected by a softmax over 256 (0-255) potential values. (That this truly labored looks like one other occasion of deep studying magic. Think about: On this mannequin, 254 is as removed from 255 as it’s from 0.)

In distinction, PixelCNN++ assumes an underlying steady distribution of shade depth, and rounds to the closest integer. That underlying distribution is a combination of logistic distributions, thus permitting for multimodality:

[nu sim sum_{i} pi_i logistic(mu_i, sigma_i)]

General structure and the PixelCNN distribution

General, PixelCNN++, as described in (Salimans et al. 2017), consists of six blocks. The blocks collectively make up a UNet-like construction, successively downsizing the enter after which, upsampling once more:

In TFP’s PixelCNN distribution, the variety of blocks is configurable as num_hierarchies, the default being 3.

Every block consists of a customizable variety of layers, referred to as ResNet layers because of the residual connection (seen on the suitable) complementing the convolutional operations within the horizontal stack:

In TFP, the variety of these layers per block is configurable as num_resnet.

num_resnet and num_hierarchies are the parameters you’re almost certainly to experiment with, however there are a couple of extra you may try within the documentation. The variety of logistic distributions within the combination can be configurable, however from my experiments it’s greatest to maintain that quantity fairly low to keep away from producing NaNs throughout coaching.

Let’s now see a whole instance.

Finish-to-end instance

Our playground can be QuickDraw, a dataset – nonetheless rising – obtained by asking folks to attract some object in at most twenty seconds, utilizing the mouse. (To see for your self, simply try the web site). As of immediately, there are greater than a fifty million situations, from 345 completely different courses.

At first, these information had been chosen to take a break from MNIST and its variants. However identical to these (and lots of extra!), QuickDraw could be obtained, in tfdatasets-ready type, by way of tfds, the R wrapper to TensorFlow datasets. In distinction to the MNIST “household” although, the “actual samples” are themselves extremely irregular, and sometimes even lacking important components. So to anchor judgment, when displaying generated samples we all the time present eight precise drawings with them.

Making ready the info

The dataset being gigantic, we instruct tfds to load the primary 500,000 drawings “solely.”

To hurry up coaching additional, we then zoom in on twenty courses. This successfully leaves us with ~ 1,100 – 1,500 drawings per class.

# bee, bicycle, broccoli, butterfly, cactus,
# frog, guitar, lightning, penguin, pizza,
# rollerskates, sea turtle, sheep, snowflake, solar,
# swan, The Eiffel Tower, tractor, practice, tree
courses <- c(26, 29, 43, 49, 50,
             125, 134, 172, 218, 225,
             246, 255, 258, 271, 295,
             296, 308, 320, 322, 323
)

classes_tensor <- tf$forged(courses, tf$int64)

train_ds <- train_ds %>%
  dataset_filter(
    operate(document) tf$reduce_any(tf$equal(classes_tensor, document$label), -1L)
  )

The PixelCNN distribution expects values within the vary from 0 to 255 – no normalization required. Preprocessing then consists of simply casting pixels and labels every to float:

preprocess <- operate(document) {
  document$picture <- tf$forged(document$picture, tf$float32) 
  document$label <- tf$forged(document$label, tf$float32)
  record(tuple(document$picture, document$label))
}

batch_size <- 32

practice <- train_ds %>%
  dataset_map(preprocess) %>%
  dataset_shuffle(10000) %>%
  dataset_batch(batch_size)

Creating the mannequin

We now use tfd_pixel_cnn to outline what would be the loglikelihood utilized by the mannequin.

dist <- tfd_pixel_cnn(
  image_shape = c(28, 28, 1),
  conditional_shape = record(),
  num_resnet = 5,
  num_hierarchies = 3,
  num_filters = 128,
  num_logistic_mix = 5,
  dropout_p =.5
)

image_input <- layer_input(form = c(28, 28, 1))
label_input <- layer_input(form = record())
log_prob <- dist %>% tfd_log_prob(image_input, conditional_input = label_input)

This practice loglikelihood is added as a loss to the mannequin, after which, the mannequin is compiled with simply an optimizer specification solely. Throughout coaching, loss first decreased rapidly, however enhancements from later epochs had been smaller.

mannequin <- keras_model(inputs = record(image_input, label_input), outputs = log_prob)
mannequin$add_loss(-tf$reduce_mean(log_prob))
mannequin$compile(optimizer = optimizer_adam(lr = .001))

mannequin %>% match(practice, epochs = 10)

To collectively show actual and faux photographs:

for (i in courses) {
  
  real_images <- train_ds %>%
    dataset_filter(
      operate(document) document$label == tf$forged(i, tf$int64)
    ) %>% 
    dataset_take(8) %>%
    dataset_batch(8)
  it <- as_iterator(real_images)
  real_images <- iter_next(it)
  real_images <- real_images$picture %>% as.array()
  real_images <- real_images[ , , , 1]/255
  
  generated_images <- dist %>% tfd_sample(8, conditional_input = i)
  generated_images <- generated_images %>% as.array()
  generated_images <- generated_images[ , , , 1]/255
  
  photographs <- abind::abind(real_images, generated_images, alongside = 1)
  png(paste0("draw_", i, ".png"), width = 8 * 28 * 10, top = 2 * 28 * 10)
  par(mfrow = c(2, 8), mar = c(0, 0, 0, 0))
  photographs %>%
    purrr::array_tree(1) %>%
    purrr::map(as.raster) %>%
    purrr::iwalk(plot)
  dev.off()
}

From our twenty courses, right here’s a alternative of six, every exhibiting actual drawings within the high row, and faux ones under.

We in all probability wouldn’t confuse the primary and second rows, however then, the precise human drawings exhibit monumental variation, too. And nobody ever mentioned PixelCNN was an structure for idea studying. Be happy to mess around with different datasets of your alternative – TFP’s PixelCNN distribution makes it straightforward.

Wrapping up

On this publish, we had tfprobability / TFP do all of the heavy lifting for us, and so, may concentrate on the underlying ideas. Relying in your inclinations, this may be a super state of affairs – you don’t lose sight of the forest for the timber. Then again: Must you discover that altering the offered parameters doesn’t obtain what you need, you might have a reference implementation to begin from. So regardless of the final result, the addition of such higher-level performance to TFP is a win for the customers. (In the event you’re a TFP developer studying this: Sure, we’d like extra :-)).

To everybody although, thanks for studying!

Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. 2016. “Pixel Recurrent Neural Networks.” CoRR abs/1601.06759. http://arxiv.org/abs/1601.06759.
Oord, Aaron van den, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. 2016. “Conditional Picture Technology with PixelCNN Decoders.” CoRR abs/1606.05328. http://arxiv.org/abs/1606.05328.

Salimans, Tim, Andrej Karpathy, Xi Chen, and Diederik P. Kingma. 2017. “PixelCNN++: A PixelCNN Implementation with Discretized Logistic Combination Chance and Different Modifications.” In ICLR.

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