More flexible models with TensorFlow eager execution and Keras

Advanced applications like generative adversarial networks, neural style transfer, and the attention mechanism ubiquitous in natural language processing used to be not-so-simple to implement with the Keras declarative coding paradigm. Now, with the advent of TensorFlow eager execution, things have changed. This post explores using eager execution with R.

Sigrid Keydana (RStudio)https://www.rstudio.com/
10-02-2018

If you have used Keras to create neural networks you are no doubt familiar with the Sequential API, which represents models as a linear stack of layers. The Functional API gives you additional options: Using separate input layers, you can combine text input with tabular data. Using multiple outputs, you can perform regression and classification at the same time. Furthermore, you can reuse layers within and between models.

With TensorFlow eager execution, you gain even more flexibility. Using custom models, you define the forward pass through the model completely ad libitum. This means that a lot of architectures get a lot easier to implement, including the applications mentioned above: generative adversarial networks, neural style transfer, various forms of sequence-to-sequence models. In addition, because you have direct access to values, not tensors, model development and debugging are greatly sped up.

How does it work?

In eager execution, operations are not compiled into a graph, but directly defined in your R code. They return values, not symbolic handles to nodes in a computational graph - meaning, you don’t need access to a TensorFlow session to evaluate them.


m1 <- matrix(1:8, nrow = 2, ncol = 4)
m2 <- matrix(1:8, nrow = 4, ncol = 2)
tf$matmul(m1, m2)

tf.Tensor(
[[ 50 114]
 [ 60 140]], shape=(2, 2), dtype=int32)

Eager execution, recent though it is, is already supported in the current CRAN releases of keras and tensorflow. The eager execution guide describes the workflow in detail.

Here’s a quick outline: You define a model, an optimizer, and a loss function. Data is streamed via tfdatasets, including any preprocessing such as image resizing. Then, model training is just a loop over epochs, giving you complete freedom over when (and whether) to execute any actions.

How does backpropagation work in this setup? The forward pass is recorded by a GradientTape, and during the backward pass we explicitly calculate gradients of the loss with respect to the model’s weights. These weights are then adjusted by the optimizer.


with(tf$GradientTape() %as% tape, {
     
  # run model on current batch
  preds <- model(x)
 
  # compute the loss
  loss <- mse_loss(y, preds, x)
  
})
    
# get gradients of loss w.r.t. model weights
gradients <- tape$gradient(loss, model$variables)

# update model weights
optimizer$apply_gradients(
  purrr::transpose(list(gradients, model$variables)),
  global_step = tf$train$get_or_create_global_step()
)

See the eager execution guide for a complete example. Here, we want to answer the question: Why are we so excited about it? At least three things come to mind:

We’ll illustrate these points using a set of eager execution case studies that have recently appeared on this blog.

Complicated stuff made easier

A good example of architectures that become much easier to define with eager execution are attention models. Attention is an important ingredient of sequence-to-sequence models, e.g. (but not only) in machine translation.

When using LSTMs on both the encoding and the decoding sides, the decoder, being a recurrent layer, knows about the sequence it has generated so far. It also (in all but the simplest models) has access to the complete input sequence. But where in the input sequence is the piece of information it needs to generate the next output token? It is this question that attention is meant to address.

Now consider implementing this in code. Each time it is called to produce a new token, the decoder needs to get current input from the attention mechanism. This means we can’t just squeeze an attention layer between the encoder and the decoder LSTM. Before the advent of eager execution, a solution would have been to implement this in low-level TensorFlow code. With eager execution and custom models, we can just use Keras.

Attention is not just relevant to sequence-to-sequence problems, though. In image captioning, the output is a sequence, while the input is a complete image. When generating a caption, attention is used to focus on parts of the image relevant to different time steps in the text-generating process.

Easy inspection

In terms of debuggability, just using custom models (without eager execution) already simplifies things. If we have a custom model like simple_dot from the recent embeddings post and are unsure if we’ve got the shapes correct, we can simply add logging statements, like so:


function(x, mask = NULL) {
  
  users <- x[, 1]
  movies <- x[, 2]
  
  user_embedding <- self$user_embedding(users)
  cat(dim(user_embedding), "\n")
  
  movie_embedding <- self$movie_embedding(movies)
  cat(dim(movie_embedding), "\n")
  
  dot <- self$dot(list(user_embedding, movie_embedding))
  cat(dim(dot), "\n")
  dot
}

With eager execution, things get even better: We can print the tensors’ values themselves.1

But convenience does not end there. In the training loop we showed above, we can obtain losses, model weights, and gradients just by printing them. For example, add a line after the call to tape$gradient to print the gradients for all layers as a list.


gradients <- tape$gradient(loss, model$variables)
print(gradients)

Matching the mental model

If you’ve read Deep Learning with R, you know that it’s possible to program less straightforward workflows, such as those required for training GANs or doing neural style transfer, using the Keras functional API. However, the graph code does not make it easy to keep track of where you are in the workflow.

Now compare the example from the generating digits with GANs post. Generator and discriminator each get set up as actors in a drama:


generator <- function(name = NULL) {
  keras_model_custom(name = name, function(self) {
    # ...
  }
}

discriminator <- function(name = NULL) {
  keras_model_custom(name = name, function(self) {
    # ...
  }
}

Both are informed about their respective loss functions and optimizers.

Then, the duel starts. The training loop is just a succession of generator actions, discriminator actions, and backpropagation through both models. No need to worry about freezing/unfreezing weights in the appropriate places.


with(tf$GradientTape() %as% gen_tape, { with(tf$GradientTape() %as% disc_tape, {
  
 # generator action
 generated_images <- generator(# ...
   
 # discriminator assessments
 disc_real_output <- discriminator(# ... 
 disc_generated_output <- discriminator(# ...
      
 # generator loss
 gen_loss <- generator_loss(# ...                        
 # discriminator loss
 disc_loss <- discriminator_loss(# ...
   
})})
   
# calcucate generator gradients   
gradients_of_generator <- gen_tape$gradient(#...
  
# calcucate discriminator gradients   
gradients_of_discriminator <- disc_tape$gradient(# ...
 
# apply generator gradients to model weights       
generator_optimizer$apply_gradients(# ...

# apply discriminator gradients to model weights 
discriminator_optimizer$apply_gradients(# ...

The code ends up so close to how we mentally picture the situation that hardly any memorization is needed to keep in mind the overall design.

Relatedly, this way of programming lends itself to extensive modularization. This is illustrated by the second post on GANs that includes U-Net like downsampling and upsampling steps.

Here, the downsampling and upsampling layers are each factored out into their own models


downsample <- function(# ...
  keras_model_custom(name = NULL, function(self) { # ...

such that they can be readably composed in the generator’s call method:


# model fields
self$down1 <- downsample(# ...
self$down2 <- downsample(# ...
# ...
# ...

# call method
function(x, mask = NULL, training = TRUE) {       
     
  x1 <- x %>% self$down1(training = training)         
  x2 <- self$down2(x1, training = training)           
  # ...
  # ...

Wrapping up

Eager execution is still a very recent feature and under development. We are convinced that many interesting use cases will still turn up as this paradigm gets adopted more widely among deep learning practitioners.

However, now already we have a list of use cases illustrating the vast options, gains in usability, modularization and elegance offered by eager execution code.

For quick reference, these cover:

When diving into these applications, it is a good idea to also refer to the eager execution guide so you don’t lose sight of the forest for the trees.

We are excited about the use cases our readers will come up with!


  1. Note that the embeddings example uses standard (graph) execution; refactoring would be needed in order to enable eager execution on it.

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Keydana (2018, Oct. 2). TensorFlow for R: More flexible models with TensorFlow eager execution and Keras. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2018-10-02-eager-wrapup/

BibTeX citation

@misc{keydana2018eager-wrapup,
  author = {Keydana, Sigrid},
  title = {TensorFlow for R: More flexible models with TensorFlow eager execution and Keras},
  url = {https://blogs.rstudio.com/tensorflow/posts/2018-10-02-eager-wrapup/},
  year = {2018}
}