The wait is over – TensorFlow 2.0 (TF 2) is now formally right here! What does this imply for us, customers of R packages keras and/or tensorflow, which, as we all know, depend on the Python TensorFlow backend?

Earlier than we go into particulars and explanations, right here is an all-clear, for the involved consumer who fears their keras code would possibly turn into out of date (it gained’t).

Don’t panic

• If you’re utilizing keras in customary methods, comparable to these depicted in most code examples and tutorials seen on the internet, and issues have been working wonderful for you in current keras releases (>= 2.2.4.1), don’t fear. Most all the things ought to work with out main modifications.
• If you’re utilizing an older launch of keras (< 2.2.4.1), syntactically issues ought to work wonderful as effectively, however it would be best to verify for modifications in conduct/efficiency.

And now for some information and background. This submit goals to do three issues:

• Clarify the above all-clear assertion. Is it actually that easy – what precisely is happening?
• Characterize the modifications caused by TF 2, from the standpoint of the R consumer.
• And, maybe most apparently: Check out what’s going on, within the r-tensorflow ecosystem, round new performance associated to the appearance of TF 2.

Some background

So if all nonetheless works wonderful (assuming customary utilization), why a lot ado about TF 2 in Python land?

The distinction is that on the R facet, for the overwhelming majority of customers, the framework you used to do deep studying was keras. tensorflow was wanted simply sometimes, or under no circumstances.

Between keras and tensorflow, there was a transparent separation of tasks: keras was the frontend, relying on TensorFlow as a low-level backend, identical to the authentic Python Keras it was wrapping did. . In some instances, this result in individuals utilizing the phrases keras and tensorflow nearly synonymously: Perhaps they mentioned tensorflow, however the code they wrote was keras.

Issues had been completely different in Python land. There was authentic Python Keras, however TensorFlow had its personal layers API, and there have been a variety of third-party high-level APIs constructed on TensorFlow.
Keras, in distinction, was a separate library that simply occurred to depend on TensorFlow.

So in Python land, now we’ve got an enormous change: With TF 2, Keras (as integrated within the TensorFlow codebase) is now the official high-level API for TensorFlow. To convey this throughout has been a significant level of Google’s TF 2 info marketing campaign because the early phases.

As R customers, who’ve been specializing in keras on a regular basis, we’re primarily much less affected. Like we mentioned above, syntactically most all the things stays the best way it was. So why differentiate between completely different keras variations?

When keras was written, there was authentic Python Keras, and that was the library we had been binding to. Nonetheless, Google began to include authentic Keras code into their TensorFlow codebase as a fork, to proceed improvement independently. For some time there have been two “Kerases”: Authentic Keras and tf.keras. Our R keras provided to change between implementations , the default being authentic Keras.

In keras launch 2.2.4.1, anticipating discontinuation of authentic Keras and desirous to prepare for TF 2, we switched to utilizing tf.keras because the default. Whereas to start with, the tf.keras fork and authentic Keras developed kind of in sync, the newest developments for TF 2 introduced with them greater modifications within the tf.keras codebase, particularly as regards optimizers.
Because of this, in case you are utilizing a keras model < 2.2.4.1, upgrading to TF 2 it would be best to verify for modifications in conduct and/or efficiency.

That’s it for some background. In sum, we’re pleased most current code will run simply wonderful. However for us R customers, one thing should be altering as effectively, proper?

TF 2 in a nutshell, from an R perspective

In truth, essentially the most evident-on-user-level change is one thing we wrote a number of posts about, greater than a yr in the past . By then, keen execution was a brand-new possibility that needed to be turned on explicitly; TF 2 now makes it the default. Together with it got here customized fashions (a.okay.a. subclassed fashions, in Python land) and customized coaching, making use of tf$GradientTape. Let’s speak about what these termini consult with, and the way they’re related to R customers.

Keen Execution

In TF 1, it was all in regards to the graph you constructed when defining your mannequin. The graph, that was – and is – an Summary Syntax Tree (AST), with operations as nodes and tensors “flowing” alongside the perimeters. Defining a graph and working it (on precise knowledge) had been completely different steps.

In distinction, with keen execution, operations are run instantly when outlined.

Whereas it is a more-than-substantial change that should have required a lot of assets to implement, in case you use keras you gained’t discover. Simply as beforehand, the standard keras workflow of create mannequin -> compile mannequin -> practice mannequin by no means made you consider there being two distinct phases (outline and run), now once more you don’t should do something. Although the general execution mode is raring, Keras fashions are educated in graph mode, to maximise efficiency. We are going to speak about how that is performed partially 3 when introducing the tfautograph package deal.

If keras runs in graph mode, how are you going to even see that keen execution is “on”? Nicely, in TF 1, if you ran a TensorFlow operation on a tensor , like so

that is what you noticed:

Tensor("Cumprod:0", form=(5,), dtype=int32)

To extract the precise values, you needed to create a TensorFlow Session and run the tensor, or alternatively, use keras::k_eval that did this below the hood:

[1]   1   2   6  24 120

With TF 2’s execution mode defaulting to keen, we now routinely see the values contained within the tensor:

tf.Tensor([  1   2   6  24 120], form=(5,), dtype=int32)

In order that’s keen execution. In our final yr’s Keen-category weblog posts, it was at all times accompanied by customized fashions, so let’s flip there subsequent.

Customized fashions

As a keras consumer, in all probability you’re acquainted with the sequential and practical kinds of constructing a mannequin. Customized fashions enable for even better flexibility than functional-style ones. Try the documentation for the best way to create one.

Final yr’s sequence on keen execution has loads of examples utilizing customized fashions, that includes not simply their flexibility, however one other necessary side as effectively: the best way they permit for modular, easily-intelligible code.

Encoder-decoder eventualities are a pure match. When you have seen, or written, “old-style” code for a Generative Adversarial Community (GAN), think about one thing like this as an alternative:

# outline the generator (simplified)
generator <-
  perform(identify = NULL) {
    keras_model_custom(identify = identify, perform(self) {
      
      # outline layers for the generator 
      self$fc1 <- layer_dense(models = 7 * 7 * 64, use_bias = FALSE)
      self$batchnorm1 <- layer_batch_normalization()
      # extra layers ...
      
      # outline what ought to occur within the ahead cross
      perform(inputs, masks = NULL, coaching = TRUE) {
        self$fc1(inputs) %>%
          self$batchnorm1(coaching = coaching) %>%
          # name remaining layers ...
      }
    })
  }

# outline the discriminator
discriminator <-
  perform(identify = NULL) {
    keras_model_custom(identify = identify, perform(self) {
      
      self$conv1 <- layer_conv_2d(filters = 64, #...)
      self$leaky_relu1 <- layer_activation_leaky_relu()
      # extra layers ...
    
      perform(inputs, masks = NULL, coaching = TRUE) {
        inputs %>% self$conv1() %>%
          self$leaky_relu1() %>%
          # name remaining layers ...
      }
    })
  }

# zooming in on a single batch of a single epoch
with(tf$GradientTape() %as% gen_tape, { with(tf$GradientTape() %as% disc_tape, {
  
  # first, it is the generator's name (yep pun meant)
  generated_images <- generator(noise)
  # now the discriminator offers its verdict on the actual pictures 
  disc_real_output <- discriminator(batch, coaching = TRUE)
  # in addition to the pretend ones
  disc_generated_output <- discriminator(generated_images, coaching = TRUE)
  
  # relying on the discriminator's verdict we simply acquired,
  # what is the generator's loss?
  gen_loss <- generator_loss(disc_generated_output)
  # and what is the loss for the discriminator?
  disc_loss <- discriminator_loss(disc_real_output, disc_generated_output)
}) })

# now outdoors the tape's context compute the respective gradients
gradients_of_generator <- gen_tape$gradient(gen_loss, generator$variables)
gradients_of_discriminator <- disc_tape$gradient(disc_loss, discriminator$variables)
 
# and apply them!
generator_optimizer$apply_gradients(
  purrr::transpose(checklist(gradients_of_generator, generator$variables)))
discriminator_optimizer$apply_gradients(
  purrr::transpose(checklist(gradients_of_discriminator, discriminator$variables)))

Once more, evaluate this with pre-TF 2 GAN coaching – it makes for a lot extra readable code.

As an apart, final yr’s submit sequence could have created the impression that with keen execution, you have to make use of customized (GradientTape) coaching as an alternative of Keras-style match. In truth, that was the case on the time these posts had been written. Right now, Keras-style code works simply wonderful with keen execution.

So now with TF 2, we’re in an optimum place. We can use customized coaching once we need to, however we don’t should if declarative match is all we want.

That’s it for a flashlight on what TF 2 means to R customers. We now have a look round within the r-tensorflow ecosystem to see new developments – recent-past, current and future – in areas like knowledge loading, preprocessing, and extra.

New developments within the r-tensorflow ecosystem

These are what we’ll cowl:

• tfdatasets: Over the current previous, tfdatasets pipelines have turn into the popular approach for knowledge loading and preprocessing.
• characteristic columns and characteristic specs: Specify your options recipes-style and have keras generate the ample layers for them.
• Keras preprocessing layers: Keras preprocessing pipelines integrating performance comparable to knowledge augmentation (presently in planning).
• tfhub: Use pretrained fashions as keras layers, and/or as characteristic columns in a keras mannequin.
• tf_function and tfautograph: Pace up coaching by working elements of your code in graph mode.

tfdatasets enter pipelines

For two years now, the tfdatasets package deal has been out there to load knowledge for coaching Keras fashions in a streaming approach.

Logically, there are three steps concerned:

1. First, knowledge must be loaded from some place. This could possibly be a csv file, a listing containing pictures, or different sources. On this current instance from Picture segmentation with U-Internet, details about file names was first saved into an R tibble, after which tensor_slices_dataset was used to create a dataset from it:

knowledge <- tibble(
  img = checklist.information(right here::right here("data-raw/practice"), full.names = TRUE),
  masks = checklist.information(right here::right here("data-raw/train_masks"), full.names = TRUE)
)

knowledge <- initial_split(knowledge, prop = 0.8)

dataset <- coaching(knowledge) %>%  
  tensor_slices_dataset() 

2. As soon as we’ve got a dataset, we carry out any required transformations, mapping over the batch dimension. Persevering with with the instance from the U-Internet submit, right here we use features from the tf.picture module to (1) load pictures in line with their file sort, (2) scale them to values between 0 and 1 (changing to float32 on the similar time), and (3) resize them to the specified format:

dataset <- dataset %>%
  dataset_map(~.x %>% list_modify(
    img = tf$picture$decode_jpeg(tf$io$read_file(.x$img)),
    masks = tf$picture$decode_gif(tf$io$read_file(.x$masks))[1,,,][,,1,drop=FALSE]
  )) %>% 
  dataset_map(~.x %>% list_modify(
    img = tf$picture$convert_image_dtype(.x$img, dtype = tf$float32),
    masks = tf$picture$convert_image_dtype(.x$masks, dtype = tf$float32)
  )) %>% 
  dataset_map(~.x %>% list_modify(
    img = tf$picture$resize(.x$img, measurement = form(128, 128)),
    masks = tf$picture$resize(.x$masks, measurement = form(128, 128))
  ))

Be aware how as soon as you recognize what these features do, they free you of a variety of considering (bear in mind how within the “previous” Keras strategy to picture preprocessing, you had been doing issues like dividing pixel values by 255 “by hand”?)

3. After transformation, a 3rd conceptual step pertains to merchandise association. You’ll usually need to shuffle, and also you actually will need to batch the info:

 if (practice) {
    dataset <- dataset %>% 
      dataset_shuffle(buffer_size = batch_size*128)
  }

dataset <- dataset %>%  dataset_batch(batch_size)

Summing up, utilizing tfdatasets you construct a pipeline, from loading over transformations to batching, that may then be fed on to a Keras mannequin. From preprocessing, let’s go a step additional and have a look at a brand new, extraordinarily handy option to do characteristic engineering.

Characteristic columns and have specs

Characteristic columns
as such are a Python-TensorFlow characteristic, whereas characteristic specs are an R-only idiom modeled after the favored recipes package deal.

All of it begins off with making a characteristic spec object, utilizing formulation syntax to point what’s predictor and what’s goal:

library(tfdatasets)
hearts_dataset <- tensor_slices_dataset(hearts)
spec <- feature_spec(hearts_dataset, goal ~ .)

That specification is then refined by successive details about how we need to make use of the uncooked predictors. That is the place characteristic columns come into play. Completely different column sorts exist, of which you’ll be able to see a number of within the following code snippet:

spec <- feature_spec(hearts, goal ~ .) %>% 
  step_numeric_column(
    all_numeric(), -cp, -restecg, -exang, -intercourse, -fbs,
    normalizer_fn = scaler_standard()
  ) %>% 
  step_categorical_column_with_vocabulary_list(thal) %>% 
  step_bucketized_column(age, boundaries = c(18, 25, 30, 35, 40, 45, 50, 55, 60, 65)) %>% 
  step_indicator_column(thal) %>% 
  step_embedding_column(thal, dimension = 2) %>% 
  step_crossed_column(c(thal, bucketized_age), hash_bucket_size = 10) %>%
  step_indicator_column(crossed_thal_bucketized_age)

spec %>% match()

What occurred right here is that we informed TensorFlow, please take all numeric columns (apart from a number of ones listed exprès) and scale them; take column thal, deal with it as categorical and create an embedding for it; discretize age in line with the given ranges; and eventually, create a crossed column to seize interplay between thal and that discretized age-range column.

That is good, however when creating the mannequin, we’ll nonetheless should outline all these layers, proper? (Which might be fairly cumbersome, having to determine all the precise dimensions…)
Fortunately, we don’t should. In sync with tfdatasets, keras now gives layer_dense_features to create a layer tailored to accommodate the specification.

And we don’t have to create separate enter layers both, resulting from layer_input_from_dataset. Right here we see each in motion:

enter <- layer_input_from_dataset(hearts %>% choose(-goal))

output <- enter %>% 
  layer_dense_features(feature_columns = dense_features(spec)) %>% 
  layer_dense(models = 1, activation = "sigmoid")

From then on, it’s simply regular keras compile and match. See the vignette for the whole instance. There is also a submit on characteristic columns explaining extra of how this works, and illustrating the time-and-nerve-saving impact by evaluating with the pre-feature-spec approach of working with heterogeneous datasets.

As a final merchandise on the subjects of preprocessing and have engineering, let’s have a look at a promising factor to come back in what we hope is the close to future.

Keras preprocessing layers

Studying what we wrote above about utilizing tfdatasets for constructing a enter pipeline, and seeing how we gave a picture loading instance, you’ll have been questioning: What about knowledge augmentation performance out there, traditionally, by means of keras? Like image_data_generator?

This performance doesn’t appear to suit. However a nice-looking resolution is in preparation. Within the Keras neighborhood, the current RFC on preprocessing layers for Keras addresses this matter. The RFC remains to be below dialogue, however as quickly because it will get carried out in Python we’ll comply with up on the R facet.

The concept is to offer (chainable) preprocessing layers for use for knowledge transformation and/or augmentation in areas comparable to picture classification, picture segmentation, object detection, textual content processing, and extra. The envisioned, within the RFC, pipeline of preprocessing layers ought to return a dataset, for compatibility with tf.knowledge (our tfdatasets). We’re undoubtedly wanting ahead to having out there this kind of workflow!

Let’s transfer on to the following matter, the frequent denominator being comfort. However now comfort means not having to construct billion-parameter fashions your self!

Tensorflow Hub and the tfhub package deal

Tensorflow Hub is a library for publishing and utilizing pretrained fashions. Current fashions will be browsed on tfhub.dev.

As of this writing, the unique Python library remains to be below improvement, so full stability will not be assured. That however, the tfhub R package deal already permits for some instructive experimentation.

The standard Keras thought of utilizing pretrained fashions usually concerned both (1) making use of a mannequin like MobileNet as a complete, together with its output layer, or (2) chaining a “customized head” to its penultimate layer . In distinction, the TF Hub thought is to make use of a pretrained mannequin as a module in a bigger setting.

There are two essential methods to perform this, specifically, integrating a module as a keras layer and utilizing it as a characteristic column. The tfhub README exhibits the primary possibility:

library(tfhub)
library(keras)

enter <- layer_input(form = c(32, 32, 3))

output <- enter %>%
  # we're utilizing a pre-trained MobileNet mannequin!
  layer_hub(deal with = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/2") %>%
  layer_dense(models = 10, activation = "softmax")

mannequin <- keras_model(enter, output)

Whereas the tfhub characteristic columns vignette illustrates the second:

spec <- dataset_train %>%
  feature_spec(AdoptionSpeed ~ .) %>%
  step_text_embedding_column(
    Description,
    module_spec = "https://tfhub.dev/google/universal-sentence-encoder/2"
    ) %>%
  step_image_embedding_column(
    img,
    module_spec = "https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/3"
  ) %>%
  step_numeric_column(Age, Price, Amount, normalizer_fn = scaler_standard()) %>%
  step_categorical_column_with_vocabulary_list(
    has_type("string"), -Description, -RescuerID, -img_path, -PetID, -Title
  ) %>%
  step_embedding_column(Breed1:Well being, State)

Each utilization modes illustrate the excessive potential of working with Hub modules. Simply be cautioned that, as of in the present day, not each mannequin printed will work with TF 2.

tf_function, TF autograph and the R package deal tfautograph

As defined above, the default execution mode in TF 2 is raring. For efficiency causes nonetheless, in lots of instances it will likely be fascinating to compile elements of your code right into a graph. Calls to Keras layers, for instance, are run in graph mode.

To compile a perform right into a graph, wrap it in a name to tf_function, as performed e.g. within the submit Modeling censored knowledge with tfprobability:

run_mcmc <- perform(kernel) {
  kernel %>% mcmc_sample_chain(
    num_results = n_steps,
    num_burnin_steps = n_burnin,
    current_state = tf$ones_like(initial_betas),
    trace_fn = trace_fn
  )
}

# necessary for efficiency: run HMC in graph mode
run_mcmc <- tf_function(run_mcmc)

On the Python facet, the tf.autograph module routinely interprets Python management movement statements into applicable graph operations.

Independently of tf.autograph, the R package deal tfautograph, developed by Tomasz Kalinowski, implements management movement conversion instantly from R to TensorFlow. This allows you to use R’s if, whereas, for, break, and subsequent when writing customized coaching flows. Try the package deal’s intensive documentation for instructive examples!

Conclusion

With that, we finish our introduction of TF 2 and the brand new developments that encompass it.

When you have been utilizing keras in conventional methods, how a lot modifications for you is especially as much as you: Most all the things will nonetheless work, however new choices exist to put in writing extra performant, extra modular, extra elegant code. Particularly, take a look at tfdatasets pipelines for environment friendly knowledge loading.

In case you’re a sophisticated consumer requiring non-standard setup, take a look into customized coaching and customized fashions, and seek the advice of the tfautograph documentation to see how the package deal might help.

In any case, keep tuned for upcoming posts exhibiting a number of the above-mentioned performance in motion. Thanks for studying!