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right here is an all-clear, for the involved consumer who fears their keras code would possibly grow to be out of date (it gained’t).

Don’t panic

• If you’re utilizing keras in customary methods, similar to these depicted in most code examples and tutorials seen on the internet, and issues have been working advantageous for you in latest keras releases (>= 2.2.4.1), don’t fear. Most every part ought to work with out main adjustments.
• If you’re utilizing an older launch of keras (< 2.2.4.1), syntactically issues ought to work advantageous as effectively, however it would be best to test for adjustments in habits/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 adjustments caused by TF 2, from the standpoint of the R consumer.
• And, maybe most curiously: Check out what’s going on, within the r-tensorflow ecosystem, round new performance associated to the arrival of TF 2.

Some background

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

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

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

Issues had been completely different in Python land. There was unique 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 a giant change: With TF 2, Keras (as included within the TensorFlow codebase) is now the official high-level API for TensorFlow. To carry this throughout has been a serious level of Google’s TF 2 info marketing campaign for the reason that early levels.

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

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

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

That’s it for some background. In sum, we’re completely satisfied most present code will run simply advantageous. However for us R customers, one thing have to be altering as effectively, proper?

TF 2 in a nutshell, from an R perspective

Actually, probably the most evident-on-user-level change is one thing we wrote a number of posts about, greater than a 12 months in the past . By then, keen execution was a brand-new choice 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 confer 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 information) had been completely different steps.

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

Whereas this can be a more-than-substantial change that should have required numerous assets to implement, should you use keras you gained’t discover. Simply as beforehand, the standard keras workflow of create mannequin -> compile mannequin -> prepare mannequin by no means made you concentrate on there being two distinct phases (outline and run), now once more you don’t need to do something. Although the general execution mode is keen, Keras fashions are skilled in graph mode, to maximise efficiency. We are going to speak about how that is finished 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”? Effectively, 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 12 months’s Keen-category weblog posts, it was all the time accompanied by customized fashions, so let’s flip there subsequent.

Customized fashions

As a keras consumer, in all probability you’re accustomed to the sequential and practical types of constructing a mannequin. Customized fashions enable for even higher flexibility than functional-style ones. Take a look at the documentation for easy methods to create one.

Final 12 months’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 way in which 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 <-
  operate(identify = NULL) {
    keras_model_custom(identify = identify, operate(self) {
      
      # outline layers for the generator 
      self$fc1 <- layer_dense(items = 7 * 7 * 64, use_bias = FALSE)
      self$batchnorm1 <- layer_batch_normalization()
      # extra layers ...
      
      # outline what ought to occur within the ahead move
      operate(inputs, masks = NULL, coaching = TRUE) {
        self$fc1(inputs) %>%
          self$batchnorm1(coaching = coaching) %>%
          # name remaining layers ...
      }
    })
  }

# outline the discriminator
discriminator <-
  operate(identify = NULL) {
    keras_model_custom(identify = identify, operate(self) {
      
      self$conv1 <- layer_conv_2d(filters = 64, #...)
      self$leaky_relu1 <- layer_activation_leaky_relu()
      # extra layers ...
    
      operate(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 supposed)
  generated_images <- generator(noise)
  # now the discriminator offers its verdict on the actual photographs 
  disc_real_output <- discriminator(batch, coaching = TRUE)
  # in addition to the faux ones
  disc_generated_output <- discriminator(generated_images, coaching = TRUE)
  
  # relying on the discriminator's verdict we simply received,
  # 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$apply_gradients(
  purrr::transpose(listing(gradients_of_generator, generator$variables)))
discriminator_optimizer$apply_gradients(
  purrr::transpose(listing(gradients_of_discriminator, discriminator$variables)))

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

As an apart, final 12 months’s submit sequence might have created the impression that with keen execution, you have to make use of customized (GradientTape) coaching as an alternative of Keras-style match. Actually, that was the case on the time these posts had been written. Immediately, Keras-style code works simply advantageous with keen execution.

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

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 information loading, preprocessing, and extra.

New developments within the r-tensorflow ecosystem

These are what we’ll cowl:

• tfdatasets: Over the latest previous, tfdatasets pipelines have grow to be the popular manner for information loading and preprocessing.
• function columns and function specs: Specify your options recipes-style and have keras generate the ample layers for them.
• Keras preprocessing layers: Keras preprocessing pipelines integrating performance similar to information augmentation (at present in planning).
• tfhub: Use pretrained fashions as keras layers, and/or as function columns in a keras mannequin.
• tf_function and tfautograph: Pace up coaching by working components of your code in graph mode.

tfdatasets enter pipelines

For two years now, the tfdatasets package deal has been accessible to load information for coaching Keras fashions in a streaming manner.

Logically, there are three steps concerned:

1. First, information needs to be loaded from some place. This might be a csv file, a listing containing photographs, or different sources. On this latest instance from Picture segmentation with U-Web, details about file names was first saved into an R tibble, after which tensor_slices_dataset was used to create a dataset from it:

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

information <- initial_split(information, prop = 0.8)

dataset <- coaching(information) %>%  
  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-Web submit, right here we use capabilities from the tf.picture module to (1) load photographs in line with their file sort, (2) scale them to values between 0 and 1 (changing to float32 on the identical 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$img, dimension = form(128, 128)),
    masks = tf$picture$resize(.x$masks, dimension = form(128, 128))
  ))

Word how as soon as you understand what these capabilities do, they free you of lots of considering (bear in mind how within the “outdated” 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 wish to shuffle, and also you definitely will wish to batch the info:

 if (prepare) {
    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 method to do function engineering.

Characteristic columns and have specs

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

All of it begins off with making a function 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 wish to make use of the uncooked predictors. That is the place function columns come into play. Totally different column varieties exist, of which you’ll be able to see just a few 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 (moreover just a few 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 need to outline all these layers, proper? (Which might be fairly cumbersome, having to determine all the suitable dimensions…)
Fortunately, we don’t need to. 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, on account of 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(items = 1, activation = "sigmoid")

From then on, it’s simply regular keras compile and match. See the vignette for the entire instance. There is also a submit on function columns explaining extra of how this works, and illustrating the time-and-nerve-saving impact by evaluating with the pre-feature-spec manner 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 return 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 might have been questioning: What about information augmentation performance accessible, traditionally, via keras? Like image_data_generator?

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

The concept is to supply (chainable) preprocessing layers for use for information transformation and/or augmentation in areas similar 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.information (our tfdatasets). We’re undoubtedly trying ahead to having accessible this type of workflow!

Let’s transfer on to the subsequent 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 continues to be below improvement, so full stability just isn’t assured. That however, the tfhub R package deal already permits for some instructive experimentation.

The normal Keras thought of utilizing pretrained fashions sometimes concerned both (1) making use of a mannequin like MobileNet as an entire, 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 primary methods to perform this, particularly, integrating a module as a keras layer and utilizing it as a function column. The tfhub README reveals the primary choice:

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(items = 10, activation = "softmax")

mannequin <- keras_model(enter, output)

Whereas the tfhub function 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, Charge, 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 as we speak, 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 keen. For efficiency causes nevertheless, in lots of circumstances will probably be fascinating to compile components of your code right into a graph. Calls to Keras layers, for instance, are run in graph mode.

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

run_mcmc <- operate(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 aspect, the tf.autograph module routinely interprets Python management move statements into applicable graph operations.

Independently of tf.autograph, the R package deal tfautograph, developed by Tomasz Kalinowski, implements management move conversion instantly from R to TensorFlow. This allows you to use R’s if, whereas, for, break, and subsequent when writing customized coaching flows. Take a look at the package deal’s in depth 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 adjustments for you is especially as much as you: Most every part will nonetheless work, however new choices exist to jot down extra performant, extra modular, extra elegant code. Specifically, try tfdatasets pipelines for environment friendly information loading.

For those who’re a complicated 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 few of the above-mentioned performance in motion. Thanks for studying!