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and explanations, right here is an all-clear, for the involved consumer who fears their keras code may change into out of date (it gained’t).

Don’t panic

• If you’re utilizing keras in commonplace methods, equivalent to these depicted in most code examples and tutorials seen on the net, and issues have been working wonderful for you in current keras releases (>= 2.2.4.1), don’t fear. Most every little thing 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 properly, however it would be best to verify for modifications in habits/efficiency.

And now for some information and background. This put up 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 perspective 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 commonplace 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 by no means.

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 unique Python Keras it was wrapping did. . In some circumstances, this result in folks utilizing the phrases keras and tensorflow virtually synonymously: Perhaps they mentioned 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 plenty 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 a giant change: With TF 2, Keras (as integrated within the TensorFlow codebase) is now the official high-level API for TensorFlow. To carry this throughout has been a significant level of Google’s TF 2 data 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 every little thing stays the best way 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. Nevertheless, Google began to include unique Keras code into their TensorFlow codebase as a fork, to proceed growth independently. For some time there have been two “Kerases”: Unique Keras and tf.keras. Our R keras supplied to modify between implementations , the default being unique Keras.

In keras launch 2.2.4.1, anticipating discontinuation of unique Keras and desirous to prepare for TF 2, we switched to utilizing tf.keras because the default. Whereas at first, the tf.keras fork and unique Keras developed roughly in sync, the newest developments for TF 2 introduced with them greater modifications within the tf.keras codebase, particularly as regards optimizers.
For this reason, if you’re utilizing a keras model < 2.2.4.1, upgrading to TF 2 it would be best to verify for modifications in habits and/or efficiency.

That’s it for some background. In sum, we’re joyful most present code will run simply wonderful. However for us R customers, one thing have to be altering as properly, 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 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 discuss what these termini seek advice from, and the way they’re related to R customers.

Keen Execution

In TF 1, it was all concerning 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 sides. Defining a graph and operating it (on precise information) had been completely different steps.

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

Whereas this can be a more-than-substantial change that should have required plenty of sources to implement, in case you use keras you gained’t discover. Simply as beforehand, the everyday 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 must do something. Despite the fact that the general execution mode is keen, Keras fashions are skilled in graph mode, to maximise efficiency. We are going to discuss how that is achieved partly 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 robotically 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 larger flexibility than functional-style ones. Try the documentation for easy methods to create one.

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

Encoder-decoder eventualities are a pure match. In case 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(title = NULL) {
    keras_model_custom(title = title, 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(title = NULL) {
    keras_model_custom(title = title, 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 photos 
  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 obtained,
  # 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 exterior 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 put up collection 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. In truth, that was the case on the time these posts had been written. At this time, 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 must 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 information 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 change into the popular means for information 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 equivalent to information augmentation (at present 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 operating components of your code in graph mode.

tfdatasets enter pipelines

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

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 photos, 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:

information <- tibble(
  img = checklist.recordsdata(right here::right here("data-raw/practice"), full.names = TRUE),
  masks = checklist.recordsdata(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 a dataset, we carry out any required transformations, mapping over the batch dimension. Persevering with with the instance from the U-Internet put up, right here we use features from the tf.picture module to (1) load photos in keeping with their file kind, (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$img, dimension = form(128, 128)),
    masks = tf$picture$resize(.x$masks, dimension = form(128, 128))
  ))

Word how as soon as what these features do, they free you of numerous pondering (keep in mind how within the “outdated” Keras method 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 typically need to shuffle, and also you actually will need to batch the information:

 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 solution to do characteristic engineering.

Function columns and have specs

Function 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 couple 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 instructed TensorFlow, please take all numeric columns (apart from a couple of ones listed exprès) and scale them; take column thal, deal with it as categorical and create an embedding for it; discretize age in keeping 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 must outline all these layers, proper? (Which might be fairly cumbersome, having to determine all the best dimensions…)
Fortunately, we don’t must. 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 put up on characteristic columns explaining extra of how this works, and illustrating the time-and-nerve-saving impact by evaluating with the pre-feature-spec means 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 could have been questioning: What about information augmentation performance out there, traditionally, via keras? Like image_data_generator?

This performance doesn’t appear to suit. However a nice-looking answer is in preparation. Within the Keras group, the current RFC on preprocessing layers for Keras addresses this subject. 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 aspect.

The thought is to supply (chainable) preprocessing layers for use for information transformation and/or augmentation in areas equivalent 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 out there this kind of workflow!

Let’s transfer on to the following subject, the widespread 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. Present fashions will be browsed on tfhub.dev.

As of this writing, the unique Python library remains to be below growth, so full stability shouldn’t 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 most important 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 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(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, -Identify
  ) %>%
  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 at this time, 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 it is going to 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 perform right into a graph, wrap it in a name to tf_function, as achieved e.g. within the put up Modeling censored information 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 aspect, the tf.autograph module robotically interprets Python management move statements into acceptable graph operations.

Independently of tf.autograph, the R package deal tfautograph, developed by Tomasz Kalinowski, implements management move conversion immediately 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 in depth documentation for instructive examples!

Conclusion

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

In case you have been utilizing keras in conventional methods, how a lot modifications for you is principally as much as you: Most every little thing 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 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 can assist.

In any case, keep tuned for upcoming posts displaying among the above-mentioned performance in motion. Thanks for studying!