---
title: Understanding masking & padding
authors: Scott Zhu, Francois Chollet
date-created: 2019/07/16
last-modified: 2023/06/25
description: Complete guide to using mask-aware sequence layers in Keras.
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Understanding masking & padding}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Setup


``` r
library(keras3)
```

## Introduction

**Masking** is a way to tell sequence-processing layers that certain timesteps
in an input are missing, and thus should be skipped when processing the data.

**Padding** is a special form of masking where the masked steps are at the start or
the end of a sequence. Padding comes from the need to encode sequence data into
contiguous batches: in order to make all sequences in a batch fit a given standard
length, it is necessary to pad or truncate some sequences.

Let's take a close look.

## Padding sequence data

When processing sequence data, it is very common for individual samples to have
different lengths. Consider the following example (text tokenized as words):


``` r
data <- list(
  c("Hello", "world", "!"),
  c("How", "are", "you", "doing", "today"),
  c("The", "weather", "will", "be", "nice", "tomorrow")
)
```

After vocabulary lookup, the data might be vectorized as integers, e.g.:


``` r
data <- list(
  c(71, 1331, 4231),
  c(73, 8, 3215, 55, 927),
  c(83, 91, 1, 645, 1253, 927)
)
```

The data is a nested list where individual samples have length 3, 5, and 6,
respectively. Since the input data for a deep learning model must be a single tensor
(of shape e.g. `(batch_size, 6, vocab_size)` in this case), samples that are shorter
than the longest item need to be padded with some placeholder value (alternatively,
one might also truncate long samples before padding short samples).

Keras provides a utility function to truncate and pad Python lists to a common length:
`pad_sequences`.


``` r
raw_inputs <- list(
  c(711, 632, 71),
  c(73, 8, 3215, 55, 927),
  c(83, 91, 1, 645, 1253, 927)
)

# By default, this will pad using 0s; it is configurable via the
# "value" parameter.
# Note that you could use "pre" padding (at the beginning) or
# "post" padding (at the end).
# We recommend using "post" padding when working with RNN layers
# (in order to be able to use the
# CuDNN implementation of the layers).
padded_inputs <- pad_sequences(raw_inputs, padding="post")
padded_inputs
```

```
##      [,1] [,2] [,3] [,4] [,5] [,6]
## [1,]  711  632   71    0    0    0
## [2,]   73    8 3215   55  927    0
## [3,]   83   91    1  645 1253  927
```

## Masking

Now that all samples have a uniform length, the model must be informed that some part
of the data is actually padding and should be ignored. That mechanism is **masking**.

There are three ways to introduce input masks in Keras models:

- Add a `layer_masking` layer.
- Configure a `layer_embedding` layer with `mask_zero=TRUE`.
- Pass a `mask` argument manually when calling layers that support this argument (e.g.
RNN layers).

## Mask-generating layers: `Embedding` and `Masking`

Under the hood, these layers will create a mask tensor (2D tensor with shape `(batch,
sequence_length)`), and attach it to the tensor output returned by the `Masking` or
`Embedding` layer.


``` r
embedding <- layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE)
masked_output <- embedding(padded_inputs)

masked_output$`_keras_mask`
```

```
## tf.Tensor(
## [[ True  True  True False False False]
##  [ True  True  True  True  True False]
##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool)
```

``` r
masking_layer <- layer_masking()
# Simulate the embedding lookup by expanding the 2D input to 3D,
# with embedding dimension of 10.
unmasked_embedding <- op_cast(
    op_tile(op_expand_dims(padded_inputs, axis=-1), c(1L, 1L, 10L)),
    dtype="float32"
)

masked_embedding <- masking_layer(unmasked_embedding)
masked_embedding$`_keras_mask`
```

```
## tf.Tensor(
## [[ True  True  True False False False]
##  [ True  True  True  True  True False]
##  [ True  True  True  True  True  True]], shape=(3, 6), dtype=bool)
```

As you can see from the printed result, the mask is a 2D boolean tensor with shape
`(batch_size, sequence_length)`, where each individual `FALSE` entry indicates that
the corresponding timestep should be ignored during processing.

## Mask propagation in the Functional API and Sequential API

When using the Functional API or the Sequential API, a mask generated by an `Embedding`
or `Masking` layer will be propagated through the network for any layer that is
capable of using them (for example, RNN layers). Keras will automatically fetch the
mask corresponding to an input and pass it to any layer that knows how to use it.

For instance, in the following Sequential model, the `LSTM` layer will automatically
receive a mask, which means it will ignore padded values:


``` r
model <- keras_model_sequential() %>%
  layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%
  layer_lstm(units=32)
```

This is also the case for the following Functional API model:


``` r
inputs <- keras_input(shape = shape(NULL), dtype="int32")
outputs <- inputs %>%
  layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%
  layer_lstm(units=32)

model <- keras_model(inputs, outputs)
```

## Passing mask tensors directly to layers

Layers that can handle masks (such as the `LSTM` layer) have a `mask` argument in their
`call` method.

Meanwhile, layers that produce a mask (e.g. `Embedding`) expose a `compute_mask(input,
previous_mask)` method which you can call.

Thus, you can pass the output of the `compute_mask()` method of a mask-producing layer
to the `call` method of a mask-consuming layer, like this:


``` r
MyLayer <- new_layer_class(
  "MyLayer",
  initialize = function(...) {
    super$initialize(...)
    self$embedding <- layer_embedding(
      input_dim=5000, output_dim=16, mask_zero=TRUE
    )
    self$lstm <- layer_lstm(units=32)
  },
  call = function(inputs) {
    inputs %>%
      self$embedding() %>%
      # Note that you could also prepare a `mask` tensor manually.
      # It only needs to be a boolean tensor
      # with the right shape, i.e. (batch_size, timesteps).
      self$lstm(mask=self$embedding$compute_mask(inputs))
  }
)

layer <- MyLayer()
x <- random_integer(c(32, 10), 0, 100)
layer(x)
```

```
## tf.Tensor(
## [[ 0.00130048 -0.00113367 -0.00715671 ... -0.00107615 -0.00162071
##    0.00135018]
##  [-0.004185    0.00726349  0.00520932 ...  0.00119117  0.00230441
##    0.00174123]
##  [-0.00537032 -0.00164898 -0.00238435 ... -0.00154158 -0.0038603
##   -0.00105811]
##  ...
##  [ 0.00622133 -0.00905907 -0.00599518 ...  0.00025823 -0.00142478
##   -0.00125036]
##  [-0.00523904  0.00336683 -0.00299453 ...  0.00876719  0.00172074
##    0.00903089]
##  [-0.00393721  0.00058538  0.00503809 ... -0.00203075  0.00325885
##   -0.00299755]], shape=(32, 32), dtype=float32)
```

## Supporting masking in your custom layers

Sometimes, you may need to write layers that generate a mask (like `Embedding`), or
layers that need to modify the current mask.

For instance, any layer that produces a tensor with a different time dimension than its
input, such as a `Concatenate` layer that concatenates on the time dimension, will
need to modify the current mask so that downstream layers will be able to properly
take masked timesteps into account.

To do this, your layer should implement the `layer.compute_mask()` method, which
produces a new mask given the input and the current mask.

Here is an example of a `TemporalSplit` layer that needs to modify the current mask.


``` r
TemporalSplit <- new_layer_class(
  "TemporalSplit",
  call = function(inputs) {
    # Expect the input to be 3D and mask to be 2D, split the input tensor into 2
    # subtensors along the time axis (axis 1).
    op_split(inputs, 2, axis=2)
  },
  compute_mask = function(inputs, mask = NULL) {
    # Also split the mask into 2 if it presents.
    if (!is.null(mask)) {
      op_split(mask, 2, axis=2)
    } else {
      NULL
    }
  }
)

c(first_half, second_half) %<-% TemporalSplit(masked_embedding)
first_half$`_keras_mask`
```

```
## tf.Tensor(
## [[ True  True  True]
##  [ True  True  True]
##  [ True  True  True]], shape=(3, 3), dtype=bool)
```

``` r
second_half$`_keras_mask`
```

```
## tf.Tensor(
## [[False False False]
##  [ True  True False]
##  [ True  True  True]], shape=(3, 3), dtype=bool)
```

Here is another example of a `CustomEmbedding` layer that is capable of generating a
mask from input values:


``` r
CustomEmbedding <- new_layer_class(
  "CustomEmbedding",
  initialize = function(input_dim, output_dim, mask_zero=FALSE, ...) {
    super$initialize(...)
    self$input_dim <- as.integer(input_dim)
    self$output_dim <- as.integer(output_dim)
    self$mask_zero <- mask_zero
  },
  build = function(input_shape) {
    self$embeddings <- self$add_weight(
      shape=c(self$input_dim, self$output_dim),
      initializer="random_normal",
      dtype="float32"
    )
  },
  call = function(inputs) {
    inputs <- op_cast(inputs, "int32")
    op_take(self$embeddings, inputs)
  },
  compute_mask = function(inputs, mask=NULL) {
    if (!self$mask_zero) {
      NULL
    } else {
      op_not_equal(inputs, 0)
    }
  }
)

layer <- CustomEmbedding(input_dim = 10, output_dim = 32, mask_zero=TRUE)
x <- random_integer(c(3, 10), 0, 9)

y <- layer(x)
mask <- layer$compute_mask(x)

mask
```

```
## tf.Tensor(
## [[ True  True  True  True  True  True  True  True  True  True]
##  [ True  True  True  True  True  True  True  True False  True]
##  [ True  True  True  True False  True  True  True  True  True]], shape=(3, 10), dtype=bool)
```

Note: For more details about format limitations related to masking, see the
[serialization guide](serialization_and_saving.html).

## Opting-in to mask propagation on compatible layers

Most layers don't modify the time dimension, so don't need to modify the current mask.
However, they may still want to be able to **propagate** the current mask, unchanged,
to the next layer. **This is an opt-in behavior.** By default, a custom layer will
destroy the current mask (since the framework has no way to tell whether propagating
the mask is safe to do).

If you have a custom layer that does not modify the time dimension, and if you want it
to be able to propagate the current input mask, you should set `self.supports_masking
= True` in the layer constructor. In this case, the default behavior of
`compute_mask()` is to just pass the current mask through.

Here's an example of a layer that is whitelisted for mask propagation:


``` r
MyActivation <- new_layer_class(
  "MyActivation",
  initialize = function(...) {
    super$initialize(...)
    self$supports_masking <- TRUE
  },
  call = function(inputs) {
    op_relu(inputs)
  }
)
```

You can now use this custom layer in-between a mask-generating layer (like `Embedding`)
and a mask-consuming layer (like `LSTM`), and it will pass the mask along so that it
reaches the mask-consuming layer.


``` r
inputs <- keras_input(shape = shape(NULL), dtype="int32")
outputs <- inputs %>%
  layer_embedding(input_dim=5000, output_dim=16, mask_zero=TRUE) %>%
  MyActivation() %>%
  layer_lstm(units=32)

model <- keras_model(inputs, outputs)
y <- model(random_integer(c(32, 100), 0, 5000))
```

## Writing layers that need mask information

Some layers are mask *consumers*: they accept a `mask` argument in `call` and use it to
determine whether to skip certain time steps.

To write such a layer, you can simply add a `mask=None` argument in your `call`
signature. The mask associated with the inputs will be passed to your layer whenever
it is available.

Here's a simple example below: a layer that computes a softmax over the time dimension
(axis 1) of an input sequence, while discarding masked timesteps.


``` r
TemporalSoftmax <- new_layer_class(
  "TemporalSoftmax",
  initialize = function(...) {
    super$initialize(...)
    self$supports_masking <- TRUE
  },
  call = function(inputs, mask=NULL) {
    if (is.null(mask)) {
      stop("`TemporalSoftmax` layer requires a previous layer to support masking.")
    }
    broadcast_float_mask <- op_expand_dims(op_cast(mask, "float32"), -1)
    inputs_exp <- op_exp(inputs) * broadcast_float_mask
    inputs_sum <- op_sum(inputs_exp * broadcast_float_mask, axis=-1, keepdims=TRUE)
    inputs_exp / inputs_sum
  }
)

inputs <- keras_input(shape = shape(NULL), dtype="int32")
outputs <- inputs %>%
  layer_embedding(input_dim=10, output_dim=32, mask_zero=TRUE) %>%
  layer_dense(1) %>%
  TemporalSoftmax()

model <- keras_model(inputs, outputs)
y <- model(random_integer(c(32, 100), 0, 10))
```

## Summary

That is all you need to know about padding & masking in Keras. To recap:

- "Masking" is how layers are able to know when to skip / ignore certain timesteps in
sequence inputs.
- Some layers are mask-generators: `Embedding` can generate a mask from input values
(if `mask_zero=TRUE`), and so can the `Masking` layer.
- Some layers are mask-consumers: they expose a `mask` argument in their `call`
method. This is the case for RNN layers.
- In the Functional API and Sequential API, mask information is propagated
automatically.
- When using layers in a standalone way, you can pass the `mask` arguments to layers
manually.
- You can easily write layers that modify the current mask, that generate a new mask,
or that consume the mask associated with the inputs.
