---
title: Making new layers and models via subclassing
date-created: 2019/03/01
last-modified: 2023/06/25
description: Complete guide to writing `Layer` and `Model` objects from scratch.
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Making new layers and models via subclassing}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Introduction
This guide will cover everything you need to know to build your own
subclassed layers and models. In particular, you'll learn about the following features:
- The `Layer` class
- The `add_weight()` method
- Trainable and non-trainable weights
- The `build()` method
- Making sure your layers can be used with any backend
- The `add_loss()` method
- The `training` argument in `call()`
- The `mask` argument in `call()`
- Making sure your layers can be serialized
Let's dive in.
## Setup
``` r
library(keras3)
library(tensorflow, exclude = c("set_random_seed", "shape"))
library(tfdatasets, exclude = "shape")
```
## The `Layer` class: the combination of state (weights) and some computation
One of the central abstractions in Keras is the `Layer` class. A layer
encapsulates both a state (the layer's "weights") and a transformation from
inputs to outputs (a "call", the layer's forward pass).
Here's a densely-connected layer. It has two state variables:
the variables `w` and `b`.
``` r
layer_linear <- Layer("Linear",
initialize = function(units = 32, input_dim = 32, ...) {
super$initialize(...)
self$w <- self$add_weight(
shape = shape(input_dim, units),
initializer = "random_normal",
trainable = TRUE
)
self$b <- self$add_weight(
shape = shape(units),
initializer = "zeros",
trainable = TRUE
)
},
call = function(inputs) {
op_matmul(inputs, self$w) + self$b
}
)
```
You would use a layer by calling it on some tensor input(s), much like an R
function.
``` r
x <- op_ones(c(2, 2))
linear_layer <- layer_linear(units = 4, input_dim = 2)
y <- linear_layer(x)
print(y)
```
```
## tf.Tensor(
## [[0.02153057 0.15450525 0.0205495 0.04493225]
## [0.02153057 0.15450525 0.0205495 0.04493225]], shape=(2, 4), dtype=float32)
```
Note that the weights `w` and `b` are automatically tracked by the layer upon
being set as layer attributes:
``` r
linear_layer$weights
```
```
## [[1]]
## <Variable path=linear/variable, shape=(2, 4), dtype=float32, value=[[-0.06251299 0.05335509 0.01485647 -0.00985784]
## [ 0.08404355 0.10115016 0.00569303 0.05479009]]>
##
## [[2]]
## <Variable path=linear/variable_1, shape=(4), dtype=float32, value=[0. 0. 0. 0.]>
```
## Layers can have non-trainable weights
Besides trainable weights, you can add non-trainable weights to a layer as
well. Such weights are meant not to be taken into account during
backpropagation, when you are training the layer.
Here's how to add and use a non-trainable weight:
``` r
layer_compute_sum <- Layer(
"ComputeSum",
initialize = function(input_dim) {
super$initialize()
self$total <- self$add_weight(
initializer = "zeros",
shape = shape(input_dim),
trainable = FALSE
)
},
call = function(inputs) {
self$total$assign_add(op_sum(inputs, axis = 1))
self$total
}
)
x <- op_ones(c(2, 2))
my_sum <- layer_compute_sum(input_dim = 2)
y <- my_sum(x)
print(as.array(y))
```
```
## [1] 2 2
```
``` r
y <- my_sum(x)
print(as.array(y))
```
```
## [1] 4 4
```
It's part of `layer$weights`, but it gets categorized as a non-trainable weight:
``` r
cat("weights:", length(my_sum$weights))
```
```
## weights: 1
```
``` r
cat("non-trainable weights:", length(my_sum$non_trainable_weights))
```
```
## non-trainable weights: 1
```
``` r
# It's not included in the trainable weights:
cat("trainable_weights:", length(my_sum$trainable_weights))
```
```
## trainable_weights: 0
```
## Best practice: deferring weight creation until the shape of the inputs is known
Our `Linear` layer above took an `input_dim` argument that was used to compute
the shape of the weights `w` and `b` in `initialize()`:
``` r
layer_linear <- Layer("Linear",
initialize = function(units = 32, input_dim = 32, ...) {
super$initialize(...)
self$w <- self$add_weight(
shape = shape(input_dim, units),
initializer = "random_normal",
trainable = TRUE
)
self$b <- self$add_weight(
shape = shape(units),
initializer = "zeros",
trainable = TRUE
)
},
call = function(inputs) {
op_matmul(inputs, self$w) + self$b
}
)
```
In many cases, you may not know in advance the size of your inputs, and you
would like to lazily create weights when that value becomes known, some time
after instantiating the layer.
In the Keras API, we recommend creating layer weights in the
`build(self, inputs_shape)` method of your layer. Like this:
``` r
layer_linear <- Layer(
"Linear",
initialize = function(units = 32, ...) {
self$units <- as.integer(units)
super$initialize(...)
},
build = function(input_shape) {
self$w <- self$add_weight(
shape = shape(tail(input_shape, 1), self$units),
initializer = "random_normal",
trainable = TRUE
)
self$b <- self$add_weight(
shape = shape(self$units),
initializer = "zeros",
trainable = TRUE
)
},
call = function(inputs) {
op_matmul(inputs, self$w) + self$b
}
)
```
The `call()` method of your layer will automatically run build the first time
it is called. You now have a layer that's lazy and thus easier to use:
``` r
# At instantiation, we don't know on what inputs this is going to get called
linear_layer <- layer_linear(units = 32)
# The layer's weights are created dynamically the first time the layer is called
y <- linear_layer(x)
```
Implementing `build()` separately as shown above nicely separates creating weights
only once from using weights in every call.
## Layers are recursively composable
If you assign a Layer instance as an attribute of another Layer, the outer layer
will start tracking the weights created by the inner layer.
We recommend creating such sublayers in the `initialize()` method and leave it to
the first `call()` to trigger building their weights.
``` r
MLPBlock <- Layer(
"MLPBlock",
initialize = function() {
super$initialize()
self$linear_1 <- layer_linear(units = 32)
self$linear_2 <- layer_linear(units = 32)
self$linear_3 <- layer_linear(units = 1)
},
call = function(inputs) {
inputs |>
self$linear_1() |>
activation_relu() |>
self$linear_2() |>
activation_relu() |>
self$linear_3()
}
)
mlp <- MLPBlock()
# The first call to the `mlp` will create the weights
y <- mlp(op_ones(shape = c(3, 64)))
cat("weights:", length(mlp$weights), "\n")
```
```
## weights: 6
```
``` r
cat("trainable weights:", length(mlp$trainable_weights), "\n")
```
```
## trainable weights: 6
```
## Backend-agnostic layers and backend-specific layers
As long as a layer only uses APIs from the `ops` namespace (ie. using functions
starting with `op_`),
(or other Keras namespaces such as `activations_*`, `random_*`, or `layer_*`),
then it can be used with any backend -- TensorFlow, JAX, or PyTorch.
All layers you've seen so far in this guide work with all Keras backends.
The `ops` namespace gives you access to:
- The NumPy API, e.g. `op_matmul`, `op_sum`, `op_reshape`, `op_stack`, etc.
- Neural networks-specific APIs such as `op_softmax`, `op_conv`, `op_binary_crossentropy`, `op_relu`, etc.
You can also use backend-native APIs in your layers (such as `tf$nn` functions),
but if you do this, then your layer will only be usable with the backend in question.
For instance, you could write the following JAX-specific layer using `jax$numpy`:
``` r
# keras3::install_keras(backend = c("jax"))
jax <- reticulate::import("jax")
Linear <- new_layer_class(
...
call = function(inputs) {
jax$numpy$matmul(inputs, self$w) + self$b
}
)
```
This would be the equivalent TensorFlow-specific layer:
``` r
library(tensorflow)
Linear <- new_layer_class(
...
call = function(inputs) {
tf$matmul(inputs, self$w) + self$b
}
)
```
And this would be the equivalent PyTorch-specific layer:
``` r
torch <- reticulate::import("torch")
Linear <- new_layer_class(
...
call = function(inputs) {
torch$matmul(inputs, self$w) + self$b
}
)
```
Because cross-backend compatibility is a tremendously useful property, we strongly
recommend that you seek to always make your layers backend-agnostic by leveraging
only Keras APIs.
## The `add_loss()` method
When writing the `call()` method of a layer, you can create loss tensors that
you will want to use later, when writing your training loop. This is doable by
calling `self$add_loss(value)`:
``` r
# A layer that creates an activity regularization loss
layer_activity_regularization <- Layer(
"ActivityRegularizationLayer",
initialize = function(rate = 1e-2) {
self$rate <- as.numeric(rate)
super$initialize()
},
call = function(inputs) {
self$add_loss(self$rate * op_mean(inputs))
inputs
}
)
```
These losses (including those created by any inner layer) can be retrieved via
`layer$losses`. This property is reset at the start of every `call` to
the top-level layer, so that `layer$losses` always contains the loss values
created during the last forward pass.
``` r
layer_outer <- Layer(
"OuterLayer",
initialize = function() {
super$initialize()
self$activity_reg <- layer_activity_regularization(rate = 1e-2)
},
call = function(inputs) {
self$activity_reg(inputs)
inputs
}
)
layer <- layer_outer()
# No losses yet since the layer has never been called
cat("losses:", length(layer$losses), "\n")
```
```
## losses: 0
```
``` r
x <- layer(op_zeros(c(1, 1)))
# We created one loss value
cat("losses:", length(layer$losses), "\n")
```
```
## losses: 1
```
``` r
# `layer$losses` gets reset at the start of each call
x <- layer(op_zeros(c(1, 1)))
# This is the loss created during the call above
cat("losses:", length(layer$losses), "\n")
```
```
## losses: 1
```
In addition, the `loss` property also contains regularization losses created
for the weights of any inner layer:
``` r
layer_outer_with_kernel_regularizer <- Layer(
"OuterLayerWithKernelRegularizer",
initialize = function() {
super$initialize()
self$dense <- layer_dense(units = 32,
kernel_regularizer = regularizer_l2(1e-3))
},
call = function(inputs) {
self$dense(inputs)
}
)
layer <- layer_outer_with_kernel_regularizer()
x <- layer(op_zeros(c(1, 1)))
# This is `1e-3 * sum(layer$dense$kernel ** 2)`,
# created by the `kernel_regularizer` above.
print(layer$losses)
```
```
## [[1]]
## tf.Tensor(0.002025157, shape=(), dtype=float32)
```
These losses are meant to be taken into account when writing custom training loops.
They also work seamlessly with `fit()` (they get automatically summed and added to the main loss, if any):
``` r
inputs <- keras_input(shape = 3)
outputs <- inputs |> layer_activity_regularization()
model <- keras_model(inputs, outputs)
# If there is a loss passed in `compile`, the regularization
# losses get added to it
model |> compile(optimizer = "adam", loss = "mse")
model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1)
```
```
## 1/1 - 0s - 142ms/step - loss: 1.9081
```
``` r
# It's also possible not to pass any loss in `compile`,
# since the model already has a loss to minimize, via the `add_loss`
# call during the forward pass!
model |> compile(optimizer = "adam")
model |> fit(random_normal(c(2, 3)), random_normal(c(2, 3)), epochs = 1)
```
```
## 1/1 - 0s - 115ms/step - loss: 1.6613
```
## You can optionally enable serialization on your layers
If you need your custom layers to be serializable as part of a
[Functional model](functional_api.html), you can optionally implement a `get_config()`
method:
``` r
layer_linear <- Layer(
"Linear",
initialize = function(units = 32) {
self$units <- as.integer(units)
super$initialize()
},
build = function(input_shape) {
self$w <- self$add_weight(
shape = shape(tail(input_shape, 1), self$units),
initializer = "random_normal",
trainable = TRUE
)
self$b <- self$add_weight(
shape = shape(self$units),
initializer = "zeros",
trainable = TRUE
)
},
call = function(inputs) {
op_matmul(inputs, self$w) + self$b
},
get_config = function() {
list(units = self$units)
}
)
# Now you can recreate the layer from its config:
layer <- layer_linear(units = 64)
config <- get_config(layer)
str(config)
```
```
## List of 1
## $ units: int 64
## - attr(*, "__class__")=<class '<r-globalenv>.Linear'>
```
``` r
new_layer <- from_config(config)
```
Note that the `initialize()` method of the base `Layer` class takes some keyword
arguments, in particular a `name` and a `dtype`. It's good practice to pass
these arguments to the parent class in `initialize()` and to include them in the
layer config:
``` r
Linear <- new_layer_class(
"Linear",
initialize = function(units = 32, ...) {
self$units <- as.integer(units)
super$initialize(...)
},
build = function(input_shape) {
self$w <- self$add_weight(
shape = shape(tail(input_shape, 1), self$units),
initializer = "random_normal",
trainable = TRUE
)
self$b <- self$add_weight(
shape = shape(self$units),
initializer = "zeros",
trainable = TRUE
)
},
call = function(inputs) {
op_matmul(inputs, self$w) + self$b
},
get_config = function() {
list(units = self$units)
}
)
layer <- Linear(units = 64)
config <- get_config(layer)
str(config)
```
```
## List of 1
## $ units: int 64
## - attr(*, "__class__")=<class '<r-globalenv>.Linear'>
```
``` r
new_layer <- from_config(config)
```
If you need more flexibility when deserializing the layer from its config, you
can also override the `from_config()` class method. This is the base
implementation of `from_config()`:
``` r
Layer(
...,
from_config = function(config) {
# calling `__class__`() creates a new instance and calls initialize()
do.call(`__class__`, config)
}
)
```
To learn more about serialization and saving, see the complete
[guide to saving and serializing models](serialization_and_saving.html).
## Privileged `training` argument in the `call()` method
Some layers, in particular the `BatchNormalization` layer and the `Dropout`
layer, have different behaviors during training and inference. For such
layers, it is standard practice to expose a `training` (boolean) argument in
the `call()` method.
By exposing this argument in `call()`, you enable the built-in training and
evaluation loops (e.g. `fit()`) to correctly use the layer in training and
inference.
``` r
layer_custom_dropout <- Layer(
"CustomDropout",
initialize = function(rate, ...) {
super$initialize(...)
self$rate <- rate
self$seed_generator <- random_seed_generator(1337)
},
call = function(inputs, training = NULL) {
if (isTRUE(training))
return(random_dropout(inputs, rate = self$rate,
seed = self.seed_generator))
inputs
}
)
```
## Privileged `mask` argument in the `call()` method
The other privileged argument supported by `call()` is the `mask` argument.
You will find it in all Keras RNN layers. A mask is a boolean tensor (one
boolean value per timestep in the input) used to skip certain input timesteps
when processing timeseries data.
Keras will automatically pass the correct `mask` argument to `call()` for
layers that support it, when a mask is generated by a prior layer.
Mask-generating layers are the `Embedding`
layer configured with `mask_zero = TRUE`, and the `Masking` layer.
## The `Model` class
In general, you will use the `Layer` class to define inner computation blocks,
and will use the `Model` class to define the outer model -- the object you
will train.
For instance, in a ResNet50 model, you would have several ResNet blocks
subclassing `Layer`, and a single `Model` encompassing the entire ResNet50
network.
The `Model` class has the same API as `Layer`, with the following differences:
- It exposes built-in training, evaluation, and prediction loops
(`fit()`, `evaluate()`, `predict()`).
- It exposes the list of its inner layers, via the `model$layers` property.
- It exposes saving and serialization APIs (`save()`, `save_weights()`...)
Effectively, the `Layer` class corresponds to what we refer to in the
literature as a "layer" (as in "convolution layer" or "recurrent layer") or as
a "block" (as in "ResNet block" or "Inception block").
Meanwhile, the `Model` class corresponds to what is referred to in the
literature as a "model" (as in "deep learning model") or as a "network" (as in
"deep neural network").
So if you're wondering, "should I use the `Layer` class or the `Model` class?",
ask yourself: will I need to call `fit()` on it? Will I need to call `save()`
on it? If so, go with `Model`. If not (either because your class is just a block
in a bigger system, or because you are writing training & saving code yourself),
use `Layer`.
For instance, we could take our mini-resnet example above, and use it to build
a `Model` that we could train with `fit()`, and that we could save with
`save_weights()`:
``` r
ResNet <- Model(
"ResNet",
initialize = function(num_classes = 1000, ...) {
super$initialize(...)
self$block_1 <- layer_resnet_block()
self$block_2 <- layer_resnet_block()
self$global_pool <- layer_global_average_pooling_2d()
self$classifier <- layer_dense(num_classes)
},
call = function(inputs) {
inputs |>
self$block_1() |>
self$block_2() |>
self$global_pool() |>
self$classifier()
}
)
resnet <- ResNet()
dataset <- ...
resnet |> fit(dataset, epochs=10)
resnet |> save_model("filepath.keras")
```
## Putting it all together: an end-to-end example
Here's what you've learned so far:
- A `Layer` encapsulate a state (created in `initialize()` or `build()`) and some
computation (defined in `call()`).
- Layers can be recursively nested to create new, bigger computation blocks.
- Layers are backend-agnostic as long as they only use Keras APIs. You can use
backend-native APIs (such as `jax$numpy`, `torch$nn` or `tf$nn`), but then
your layer will only be usable with that specific backend.
- Layers can create and track losses (typically regularization losses)
via `add_loss()`.
- The outer container, the thing you want to train, is a `Model`. A `Model` is
just like a `Layer`, but with added training and serialization utilities.
Let's put all of these things together into an end-to-end example: we're going
to implement a Variational AutoEncoder (VAE) in a backend-agnostic fashion
-- so that it runs the same with TensorFlow, JAX, and PyTorch.
We'll train it on MNIST digits.
Our VAE will be a subclass of `Model`, built as a nested composition of layers
that subclass `Layer`. It will feature a regularization loss (KL divergence).
``` r
layer_sampling <- Layer(
"Sampling",
initialize = function(...) {
super$initialize(...)
self$seed_generator <- random_seed_generator(1337)
},
call = function(inputs) {
c(z_mean, z_log_var) %<-% inputs
batch <- op_shape(z_mean)[[1]]
dim <- op_shape(z_mean)[[2]]
epsilon <- random_normal(shape = c(batch, dim),
seed=self$seed_generator)
z_mean + op_exp(0.5 * z_log_var) * epsilon
}
)
# Maps MNIST digits to a triplet (z_mean, z_log_var, z).
layer_encoder <- Layer(
"Encoder",
initialize = function(latent_dim = 32, intermediate_dim = 64, ...) {
super$initialize(...)
self$dense_proj <-
layer_dense(units = intermediate_dim, activation = "relu")
self$dense_mean <- layer_dense(units = latent_dim)
self$dense_log_var <- layer_dense(units = latent_dim)
self$sampling <- layer_sampling()
},
call = function(inputs) {
x <- self$dense_proj(inputs)
z_mean <- self$dense_mean(x)
z_log_var <- self$dense_log_var(x)
z <- self$sampling(list(z_mean, z_log_var))
list(z_mean, z_log_var, z)
}
)
# Converts z, the encoded digit vector, back into a readable digit.
layer_decoder <- Layer(
"Decoder",
initialize = function(original_dim, intermediate_dim = 64, ...) {
super$initialize(...)
self$dense_proj <-
layer_dense(units = intermediate_dim, activation = "relu")
self$dense_output <-
layer_dense(units = original_dim, activation = "sigmoid")
},
call = function(inputs) {
x <- self$dense_proj(inputs)
self$dense_output(x)
}
)
# Combines the encoder and decoder into an end-to-end model for training.
VariationalAutoEncoder <- Model(
"VariationalAutoEncoder",
initialize = function(original_dim, intermediate_dim = 64, latent_dim = 32,
name = "autoencoder", ...) {
super$initialize(name = name, ...)
self$original_dim <- original_dim
self$encoder <- layer_encoder(latent_dim = latent_dim,
intermediate_dim = intermediate_dim)
self$decoder <- layer_decoder(original_dim = original_dim,
intermediate_dim = intermediate_dim)
},
call = function(inputs) {
c(z_mean, z_log_var, z) %<-% self$encoder(inputs)
reconstructed <- self$decoder(z)
# Add KL divergence regularization loss.
kl_loss <- -0.5 * op_mean(z_log_var - op_square(z_mean) - op_exp(z_log_var) + 1)
self$add_loss(kl_loss)
reconstructed
}
)
```
Let's train it on MNIST using the `fit()` API:
``` r
c(c(x_train, .), .) %<-% dataset_mnist()
x_train <- x_train |>
op_reshape(c(60000, 784)) |>
op_cast("float32") |>
op_divide(255)
original_dim <- 784
vae <- VariationalAutoEncoder(
original_dim = 784,
intermediate_dim = 64,
latent_dim = 32
)
optimizer <- optimizer_adam(learning_rate = 1e-3)
vae |> compile(optimizer, loss = loss_mean_squared_error())
vae |> fit(x_train, x_train, epochs = 2, batch_size = 64)
```
```
## Epoch 1/2
## 938/938 - 5s - 5ms/step - loss: 0.0748
## Epoch 2/2
## 938/938 - 1s - 2ms/step - loss: 0.0676
```