Loss: Subclass the base `Loss` class

Description

Use this to define a custom loss class. Note, in most cases you do not need to subclass Loss to define a custom loss: you can also pass a bare R function, or a named R function defined with custom_metric(), as a loss function to compile().

Usage

Loss(
  classname,
  call = NULL,
  ...,
  public = list(),
  private = list(),
  inherit = NULL,
  parent_env = parent.frame()
)

Value

A function that returns Loss instances, similar to the builtin loss functions.

Arguments

classname

String, the name of the custom class. (Conventionally, CamelCase).

call

function(y_true, y_pred)

Method to be implemented by subclasses: Function that contains the logic for loss calculation using y_true, y_pred.

..., public

Additional methods or public members of the custom class.

private

Named list of R objects (typically, functions) to include in instance private environments. private methods will have all the same symbols in scope as public methods (See section "Symbols in Scope"). Each instance will have it's own private environment. Any objects in private will be invisible from the Keras framework and the Python runtime.

inherit

What the custom class will subclass. By default, the base keras class.

parent_env

The R environment that all class methods will have as a grandparent.

Methods defined by base <code>Loss</code> class:

```
initialize(name=NULL, reduction="sum_over_batch_size", dtype=NULL)
```
Args:
- name: Optional name for the loss instance.
- reduction: Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size", "mean", "mean_with_sample_weight" or NULL. "sum" sums the loss, "sum_over_batch_size" and "mean" sum the loss and divide by the sample size, and "mean_with_sample_weight" sums the loss and divides by the sum of the sample weights. "none" and NULL perform no aggregation. Defaults to "sum_over_batch_size".
- dtype: The dtype of the loss's computations. Defaults to NULL, which means using config_floatx(). config_floatx() is a "float32" unless set to different value (via config_set_floatx()). If a keras$DTypePolicy is provided, then the compute_dtype will be utilized.
```
__call__(y_true, y_pred, sample_weight=NULL)
```
Call the loss instance as a function, optionally with sample_weight.
```
get_config()
```

Readonly properties:

```
dtype
```

Symbols in scope

All R function custom methods (public and private) will have the following symbols in scope:

self: The custom class instance.
super: The custom class superclass.
private: An R environment specific to the class instance. Any objects assigned here are invisible to the Keras framework.
__class__ and as.symbol(classname): the custom class type object.

Details

Example subclass implementation:

loss_custom_mse <- Loss(
  classname = "CustomMeanSquaredError",
  call = function(y_true, y_pred) {
    op_mean(op_square(y_pred - y_true), axis = -1)
  }
)
# Usage in compile()
model <- keras_model_sequential(input_shape = 10) |> layer_dense(10)
model |> compile(loss = loss_custom_mse())
# Standalone usage
mse <- loss_custom_mse(name = "my_custom_mse_instance")
y_true <- op_arange(20) |> op_reshape(c(4, 5))
y_pred <- op_arange(20) |> op_reshape(c(4, 5)) * 2
(loss <- mse(y_true, y_pred))

## tf.Tensor(123.5, shape=(), dtype=float32)

loss2 <- (y_pred - y_true)^2 |>
  op_mean(axis = -1) |>
  op_mean()
stopifnot(all.equal(as.array(loss), as.array(loss2)))
sample_weight <-array(c(.25, .25, 1, 1))
(weighted_loss <- mse(y_true, y_pred, sample_weight = sample_weight))

## tf.Tensor(112.8125, shape=(), dtype=float32)

weighted_loss2 <- (y_true - y_pred)^2 |>
  op_mean(axis = -1) |>
  op_multiply(sample_weight) |>
  op_mean()
stopifnot(all.equal(as.array(weighted_loss),
                    as.array(weighted_loss2)))

Other losses:
loss_binary_crossentropy()
loss_binary_focal_crossentropy()
loss_categorical_crossentropy()
loss_categorical_focal_crossentropy()
loss_categorical_hinge()
loss_circle()
loss_cosine_similarity()
loss_ctc()
loss_dice()
loss_hinge()
loss_huber()
loss_kl_divergence()
loss_log_cosh()
loss_mean_absolute_error()
loss_mean_absolute_percentage_error()
loss_mean_squared_error()
loss_mean_squared_logarithmic_error()
loss_poisson()
loss_sparse_categorical_crossentropy()
loss_squared_hinge()
loss_tversky()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_hinge()
metric_huber()
metric_kl_divergence()
metric_log_cosh()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_poisson()
metric_sparse_categorical_crossentropy()
metric_squared_hinge()