loss_dice: Computes the Dice loss value between 'y_true' and 'y_pred'.

In rstudio/keras: R Interface to 'Keras'

Computes the Dice loss value between y_true and y_pred.

Description

Formula:

loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))

Formula:

loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))

Usage

loss_dice(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "dice",
  axis = NULL,
  dtype = NULL
)

Arguments

y_true	Tensor of true targets.
y_pred	Tensor of predicted targets.
...	For forward/backward compatability.
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" or NULL.
name	String, name for the object
axis	List of which dimensions the loss is calculated. Defaults to NULL.
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()).

Value

if y_true and y_pred are provided, Dice loss value. Otherwise, a Loss() instance.

Example

y_true <- array(c(1, 1, 0, 0,
                  1, 1, 0, 0), dim = c(2, 2, 2, 1))
y_pred <- array(c(0, 0.4, 0,   0,
                  1,   0, 1, 0.9), dim = c(2, 2, 2, 1))

axis <- c(2, 3, 4)
loss <- loss_dice(y_true, y_pred, axis = axis)
stopifnot(shape(loss) == shape(2))
loss

## tf.Tensor([0.50000001 0.75757576], shape=(2), dtype=float64)

loss = loss_dice(y_true, y_pred)
stopifnot(shape(loss) == shape())
loss

## tf.Tensor(0.6164383614186526, shape=(), dtype=float64)

See Also

Other losses:
Loss()
loss_binary_crossentropy()
loss_binary_focal_crossentropy()
loss_categorical_crossentropy()
loss_categorical_focal_crossentropy()
loss_categorical_hinge()
loss_cosine_similarity()
loss_ctc()
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()

rstudio/keras documentation built on July 8, 2024, 3:07 p.m.