Computes the crossentropy loss between the labels and predictions.

Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using one-hot representation, please use CategoricalCrossentropy loss. There should be # classes floating point values per feature for y_pred and a single floating point value per feature for y_true.

In the snippet below, there is a single floating point value per example for y_true and num_classes floating pointing values per example for y_pred. The shape of y_true is [batch_size] and the shape of y_pred is [batch_size, num_classes].

Usage

loss_sparse_categorical_crossentropy(
  y_true,
  y_pred,
  from_logits = FALSE,
  ignore_class = NULL,
  axis = -1L,
  ...,
  reduction = "sum_over_batch_size",
  name = "sparse_categorical_crossentropy"
)

Arguments

y_true

Ground truth values.

y_pred

The predicted values.

from_logits

Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.

ignore_class

Optional integer. The ID of a class to be ignored during loss computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (ignore_class=NULL), all classes are considered.

axis

Defaults to -1. The dimension along which the entropy is computed.

...

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

Optional name for the loss instance.

Value

Sparse categorical crossentropy loss value.

Examples

y_true <- c(1, 2)
y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))
loss <- loss_sparse_categorical_crossentropy(y_true, y_pred)
loss

## tf.Tensor([0.05129339 2.30258509], shape=(2), dtype=float64)

y_true <- c(1, 2)
y_pred <- rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))
# Using 'auto'/'sum_over_batch_size' reduction type.
scce <- loss_sparse_categorical_crossentropy()
scce(op_array(y_true), op_array(y_pred))

## tf.Tensor(1.1769392, shape=(), dtype=float32)

# 1.177

# Calling with 'sample_weight'.
scce(op_array(y_true), op_array(y_pred), sample_weight = op_array(c(0.3, 0.7)))

## tf.Tensor(0.8135988, shape=(), dtype=float32)

# Using 'sum' reduction type.
scce <- loss_sparse_categorical_crossentropy(reduction="sum")
scce(op_array(y_true), op_array(y_pred))

## tf.Tensor(2.3538785, shape=(), dtype=float32)

# 2.354

# Using 'none' reduction type.
scce <- loss_sparse_categorical_crossentropy(reduction=NULL)
scce(op_array(y_true), op_array(y_pred))

## tf.Tensor([0.05129344 2.3025851 ], shape=(2), dtype=float32)

# array([0.0513, 2.303], dtype=float32)

Usage with the compile() API:

model %>% compile(optimizer = 'sgd',
                  loss = loss_sparse_categorical_crossentropy())

See also

• https://keras.io/api/losses/probabilistic_losses#sparsecategoricalcrossentropy-class

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_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_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()