loss_categorical_crossentropy: Computes the crossentropy loss between the labels and...

In rstudio/keras: R Interface to 'Keras'

Computes the crossentropy loss between the labels and predictions.

Description

Use this crossentropy loss function when there are two or more label classes. We expect labels to be provided in a one_hot representation. If you want to provide labels as integers, please use SparseCategoricalCrossentropy loss. There should be num_classes floating point values per feature, i.e., the shape of both y_pred and y_true are ⁠[batch_size, num_classes]⁠.

Usage

loss_categorical_crossentropy(
  y_true,
  y_pred,
  from_logits = FALSE,
  label_smoothing = 0,
  axis = -1L,
  ...,
  reduction = "sum_over_batch_size",
  name = "categorical_crossentropy"
)

Arguments

y_true	Tensor of one-hot true targets.
y_pred	Tensor of predicted targets.
from_logits	Whether y_pred is expected to be a logits tensor. By default, we assume that y_pred encodes a probability distribution.
label_smoothing	Float in ⁠[0, 1].⁠ When > 0, label values are smoothed, meaning the confidence on label values are relaxed. For example, if 0.1, use 0.1 / num_classes for non-target labels and 0.9 + 0.1 / num_classes for target labels.
axis	The axis along which to compute crossentropy (the features axis). Defaults to -1.
...	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

Categorical crossentropy loss value.

Examples

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

## tf.Tensor([0.05129329 2.30258509], shape=(2), dtype=float64)

Standalone usage:

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

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

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

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

# Using 'sum' reduction type.
cce <- loss_categorical_crossentropy(reduction = "sum")
cce(y_true, y_pred)

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

# Using 'none' reduction type.
cce <- loss_categorical_crossentropy(reduction = NULL)
cce(y_true, y_pred)

## tf.Tensor([0.05129331 2.3025851 ], shape=(2), dtype=float32)

Usage with the compile() API:

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

See Also

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

Other losses:
Loss()
loss_binary_crossentropy()
loss_binary_focal_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_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 April 27, 2024, 10:11 p.m.