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].
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",
dtype = NULL
)Sparse categorical crossentropy loss value.
Ground truth values.
The predicted values.
Whether y_pred is expected to be a logits tensor. By
default, we assume that y_pred encodes a probability distribution.
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.
Defaults to -1. The dimension along which the entropy is
computed.
For forward/backward compatability.
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".
Optional name for the loss instance.
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.
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())
Other losses:
Loss()
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_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()