metric_categorical_crossentropy {keras3} | R Documentation |
Computes the crossentropy metric between the labels and predictions.
Description
This is the crossentropy metric class to be used when there are multiple
label classes (2 or more). It assumes that labels are one-hot encoded,
e.g., when labels values are c(2, 0, 1)
, then
y_true
is rbind(c([0, 0, 1), c(1, 0, 0), c(0, 1, 0))
.
Usage
metric_categorical_crossentropy(
y_true,
y_pred,
from_logits = FALSE,
label_smoothing = 0,
axis = -1L,
...,
name = "categorical_crossentropy",
dtype = NULL
)
Arguments
y_true |
Tensor of one-hot true targets. |
y_pred |
Tensor of predicted targets. |
from_logits |
(Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution. |
label_smoothing |
(Optional) Float in |
axis |
(Optional) Defaults to |
... |
For forward/backward compatability. |
name |
(Optional) string name of the metric instance. |
dtype |
(Optional) data type of the metric result. |
Value
If y_true
and y_pred
are missing, a Metric
instance is returned. The Metric
instance that can be passed directly to
compile(metrics = )
, or used as a standalone object. See ?Metric
for
example usage. If y_true
and y_pred
are provided, then a tensor with
the computed value is returned.
Examples
Standalone usage:
# EPSILON = 1e-7, y = y_true, y` = y_pred # y` = clip_op_clip_by_value(output, EPSILON, 1. - EPSILON) # y` = rbind(c(0.05, 0.95, EPSILON), c(0.1, 0.8, 0.1)) # xent = -sum(y * log(y'), axis = -1) # = -((log 0.95), (log 0.1)) # = [0.051, 2.302] # Reduced xent = (0.051 + 2.302) / 2 m <- metric_categorical_crossentropy() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)), rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1))) m$result()
## tf.Tensor(1.1769392, shape=(), dtype=float32)
# 1.1769392
m$reset_state() m$update_state(rbind(c(0, 1, 0), c(0, 0, 1)), rbind(c(0.05, 0.95, 0), c(0.1, 0.8, 0.1)), sample_weight = c(0.3, 0.7)) m$result()
## tf.Tensor(1.6271976, shape=(), dtype=float32)
Usage with compile()
API:
model %>% compile( optimizer = 'sgd', loss = 'mse', metrics = list(metric_categorical_crossentropy()))
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_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_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()
Other metrics:
Metric()
custom_metric()
metric_auc()
metric_binary_accuracy()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_binary_iou()
metric_categorical_accuracy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_cosine_similarity()
metric_f1_score()
metric_false_negatives()
metric_false_positives()
metric_fbeta_score()
metric_hinge()
metric_huber()
metric_iou()
metric_kl_divergence()
metric_log_cosh()
metric_log_cosh_error()
metric_mean()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_iou()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_mean_wrapper()
metric_one_hot_iou()
metric_one_hot_mean_iou()
metric_poisson()
metric_precision()
metric_precision_at_recall()
metric_r2_score()
metric_recall()
metric_recall_at_precision()
metric_root_mean_squared_error()
metric_sensitivity_at_specificity()
metric_sparse_categorical_accuracy()
metric_sparse_categorical_crossentropy()
metric_sparse_top_k_categorical_accuracy()
metric_specificity_at_sensitivity()
metric_squared_hinge()
metric_sum()
metric_top_k_categorical_accuracy()
metric_true_negatives()
metric_true_positives()
Other probabilistic metrics:
metric_binary_crossentropy()
metric_kl_divergence()
metric_poisson()
metric_sparse_categorical_crossentropy()