auc_wrapper: Mean AUC score

In GenomeNet/deepG: Deep Learning for Genome Sequence Data

Mean AUC score

Description

Compute AUC score as additional metric. If model has several output neurons with binary crossentropy loss, will use the average score.

Usage

auc_wrapper(model_output_size, loss = "binary_crossentropy")

Arguments

model_output_size	Number of neurons in model output layer.
loss	Loss function of model, for which metric will be applied to; must be "binary_crossentropy" or "categorical_crossentropy".

Value

A keras metric.

Examples

y_true <- c(1,0,0,1,1,0,1,0,0) %>% matrix(ncol = 3)
y_pred <- c(0.9,0.05,0.05,0.9,0.05,0.05,0.9,0.05,0.05) %>% matrix(ncol = 3)


library(keras)
auc_metric <- auc_wrapper(3L, "binary_crossentropy")

auc_metric$update_state(y_true, y_pred)
auc_metric$result()  

# add metric to a model
num_targets <- 4
model <- create_model_lstm_cnn(maxlen = 20,
                               layer_lstm = 8,
                               bal_acc = FALSE,
                               last_layer_activation = "sigmoid",
                               loss_fn = "binary_crossentropy",
                               layer_dense = c(8, num_targets))

auc_metric <- auc_wrapper(num_targets, loss = model$loss)
model %>% keras::compile(loss = model$loss, 
                         optimizer = model$optimizer,
                         metrics = c(model$metrics, auc_metric))

GenomeNet/deepG documentation built on Dec. 24, 2024, 12:11 p.m.