Update training.R

R/training.R

@@ -33,10 +33,12 @@ train_model <- function(model,  epochs, device, train_dl, valid_dl=NULL, verbose
   best_train_loss = Inf
   best_val_loss = Inf
   counter = 0
-
+  model$losses$auc <- NA
   for (epoch in min(which(is.na(model$losses$train_l))):(epochs+ min(which(is.na(model$losses$train_l))) - 1)) {
     train_l <- c()
     model$training_properties$epoch <- epoch
+    preds <- c()
+    ys <- c()
 
     ### Batch evaluation ###
     coro::loop(for (b in train_dl) {
@@ -70,7 +72,10 @@ train_model <- function(model,  epochs, device, train_dl, valid_dl=NULL, verbose
       optimizer$step()
 
       train_l <- c(train_l, loss$item())
+      preds <- c(preds, as.numeric(output$to(device = "cpu")))
+      ys <- c(ys, as.numeric(b[[2]]))
     })
+    model$losses$auc[epoch] <- Metrics::auc(ys, preds)
 
     if(is.na(loss$item())) {
       if(verbose) cat("Loss is NA. Bad training, please hyperparameters. See vignette('B-Training_neural_networks') for help.\n")
@@ -124,7 +129,8 @@ train_model <- function(model,  epochs, device, train_dl, valid_dl=NULL, verbose
       if(verbose) cat(sprintf("Loss at epoch %d: training: %3.3f, validation: %3.3f, lr: %3.5f\n",
                               epoch, model$losses$train_l[epoch], model$losses$valid_l[epoch],optimizer$param_groups[[1]]$lr))
     }else{
-      if (verbose) cat(sprintf("Loss at epoch %d: %3f, lr: %3.5f\n", epoch, model$losses$train_l[epoch],optimizer$param_groups[[1]]$lr))
+      if (verbose)       cat(sprintf("Loss at epoch %d: training: %3.3f, auc: %3.3f, lr: %3.5f\n", 
+                  epoch, model$losses$train_l[epoch], model$losses$auc[epoch], optimizer$param_groups[[1]]$lr))
     }
 
     ### create plot ###