report val loss, use log loss monitoring plot scale

mlair/plotting/training_monitoring.py

@@ -86,7 +86,7 @@ class PlotModelHistory:
         """
         ax = self._data[[self._plot_metric, f"val_{self._plot_metric}"]].plot(linewidth=0.7)
         if len(self._additional_columns) > 0:
-            self._data[self._additional_columns].plot(linewidth=0.7, secondary_y=True, ax=ax)
+            self._data[self._additional_columns].plot(linewidth=0.7, secondary_y=True, ax=ax, logy=True)
         title = f"Model {self._plot_metric}: best = {self._data[[f'val_{self._plot_metric}']].min().values}"
         ax.set(xlabel="epoch", ylabel=self._plot_metric, title=title)
         ax.axhline(y=0, color="gray", linewidth=0.5)

mlair/run_modules/training.py

@@ -247,3 +247,7 @@ class Training(RunEnvironment):
         df.to_latex(os.path.join(path, "training_settings.tex"), na_rep='---', column_format=column_format)
         df.to_markdown(open(os.path.join(path, "training_settings.md"), mode="w", encoding='utf-8'),
                        tablefmt="github")
+
+        val_score = self.model.evaluate_generator(generator=self.val_set, use_multiprocessing=True, verbose=0, steps=1)
+        for index, item in enumerate(val_score):
+            logging.info(f"{self.model.metrics_names[index]}, {item}")