model.evaluate_generator changed, because it is deprecated and works with...

model.evaluate_generator changed, because it is deprecated and works with model.evaluate(generator, ...), updated mlt_modules_juwels.sh to Stages/2020 modules with Python/3.8.5

HPC_setup/mlt_modules_juwels.sh

@@ -8,14 +8,14 @@
 module --force purge
 module use $OTHERSTAGES
 
-ml Stages/2019a
-ml GCCcore/.8.3.0
+ml Stages/2020
+ml GCCcore/.9.3.0
 
-ml Jupyter/2019a-Python-3.6.8
-ml Python/3.6.8
-ml TensorFlow/1.13.1-GPU-Python-3.6.8
-ml Keras/2.2.4-GPU-Python-3.6.8
-ml SciPy-Stack/2019a-Python-3.6.8
-ml dask/1.1.5-Python-3.6.8
-ml GEOS/3.7.1-Python-3.6.8
-ml Graphviz/2.40.1
+ml Jupyter/2020.3.0-Python-3.8.5
+ml Python/3.8.5
+# ml TensorFlow/1.13.1-GPU-Python-3.6.8
+ml TensorFlow/2.3.1-Python-3.8.5
+ml SciPy-Stack/2020-Python-3.8.5
+ml dask/2.22.0-Python-3.8.5
+ml GEOS/3.8.1-Python-3.8.5
+ml Graphviz/2.44.1

mlair/run_modules/training.py

@@ -123,7 +123,7 @@ class Training(RunEnvironment):
 
     def train(self) -> None:
         """
-        Perform training using keras fit_generator().
+        Perform training using keras fit().
 
         Callbacks are stored locally in the experiment directory. Best model from training is saved for class
         variable model. If the file path of checkpoint is not empty, this method assumes, that this is not a new
@@ -261,7 +261,7 @@ class Training(RunEnvironment):
         tables.save_to_md(path, "training_settings.md", df=df)
 
         # calculate val scores
-        val_score = self.model.evaluate_generator(generator=self.val_set, use_multiprocessing=True, verbose=0)
+        val_score = self.model.evaluate(self.val_set, use_multiprocessing=True, verbose=0)
         path = self.data_store.get("model_path")
         with open(os.path.join(path, "val_scores.txt"), "a") as f:
             for index, item in enumerate(to_list(val_score)):