batch size is now part of the exp setup and not related to the model class anymore

src/model_modules/model_class.py

@@ -119,7 +119,6 @@ from typing import Any, Callable, Dict
 
 import keras
 import tensorflow as tf
-import logging
 from src.model_modules.inception_model import InceptionModelBase
 from src.model_modules.flatten import flatten_tail
 from src.model_modules.advanced_paddings import PadUtils, Padding2D
@@ -356,7 +355,6 @@ class MyLittleModel(AbstractModelClass):
         self.dropout_rate = 0.1
         self.regularizer = keras.regularizers.l2(0.1)
         self.epochs = 20
-        self.batch_size = int(256)
         self.activation = keras.layers.PReLU
 
         # apply to model
@@ -430,7 +428,6 @@ class MyBranchedModel(AbstractModelClass):
         self.dropout_rate = 0.1
         self.regularizer = keras.regularizers.l2(0.1)
         self.epochs = 20
-        self.batch_size = int(256)
         self.activation = keras.layers.PReLU
 
         # apply to model
@@ -505,7 +502,6 @@ class MyTowerModel(AbstractModelClass):
         self.lr_decay = src.model_modules.keras_extensions.LearningRateDecay(base_lr=self.initial_lr, drop=.94,
                                                                              epochs_drop=10)
         self.epochs = 20
-        self.batch_size = int(256 * 4)
         self.activation = keras.layers.PReLU
 
         # apply to model
@@ -619,7 +615,6 @@ class MyPaperModel(AbstractModelClass):
         self.lr_decay = src.model_modules.keras_extensions.LearningRateDecay(base_lr=self.initial_lr, drop=.94,
                                                                              epochs_drop=10)
         self.epochs = 150
-        self.batch_size = int(256 * 2)
         self.activation = keras.layers.ELU
         self.padding = "SymPad2D"
 

src/run.py

@@ -25,7 +25,8 @@ def run(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW00
         train_min_length=None, val_min_length=None, test_min_length=None,
         evaluate_bootstraps=True, number_of_bootstraps=None, create_new_bootstraps=False,
         plot_list=None,
-        model=None):
+        model=None,
+        batch_size=None):
 
     params = inspect.getfullargspec(ExperimentSetup).args
     kwargs = {k: v for k, v in locals().items() if k in params}

src/run_modules/experiment_setup.py

@@ -233,12 +233,12 @@ class ExperimentSetup(RunEnvironment):
                  create_new_model: bool = None, bootstrap_path=None, permute_data_on_training: bool = None, transformation=None,
                  train_min_length=None, val_min_length=None, test_min_length=None, extreme_values: list = None,
                  extremes_on_right_tail_only: bool = None, evaluate_bootstraps=True, plot_list=None, number_of_bootstraps=None,
-                 create_new_bootstraps=None, data_path: str = None, login_nodes=None, hpc_hosts=None, model=None):
+                 create_new_bootstraps=None, data_path: str = None, login_nodes=None, hpc_hosts=None, model=None, batch_size=None):
 
         # create run framework
         super().__init__()
 
-        # experiment setup
+        # experiment setup, hyperparameters
         self._set_param("data_path", path_config.prepare_host(data_path=data_path, sampling=sampling))
         self._set_param("hostname", path_config.get_host())
         self._set_param("hpc_hosts", hpc_hosts, default=DEFAULT_HPC_HOST_LIST + DEFAULT_HPC_LOGIN_LIST)
@@ -257,6 +257,7 @@ class ExperimentSetup(RunEnvironment):
         upsampling = self.data_store.get("upsampling", "train")
         permute_data = False if permute_data_on_training is None else permute_data_on_training
         self._set_param("permute_data", permute_data or upsampling, scope="train")
+        self._set_param("batch_size", batch_size, default=int(256 * 2))
 
         # set experiment name
         exp_date = self._get_parser_args(parser_args).get("experiment_date")

src/run_modules/model_setup.py

@@ -33,6 +33,7 @@ class ModelSetup(RunEnvironment):
         * `generator` [train]
         * `window_lead_time` [.]
         * `window_history_size` [.]
+        * `model_class` [.]
 
     Optional objects
         * `lr_decay` [model]
@@ -43,7 +44,7 @@ class ModelSetup(RunEnvironment):
         * `hist` [model]
         * `callbacks` [model]
         * `model_name` [model]
-        * all settings from model class like `dropout_rate`, `initial_lr`, `batch_size`, and `optimizer` [model]
+        * all settings from model class like `dropout_rate`, `initial_lr`, and `optimizer` [model]
 
     Creates
         * plot of model architecture `<model_name>.pdf`

src/run_modules/training.py

@@ -33,7 +33,7 @@ class Training(RunEnvironment):
 
     Required objects [scope] from data store:
         * `model` [model]
-        * `batch_size` [model]
+        * `batch_size` [.]
         * `epochs` [model]
         * `callbacks` [model]
         * `model_name` [model]
@@ -67,7 +67,7 @@ class Training(RunEnvironment):
         self.train_set: Union[Distributor, None] = None
         self.val_set: Union[Distributor, None] = None
         self.test_set: Union[Distributor, None] = None
-        self.batch_size = self.data_store.get("batch_size", "model")
+        self.batch_size = self.data_store.get("batch_size")
         self.epochs = self.data_store.get("epochs", "model")
         self.callbacks: CallbackHandler = self.data_store.get("callbacks", "model")
         self.experiment_name = self.data_store.get("experiment_name")