refactored train variable names to validation

run.py

@@ -52,7 +52,6 @@ def load_data(data_parameters):
     """Dataset Loader
 
     This function loads the training and validation datasets.
-    TODO: Will need to define if all the training data is loaded as bulk or individually!
 
     Args:
         data_parameters (dict): Dictionary containing relevant information for the datafiles.
@@ -86,7 +85,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
         training_parameters(dict): Dictionary containing relevant hyperparameters for training the network.
         training_parameters = {
             'training_batch_size': 5
-            'test_batch_size: 5
+            'validation_batch_size: 5
             'use_pre_trained': False
             'pre_trained_path': 'pre_trained/path'
             'experiment_name': 'experiment_name'
@@ -114,7 +113,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
         }
     """
 
-    train_data, test_data = load_data(data_parameters)
+    train_data, validation_data = load_data(data_parameters)
 
     train_loader = data.DataLoader(
         dataset=train_data,
@@ -124,9 +123,9 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
         pin_memory=True
     )
 
-    test_loader = data.DataLoader(
-        dataset=test_data,
-        batch_size=training_parameters['test_batch_size'],
+    validation_loader = data.DataLoader(
+        dataset=validation_data,
+        batch_size=training_parameters['validation_batch_size'],
         shuffle=False,
         num_workers=4,
         pin_memory=True
@@ -157,7 +156,7 @@ def train(data_parameters, training_parameters, network_parameters, misc_paramet
                     logs_directory=misc_parameters['logs_directory']
                     )
 
-    solver.train(train_loader, test_loader)
+    solver.train(train_loader, validation_loader)
 
     model_output_path = os.path.join(
         misc_parameters['save_model_directory'], training_parameters['final_model_output_file'])

settings.ini

@@ -13,7 +13,7 @@ validation_target_file = "fMRI/rfMRI_25.dr/dr_stage2.nii.gz"
 
 [TRAINING]
 training_batch_size = 5
-test_batch_size = 5
+validation_batch_size = 5
 use_pre_trained = False
 pre_trained_path = "saved_models/preTrained.pth.tar"
 experiment_name = "experiment_name"