test varying train vs larger test

src/main.py

@@ -40,13 +40,14 @@ def main():
     dataset = tf.data.Dataset.sample_from_datasets(datasets, seed=42) # <-- shuffle
 
     # Defining dataset size as a function of the number of knots
-    ninputs = len(knots) * len_db
+    ninputs = len(knots) * 100000 # <-- OG dataset size
+    ninputs_test = len(knots) * len_db # <-- for training 
 
     dataset = dataset.take(int(ninputs)) # <-- takes after shuffle // 
 
     # Splitting in train test and validation according to chosen sizes
     train_dataset, test_dataset, val_dataset = split_train_test_validation(
-        dataset, int(ninputs * (0.9)), int(ninputs * (0.075)), int(ninputs * (0.025))
+        dataset, int(ninputs_test), int(ninputs * (0.075)), int(ninputs_test * (0.025))
     )
 
     # Starting training or testing according to user preferences